SEARCH

SEARCH BY CITATION

Keywords:

  • Boreal forest;
  • distribution limit;
  • genetic diversity;
  • latitudinal gradient;
  • microsatellite genotyping;
  • northern edge

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Biosketches
  9. Conflict of Interest
  10. References
  11. Supporting Information

We tested the hypothesis that marginal fragmented populations of eastern white cedar (EWC) are genetically isolated due to reduced pollen and gene flow. In accordance with the central-marginal model, we predicted a decrease in population genetic diversity and an increase in differentiation along the latitudinal gradient from the boreal mixed-wood to northern coniferous forest. A total of 24 eastern white cedar populations were sampled along the north-south latitudinal gradient for microsatellite genotyping analysis. Positive Fis values and heterozygote deficiency were observed in populations from the marginal (Fis = 0.244; PHW = 0.0042) and discontinuous zones (Fis = 0.166; PHW = 0.0042). However, populations from the continuous zone were in HW equilibrium (Fis = −0.007; PHW = 0.3625). There were no significant latitudinal effects on gene diversity (Hs), allelic richness (AR), or population differentiation (Fst). Bayesian and NJT (neighbor-joining tree) analyses demonstrated the presence of a population structure that was partly consistent with the geographic origins of the populations. The impact of population fragmentation on the genetic structure of EWC is to create a positive inbreeding coefficient, which was two to three times higher on average than that of a population from the continuous zone. This result indicated a higher occurrence of selfing within fragmented EWC populations coupled with a higher degree of gene exchange among near-neighbor relatives, thereby leading to significant inbreeding. Increased population isolation was apparently not correlated with a detectable effect on genetic diversity. Overall, the fragmented populations of EWC appear well-buffered against effects of inbreeding on genetic erosion.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Biosketches
  9. Conflict of Interest
  10. References
  11. Supporting Information

Climate is among the most important ecological processes that strongly shape the range and genetic diversity of a species (Hewitt 2000; Thomas et al. 2004; Sexton et al. 2009; Hoban et al. 2010; Provan and Maggs 2011). The well-documented central-marginal model (Diniz-Filho et al. 2009), which is also referred to as the abundant-center model (Sagarin and Gaines 2002; Sagarin et al. 2006), predicts geographic variation in population genetic structure across a species' range (Loveless and Hamrick 1984; Yakimowski and Eckert 2008). Populations at the edge of their distribution range are subject to ecological marginality, which may affect population genetic diversity due to harsher environmental conditions (e.g., limited resources for growth and mating), isolation, and fragmentation (Diniz-Filho et al. 2009; Tollefsrud et al. 2009; Hoban et al. 2010). Fragmented populations may be prone to genetic loss and increased genetic differentiation through drift (Ellstrand and Elam 1993; Young et al. 1996; Aguilar et al. 2008). However, these responses are unlikely to be universal. Long-lived plant species, such as trees, may be buffered against genetic effects for decades or centuries (Templeton and Levin 1979; Cabin 1996; Piotti 2009). Tree species combine life-history traits that promote a high level of gene flow between populations, the maintenance of a high within-population gene diversity and low population differentiation (Hamrick et al. 1992). Thus, the genetic consequences of recent alterations to mating systems in remnant fragments are sometimes not detectable for a long time (Gamache et al. 2003).

In the boreal forests of Canada, many tree species reach their continuous distribution range at the transition between the southern mixed-wood forests, which are dominated by balsam fir (Abies balsamea [L.] Miller), and the northern coniferous forest, which is dominated by black spruce (Picea mariana [Miller] BSP). The present-day transition between these two boreal zones is controlled by both climate and fire (Bergeron et al. 2004). Mixed-wood forests are characterized by smaller and fewer severe fire events than are coniferous forests (Hély et al. 2001). Large and severe fires induce high tree mortality that results in a disadvantage to mixed-wood forest species, which generally need survivor seed trees to reinvade burned areas (Asselin et al. 2001; Bergeron et al. 2004; Albani et al. 2005).

Paleoecological records indicate that the presence of mixed-wood forest species in the coniferous forest possibly represents the remnants of formerly larger populations and would thus result from the fragmentation of those initial populations. In western Québec, post-glacial colonization occurred rapidly after the retreat of proglacial Lake Ojibway (8400 cal. BP) and involved all of the tree species that are presently found within the area (Richard 1980; Liu 1990; Carcaillet et al. 2001). Since 7000 cal. BP, balsam fir and black spruce have dominated the mixed-wood and coniferous forests, respectively (Garralla and Gajewski 1992; Gajewski et al. 1996; Carcaillet et al. 2001). The decline in the number of mixed-wood forest species could be related to the climatic shift that characterized the beginning of the Neoglacial period and the establishment of cooler and drier summers coincident with an increase in fire frequency in the coniferous forest 3000 cal. BP (Carcaillet et al. 2001; Ali et al. 2009).

Eastern white cedar (Thuja occidentalis L.) (Fig. 1) is ill-adapted to fire and needs a protected area to reinvade burned areas. This species does not regenerate easily after fire, and population fragmentation following such a disturbance greatly limits its natural distribution. Eastern white cedar (EWC) reaches its northernmost distribution limit in the James Bay region of Québec at the ecotone of the mixed-wood and coniferous forest, at which point its distribution becomes increasingly sporadic as one moves northward along a latitudinal gradient.

image

Figure 1. Eastern white cedar (Thuja occidentalis L.).

Download figure to PowerPoint

In this study, we examined the impact of population fragmentation on the genetic diversity of EWC toward the northern edge of its range. Previous genetic studies have been based on studying allozymes and showed contrasting results. Lamy et al. (1999) reported the presence of a substantial level of genetic substructuring (Fst = 0.073) within six EWC populations. In contrast, Perry and others showed that six northern populations were not differentiated (Fst = 0.016) and, indeed, were in Hardy–Weinberg equilibrium (Perry and Knowles 1989, 1990, 1991; Perry et al. 1990).

Our main hypothesis is that populations of EWC are more genetically isolated toward the northern edge of this species' range, due to reduced pollen and gene flow between populations. We tested whether population differentiation increases and genetic diversity decreases from continuous to discontinuous and to the peripheral part of the species' distribution range. Understanding the genetic structural pattern of ecotonal populations is important because remnant marginal stands that have been eroded from larger populations that were present during the early Holocene might be at the forefront of range expansion driven by climatic changes. The amount and structure of genetic variation within these remnant populations will likely affect their potential to respond to climatic changes.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Biosketches
  9. Conflict of Interest
  10. References
  11. Supporting Information

Study area and materials

Eastern white cedar, which is native to North America, is a wind-pollinated, monoecious, evergreen conifer species (Fowells 1965). An abundant seed crop occurs every 3–5 years, with cones opening in the autumn, but seeds may continue to fall throughout winter. Sexual maturity is generally reached at an early age, but effective seed dispersal is observed after age 20 years. Most seeds are disseminated by wind, with seed dispersal distances with estimates ranging from 45 to 60 m (Fowells 1965). Eastern white cedar is a long-lived species, which can live up to 800 years in Quebec (Archambault and Bergeron 1992).

The study area is located in the Abitibi-Témiscamingue and Nord-du-Québec regions of Quebec and is divided into three bioclimatic zones based on the abundance of EWC (Fig. 2). The continuous zone falls into the balsam fir (Abies balsamea [L.] Mill.) and yellow birch (Betula alleghaniensis Britton) bioclimatic domain and represents an area where eastern white cedar is common. The discontinuous zone is in the balsam fir and white birch (Betula papyrifera Marsh.) bioclimatic domain and marks the northern edge of the continuous distribution, where eastern white cedar becomes less common in the forest matrix. The marginal zone is in the black spruce (Picea mariana [Mill.] B.S.P.) and feather moss bioclimatic domain, where only a few isolated populations are found. The site occupation rates by EWC along the gradient were estimated to 55%, 9%, and 3% in the continuous, discontinuous, and marginal zones, respectively (Paul 2011).

image

Figure 2. Distribution of eastern white cedar in Quebec and North America (shaded region in the map inset) and sampling sites were doted in red; the study area is divided according to bioclimatic zone (Paul 2011).

Download figure to PowerPoint

A total of 24 populations were selected: eight in the continuous zone (Témiscamingue, CZ1 to CZ8), seven in the discontinuous zone (Abitibi, DZ1 to DZ7), and nine in the marginal zone (Chibougamau, MZ1 to MZ4; James Bay, MZ5 to MZ9) (Table 1). Population sizes range from less than one hundred individuals in marginal and discontinuous zones to thousands of individuals in the continuous zone, with the exception of two marginal populations (MZ6, MZ2) that had 8 and 11 trees, respectively. The distance between one population and its nearest neighbor ranges from about 2 to 70 km, except for populations in Chibougamau (MZ1–MZ4), which were located about 300 km from others in the marginal zone. Between 15 and 30 trees were randomly selected in each site, for a total of about 180 trees per zone; we retained marginal populations MZ6 and MZ2 in the analysis. Foliage was collected from individual trees in each population and used for DNA analysis.

Table 1. Genetic variability in 24 EWC populations
LocationPopLatitudeLongitude N AR N a N e H o H e
  1. Na,average number of alleles per locus; Ne,average number of effective alleles per locus. Ho,observed heterozygosity; He,expected heterozygosity; AR, allelic richness; N, number of individuals genotyped per population.

Marginal zone (MZ)
ChibougamauMZ149.8754−74.3928216.18.04.60.6550.756
MZ249.90916−74.3226116.47.05.20.7270.794
MZ349.95351−74.2291206.68.06.10.4630.826
MZ449.64176−74.3341186.98.36.40.6390.840
James BayMZ548.92772−78.8858306.69.56.20.6610.815
MZ649.42317−79.21185.35.34.00.8130.740
MZ749.85853−78.6072206.38.35.20.6380.797
MZ849.88349−78.6461256.710.05.10.6800.798
MZ949.85609−78.6449246.18.84.90.7400.781
Mean206.38.15.30.6680.794
Pooled17711.511.57.80.6570.867
Discontinuous zone (DZ)
AbitibiDZ148.5402−78.6419306.08.34.90.6830.789
DZ248.47015−79.4524246.18.34.80.6250.789
DZ348.47979−79.4368255.47.54.20.7200.753
DZ448.43161−79.4018285.48.04.10.7590.743
DZ548.26296−78.5748256.08.35.10.6200.759
DZ648.43101−79.3842256.48.55.30.6300.805
DZ748.20132−79.4191195.26.54.00.8820.728
Mean255.87.94.60.7030.767
Pooled17611.811.86.30.6970.834
Continuous zone (CZ)
TémiscamingueCZ147.42922−78.6785305.67.84.40.8420.771
CZ247.41669−78.6821275.26.83.90.7960.712
CZ347.39557−78.7316264.66.03.50.8270.714
CZ447.34505−79.3926154.65.03.60.8500.721
CZ547.3111−78.5155235.57.34.30.8700.744
CZ647.45395−78.5877306.28.85.60.8830.801
CZ747.41894−78.6784296.99.56.40.7930.826
CZ847.41579−78.7117186.18.05.00.7780.780
Mean255.67.44.60.8300.759
Pooled19812.513.56.30.8310.823

DNA extraction, microsatellite loci amplification, and genotyping

Foliage samples were ground, and genomic DNA was extracted using the GenElute Plant Genomic DNA Miniprep Kit (Sigma-Aldrich, St. Louis, MO, USA)). Amplification was performed by a gradient polymerase chain reaction (PCR) in a total volume of 10 μL using a 96-well GeneAmp PCR System 9700 (Applied Biosystems, California, USA). Each reaction mixture contained 2.5 μL of DNA extract, 2.5 mmol/L MgCl2, 1 pmol each of forward and reverse primers, 0.2 μL of 10 mmol/L dNTP Mix, 1 μL 10X NovaTag Hot Start Buffer, and 0.25 U NovaTag Hot Start DNA Polymerase (Novagen PCR Kit, Madison, Wisconsin). The best results were obtained by performing a touchdown PCR that decreased the annealing temperature by 0.2°C every other cycle. At the end of each cycle, we added a final 72°C extension step. Loci developed by O'Connell and Ritland (2000) for Thuja plicata and by Nakao et al. (2001) for Chamaecyparis obtusa were utilized for microsatellite genotyping. Four loci exhibited high polymorphism (Table S1). Prior to electrophoresis, 0.5 μL of fluorescent dye-labeled PCR products were mixed with 0.25 μL of internal standard (MapMarker-1000) and 10 μL of deionized formamide. The loading products were heat denatured at 95°C for 3 min, immediately placed on ice for 5 min, and separated using capillary electrophoresis on an ABI Prism 3130 Genetic Analyzer (Applied Biosystems). Microsatellites were sized and genotyped using GeneMapper 3.7 (Applied Biosystems).

Descriptive statistics

Micro-Checker software (Oosterhout et al. 2004) was used to detect null alleles and large allele dropouts at each locus for each population. We used the program FreeNA to estimate the frequencies of putative null alleles [r] and genetic differentiation [Fst] with and without ignoring the null alleles at each locus (Chapuis and Estoup 2007). Allele frequency, allele number, and genetic estimates within populations, including the average number of alleles per locus [Na], average number of effective alleles per locus [Ne], observed heterozygosity [Ho], and expected heterozygosity [He], were calculated using GenAlex v. 6.2 (Peakall and Smouse 2006). We also calculated allelic richness [AR] using rarefaction and the inbreeding coefficient [Fis] at each locus. The calculations were performed using FSTAT v. 2.9.3 (Goudet 2001). We also calculated the aforementioned genetic estimates on pooled samples for each zone. Hardy–Weinberg equilibrium was tested in each population. We also ran a global test of Hardy–Weinberg equilibrium for pooled samples from three distribution zones and for all pooled samples as a group. Bonferroni correction (Rice 1989) was applied when testing the significance of heterozygosity deficit and heterozygosity excess. All of the HW equilibrium tests were performed in FSTAT v. 2.9.3 (Goudet 2001).

Latitudinal effects on genetic estimates

We tested for latitudinal effects by comparing differences in population genetic estimates among the three zones (marginal, discontinuous, and continuous). The genetic estimates that we compared included AR, Ho (Nei 1987), gene diversity [Hs] (Nei 1987), Fis (Weir and Cockerham 1984), Fst (Weir and Cockerham 1984), relatedness [Rel], and corrected relatedness [Relc]. We applied Hamilton's (1971) measure of relatedness, which was calculated using an estimator that was strictly equivalent to the one proposed by Queller and Goodnight (1989). To avoid bias in relatedness when inbreeding exists, we applied the corrected relatedness of Pamilo (1984, 1985). All calculations and subsequent comparisons using a permutation procedure (10,000 iterations) were performed using FSTAT v. 2.9.3 software followed the statistics of its documentation (Goudet 2001).

Population genetic structure

To reveal genetic structure, and test if the samples could be clustered according to their respective distribution zones, we used STRUCTURE v. 2.3.2 software (Pritchard et al. 2000). Individuals were assigned to a number of assumptive clusters (assumptive groups) (K) ranging from 1 to 15 with an admixture model and the option of correlated allele frequency (Falush et al. 2003). All parameters were set following the user's manual. To choose an appropriate run length, we performed a pilot run that showed that burn-in and MCMC (Markov chain Monte Carlo) lengths of 300,000 each were sufficient to obtain consistent data. Increasing the burn-in or MCMC lengths did not improve the results significantly. Ten replicate runs for each value of K were carried out. The most likely value of K was selected by plotting ∆K following the ad hoc statistics (Evanno et al. 2005). The STRUCTURE results were graphically displayed using DISTRUCT (Rosenberg 2004). A neighbor-joining tree analysis (Saitou and Nei 1987) was also used to analyze the genetic structure of our samples. The neighbor-joining tree was visualized using TreeView software (D.m.page, 1996) based on Nei's standard (Nei 1987) genetic distance, Ds, calculated using the program POPULATIONS v. 1.2.30 (http://bioinformatics.org/~tryphon/populations/). The neighbor-joining tree was bootstrapped 1000 times.

We determined the overall level of genetic differentiation using analysis of molecular variance (AMOVA) (Excoffier et al. 1992). The genetic distance matrix based on pairwise Fst (Weir and Cockerham 1984) was used to carry out the AMOVA using Arlequin v. 3.11 (Excoffier et al. 2005), with 10,000 permutations. Analysis of molecular variance was performed without grouping populations, with grouping populations by assigning them to three geographic zones, and with grouping populations by assigning them to a number of genetic groups that were identified by STRUCTURE v. 2.3.2 (Pritchard et al. 2000). We also performed a separate AMOVA on data from each of the three distribution zones. The geographic distance matrix was calculated using PASSaGE2 software (Rosenberg and Anderson 2011). A Mantel test (Mantel 1967) was applied to analyze the correlation between the geographic distance and Nei's standard genetic distance (Nei 1987). All Mantel tests were performed using GenAlex v. 6.2 (Peakall and Smouse 2006).

Population genetic bottleneck

We tested for a recent population genetic bottleneck using the program BOTTLENECK v. 1.2.02 (Piry et al. 1999). An infinite allele model (IAM) and one-step stepwise mutation model (SMM) were applied in the bottleneck program (Cornuet and Luikart 1996). As all loci were in-between, we finally used the option of a two-phase model (TPM) (Di Rienzo et al. 1994) with 95% SMM and 5% IAM and a variance of 12, as recommended by Piry et al. (1999). Wilcoxon's test, which is better adapted to a dataset with few polymorphic loci (our case), has a robustness similar to the sign test and is as powerful as the standardized differences test, was used to test the significance of the heterozygosity excess (Piry et al. 1999). A graphical descriptor was also used to distinguish between stable and bottlenecked populations (Luikart et al. 1998). We complemented the results of heterozygosity excess and mode-shift tests with Bayesian MSVAR (Beaumont 1999; Storz and Beaumont 2002; Girod et al. 2011). MSVAR assumes that microsatellite data evolve by a stepwise mutation model and it relies on MCMC simulation to estimate the posterior distribution of parameters that describe the demographic history (Beaumont 1999). The parameters of interest in our study were current population size (N0), ancestral population size at the time population started to decline or expand (N1), and time (in generations) since population started to decline or expand (T). The change in population size was determined by the ratio r (= N0/N1) where r < 1 indicates decline, r = 1 indicates stability, and r > 1 indicates expansion (Beaumont 1999). As the generation time for EWC is unknown, we used a value of 20 years, given that its effective seed dispersal is observed after age 20 (Fowells 1965). The exponential model was applied. The length of run for chains was determined by Raftery–Lewis statistic (Raftery and Lewis 1992, 1995). Two-hundred million iterations were sufficiently long for each chain to converge, with every 10,000th sample points being stored. The first 10% of data points were discarded from chains as burn-in to achieve stable simulations. The output was analyzed with CODA 0.14-7 package implemented in R version 2.15.0 (http://cran.r-project.org/).

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Biosketches
  9. Conflict of Interest
  10. References
  11. Supporting Information

Descriptive statistics

The number of alleles per locus ranged from 10 (Locus TP10) to 17 (Locus TP12) (Table S1). Our results showed that all four loci were highly polymorphic (Table S2). The number of alleles per locus ranged from 8 at locus TP10 in the populations from the discontinuous distribution zone to 17 at locus TP12 in populations from the continuous distribution zone (Table S2). All loci exhibited positive Fis except for locus TP10 (Table S1). MICRO-CHECKER detected the presence of null alleles at loci TP9, TP11, and TP12, and there was no evidence for large allele dropout or scoring errors due to stuttering. Null alleles occurred at very low frequencies, and similar levels of genetic differentiation (Fst) were obtained when either excluding or not excluding the null alleles (Table S1).

At the population level, AR averaged 5.9 and ranged from 4.6 (CZ3, CZ4) to 6.9 (MZ4, CZ7). Na ranged from 5.3 (MZ6) to 10.0 (MZ8), with an average of 7.8. The mean Ne was 4.9, with lowest value being 3.5 (CZ3) and the highest being 6.4 (MZ4, CZ7). Ho had a mean value of 0.7 and was lowest in population MZ3 (0.463) and highest in population CZ6 (0.883). The mean He was 0.77, ranging from 0.712 (CZ2) to 0.826 (MZ3, CZ7) (Table 1).

When populations were pooled, AR was quite similar among the three distribution zones (11.5, 11.8, and 12.5), as was Na. Ho showed an increase from the marginal zone (0.657) to the discontinuous zone (0.697), further, to the continuous zone (0.831) (Table 1). The populations from the continuous distribution zone had the highest proportion of rare alleles (frequency <1%; 0.148) and the highest total number of alleles (54) across the loci; the populations with the second highest proportion were from the discontinuous distribution zone (0.106; 47), and the populations with the least were from the marginal distribution zone (0.065; 46) (Table S2). Only populations from the continuous distribution zone had private alleles (one at locus TP10 and TP12) (Table S2).

Latitudinal effects on genetic estimates

Among the 24 populations, seven (four marginal: MZ3, MZ4, MZ5, MZ7; three discontinuous: DZ2, DZ5, DZ6) showed a significant deficiency of heterozygotes and a departure from Hardy–Weinberg equilibrium (data not shown). None of the populations from the continuous distribution zone exhibited significant departure from HW equilibrium (data not shown). When populations were pooled, the global HW test revealed a significant departure from equilibrium and a slight heterozygote deficiency (Fis = 0.145; PHW = 0.0125). Positive Fis values and heterozygote deficiency were also observed in populations from the marginal (Fis = 0.244; PHW = 0.0042) and discontinuous (Fis = 0.166; PHW = 0.0042) distribution zones. However, populations from the continuous zone were in HW equilibrium (Fis = −0.007; PHW = 0.3625) (Table 2).

Table 2. Hardy–Weinberg equilibrium test
Region F is Heterozygosity deficitHeterozygosity excessP-value
  1. N/A, not applicable; ns, not significant.

  2. *P < 0.05.

  3. Bonferroni corrections were applied.

Marginal zone0.244 * N/A0.0042
Discontinuous zone0.166 * N/A0.0042
Continuous zone−0.007N/Ans0.3625
Global0.145 * N/A0.0125

The difference in Ho among the populations from the three zones was highly significant (= 0.003), as were differences for Fis (= 0.002) and Relc (= 0.005). We did not find any significant differences for AR, Hs, Fst, and Rel among the populations from the three zones (Table 3).

Table 3. Comparisons of genetic estimate differences among populations from three zones
 ARHoHs F is F st RelRelc
  1. ns, not significant.

  2. *P < 0.05, significant.

  3. **P < 0.01, highly significant.

  4. P-values were obtained after 1000 permutations. AR: allelic richness. Ho, observed heterozygosity; Hs, gene diversity; Fis, inbreeding coefficient; Fst, population differentiation; Rel, relatedness; Relc, corrected relatedness.

Marginal zone6.3340.6570.8230.2020.0600.096−0.505
Discontinuous zone5.8050.6970.7860.1120.0700.119−0.253
Continuous zone5.5890.8310.777−0.0700.0660.1320.130
P-valuesns ** ns**nsns**
(0.072)(0.003)(0.153)  (0.002)(0.926)(0.702)  (0.005)

Further comparisons revealed that the difference in Ho was not significant between populations from the marginal and discontinuous zones. It was significantly different between the discontinuous and continuous zones (= 0.010) and between the marginal and continuous zones (= 0.001) (data not shown). Similarly, the differences between populations for Fis and Relc were only significant between the discontinuous and continuous zones (Fis, = 0.027; Relc, = 0.052) and between the marginal and continuous zones (Fis, = 0.001; Relc, = 0.001) (data not shown).

Genetic structure patterning

Bayesian analysis demonstrated the presence of population structure. The three clusters detected by STRUCTURE (Fig. S1) are displayed in orange, yellow, and blue. The largest cluster (yellow) includes 14 populations crossing the three zones (MZ5, MZ6, MZ7, MZ8, MZ9, DZ1, DZ2, DZ3, DZ4, DZ5, CZ5, CZ6, CZ7, and CZ8). The cluster depicted in blue includes five populations: four from southern sites in Témiscamingue (CZ1 to CZ4) and one from the discontinuous zone (DZ7). The cluster depicted in orange includes four populations from the northern sites (MZ1 to MZ4) and DZ6 in the discontinuous zone (Fig. 3). Most of the individuals from the marginal Chibougamau populations and population DZ6 from the discontinuous zone (Abitibi) were assigned to only one cluster. Similarly, almost all individuals from the Témiscamingue populations (CZ1 to CZ4) were assigned to only one cluster.

image

Figure 3. Study site, geographic origin, and genetic structure of Thuja occidentalis populations deduced by STRUCTURE at K = 3 (Orange cluster: MZ1, MZ2, MZ3, MZ4, and DZ6; yellow: MZ5, MZ6, MZ7, MZ8, MZ9, DZ1, DZ2, DZ3, DZ4, DZ5, CZ5, CZ6, CZ7, and CZ8; blue: CZ1, CZ2, CZ3, CZ4, and DZ7).

Download figure to PowerPoint

The results of the NJT that were based on Nei's (Nei 1987) standard genetic distance (Ds) were partially consistent with the geographic origins of the populations (Fig. 4). Four clusters can be identified at increased confidence levels (bootstrap values ≥50). Two of these clusters were also identified using STRUCTURE. MZ1, MZ2, MZ3, MZ4, and DZ6 were assigned to one cluster, while CZ1, CZ3, CZ2, and CZ4 were assigned to another cluster.

image

Figure 4. Neighbor-joining tree of Thuja occidentalis populations based on Nei's standard genetic distance, Ds (Nei 1987). The numbers indicate the bootstrap values; only values ≥ 50% are presented.

Download figure to PowerPoint

Genetic variation partitioning

AMOVA revealed a significant level of differentiation among the EWC populations, with 7.7% of the variation found among populations and 92.3% within populations (Table 4). When the populations are pooled based on their distribution zones (marginal, discontinuous, continuous), 1.5% of the variability occurred among zones and 6.6% occurred among populations within a zone. When the populations are pooled according to the results obtained with STRUCTURE (MZ1, MZ2, MZ3, MZ4, and DZ6 (orange); MZ5, MZ6, MZ7, MZ8, MZ9, DZ1, DZ2, DZ3, DZ4, DZ5, CZ5, CZ6, CZ7, and CZ8 (yellow); CZ1, CZ2, CZ3, CZ4, and DZ7 (blue)), the variation among groups was estimated to be 7.1% and 3.4% among populations within groups. The level of variation among populations within zones was generally similar (6.0, 7.0, and 6.6%; Table 4). The variance explained by individuals within populations from the continuous zone is negative (−6.5%) and can be interpreted as being zero, which indicates an absence of genetic structure.

Table 4. Analysis of molecular variance for 24 populations, for populations pooled by zones (continuous, discontinuous, and marginal), for populations pooled in groups identified by STRUCTURE, and for populations at the level of each zone
Source of variationSum of squaresVariance componentsPercentage variationP value
All populations
Among populations176.0340.129527.699040.00000
Within populations904.3040.1240192.300960.00000
Pooled by zones
Among zones33.4920.026101.510480.00059
Among populations within zones142.5430.111346.611510.00000
Within populations904.3040.1240191.878010.00000
Groups by clusters (3) identified by STRUCTURE
Among groups84.8300.126287.124980.00000
Among populations within groups91.2040.057363.392390.00000
Within populations904.2040.1240189.482630.00000
Populations at the level of each zone
Marginal
Among populations48.6250.105116.000380.00000
Among individuals334.3610.3319318.949620.00000
Discontinuous
Among populations46.0010.118036.987060.00000
Among individuals295.3320.1763210.437820.00000
Continuous
Among populations47.9170.109806.601700.00000
Among individuals274.611−0.10815−6.502051.00000

The correlation between genetic and geographic distances was positive and significant when all 24 populations were included in the analysis (Mantel test: = 0.645, = 0.001). However, this correlation became non-significant when the populations from Chibougamau (which are geographically distant from all other sampled populations, >300 km from the populations of James Bay) were excluded from the analysis (= −0.0002, = 0.571) (Fig. 3). Moreover, no significant correlation between geographic and genetic distances was detected when the IBD (isolation by distance) was tested at the level of each zone (data not shown).

Population genetic bottleneck

A genetic bottleneck was detected by heterozygosity excess test in only one marginal population (MZ4) under both TPM and SMM models. However, population MZ4 had a normal L-shaped allelic distribution, indicating that the bottleneck was not recent or that the population is not completely isolated. Bayesian MSVAR detected a population decline in marginal population MZ8 (r = 0.87). Several populations (MZ3, MZ4, and MZ5) had r-ratios slightly below 1, which indicated a slight decline in population size (Table 5). The remaining populations showed a signal of recent expansion (r > 1) (Table 5).

Table 5. Results of MSVAR analysis of population expansion or decline
ParameterN0SELower BoundUpper BoundN1SELower BoundUpper BoundTS.E.Lower BoundUpper Boundr-ratio
  1. N0, current effective population size; N1, ancestral effective population size; T, time in generations since population size changes; Lower and upper bound are presented as 90% Highest Probability Density intervals.

Marginal zone (MZ)
MZ14.350.00663.195.424.150.01151.806.754.430.01481.297.911.05
MZ24.370.00653.235.544.210.01281.437.424.390.01501.458.071.04
MZ34.430.00822.856.274.750.01172.687.614.670.01801.088.670.93
MZ44.330.00603.305.394.500.01172.337.274.290.01541.328.240.96
MZ54.430.00773.116.194.660.01272.317.604.420.01751.218.610.95
MZ64.470.00543.465.453.820.01081.556.424.480.01201.747.251.17
MZ74.450.00573.465.423.870.01071.175.794.310.01331.717.721.15
MZ84.360.00832.736.335.020.01143.277.814.700.01911.128.910.87
MZ94.470.00473.615.273.350.00991.094.653.990.01151.576.791.33
Discontinous zone (DZ)
DZ14.660.00303.985.302.410.00621.013.693.780.00602.425.041.94
DZ24.550.00433.795.333.250.00861.624.374.090.00912.175.941.40
DZ34.470.00533.475.453.560.00951.414.674.440.01002.386.521.26
DZ44.560.00333.855.253.290.00472.244.334.400.00632.995.721.39
DZ54.510.00323.815.202.620.00611.313.983.700.00672.095.031.72
DZ64.470.00293.825.073.050.00771.554.303.740.00672.345.091.46
DZ74.670.00304.015.352.570.00471.583.594.110.00473.105.151.82
Continuous zone (CZ)
CZ14.480.00373.765.132.720.00920.854.233.750.00921.535.221.65
CZ24.180.00573.185.103.460.01030.954.954.110.01171.586.811.21
CZ34.280.00533.335.233.170.00910.994.434.240.01141.836.811.35
CZ44.300.00713.005.653.560.01110.975.564.640.01411.637.861.21
CZ54.280.00732.935.674.190.01171.856.974.530.01551.167.891.02
CZ64.540.00733.206.054.350.00982.586.634.940.01591.548.461.04
CZ74.720.01222.517.624.500.00643.315.644.300.01661.048.321.05
CZ84.740.01072.987.374.410.00783.066.064.940.01511.778.331.07

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Biosketches
  9. Conflict of Interest
  10. References
  11. Supporting Information

Microsatellite markers revealed a significant effect of habitat fragmentation on the genetic structure in EWC populations. Populations from the marginal and discontinuous distribution ranges showed an excess of homozygotes, whereas populations from the continuous range were in HW equilibrium. Therefore, the impact of population fragmentation on the EWC genetic structure is the existence of a positive inbreeding coefficient, which was, on average, nearly 2 to 3 times higher than that of populations from the continuous zone (Table 3). This pattern could also partially reflect historical events (e.g., effects of post-glacial migration and colonization) as the farthest north population experienced population decline (Hoban et al. 2010; Dudaniec et al. 2012). This result indicated the presence of a higher occurrence of selfing within fragmented EWC populations that was coupled with a higher degree of gene exchange among near-neighbor relatives, leading to significant inbreeding. In their review, Aguilar et al. (2008) reported a trend of increased inbreeding due to habitat fragmentation; however, they reported a non-significant overall effect on Fis, possibly because the fragmentation was too recent. In many published studies, the sampled adults were established before fragmentation occurred (Young et al. 1996; Lowe et al. 2005; Kettle et al. 2007). Indeed, the effect of population fragmentation on inbreeding coefficients can be detectable only after the first generation of progeny has been established.

The presence of a high level of self-fertilization in EWC has been reported in previous studies (Perry and Knowles 1990; Lamy et al. 1999). Lamy et al. (1999) showed that mating patterns are biased toward higher selfing in recently fragmented, small EWC populations. This life-history characteristic contrasts with most coniferous species, which are generally much more affected by inbreeding (Mitton 1983; Plessas and Strauss 1986; Gauthier et al. 1992; Beaulieu and Simon 1995; Ledig et al. 2000; Gamache et al. 2003; Gapare et al. 2005). A high level of inbreeding, maintained over several generations, is expected to lead to progressive genetic erosion, higher between-population differentiation and an overall decrease in genetic diversity. This pattern was not observed in this study. Genetic variation among populations was similar in the marginal, discontinuous, and continuous populations (6.0%, 7.0%, and 6.6%, respectively), as were the levels of genetic diversity (Hs, Table 3), except that only populations from continuous zones had private alleles. This is probably because the fragmentation has not progressed long enough to have detectable effects on progressive genetic erosion. Long-lived trees may be buffered against genetic erosion for centuries (Templeton and Levin 1979; Cabin 1996; Piotti 2009).

The global level of differentiation among EWC populations was relatively high and similar to that reported by Lamy et al. (1999) (7.7% vs. 7.3%) in populations sampled over a much smaller geographic area (180 km2). It was also higher than those values that were reported in EWC populations by Matthes-Sears et al. (1991) (1.9%) and Perry et al. (1990) (1.6%). Most alleles were distributed in populations throughout the three zones. Populations from the continuous distribution zone harbored the highest proportion of rare alleles (frequency <1%), with a decreasing trend toward the northern range margins. Yet, no significant differences were observed in allelic richness among populations from the three bioclimatic zones, indicating that populations residing in the discontinuous or marginal distribution ranges have not experienced a great decrease in population size or, if so, have overcome previous bottlenecks (Nei et al. 1975; Leberg 2002). The evidence of population decline was detected in marginal populations (MZ3, MZ4, MZ5, and MZ8). However, the detection power of our bottleneck analysis was weak due to the limited number of polymorphic microsatellite loci available for the EWC. Our results were still comparable to other studies that detected significant bottlenecks based on four polymorphic loci (Aizawa et al. 2009; Heuertz et al. 2010). Genetic bottleneck effects could also be obscured by immigration events.

The majority of studies that have examined geographic variation in genetic diversity have used a ‘categorical approach' in which only groups of peripheral and central populations were sampled (Eckert et al. 2008). Yet, the ‘categorical approach’ has also been blamed for confounding geographic position with region compared to ‘continuous sampling approach’. Our study relaxed this confounding by sampling along a latitudinal transect that encompasses central, intermediate, and peripheral populations. The geographic distribution of EWC along the latitudinal gradient was estimated from the analysis of a large inventory database (a total of 5476 sample plots) and found to decrease from 55% to 9% to 3% from the continuous to the discontinuous to the marginal zones, respectively (Paul 2011). This pattern conforms to the ‘abundant centre model’, which predicts an increase in the spatial isolation of populations from the range center toward the range limits (Sagarin and Gaines 2002; Eckert et al. 2008). This increase in population isolation was apparently not correlated with a detectable effect on genetic diversity. One plausible explanation involves the life-history characteristics of EWC. Selfing species naturally retain most of their genetic diversity within populations, and their level of population genetic diversity is less affected by restricted gene flow. Moreover, the ability of EWC to reproduce vegetatively, via layering, may buffer the genetic effects of fragmentation by delaying the time between generations (Honnay and Bossuyt 2005). A parallel study conducted at the same sites showed higher levels of layering in populations in the north (marginal and discontinuous zones) than in the south (continuous zone), with equivalent seed production along the gradient (Paul 2011). Finally, the effect of inbreeding on genetic erosion may also be buffered by selection against homozygotes in young EWC individuals, which will eliminate a higher proportion of these individuals before they become adults.

Population structure

Both Bayesian and NJT analyses detected a certain level of genetic structure among the 24 EWC populations. Interestingly, the four marginal populations (MZ1, MZ2, MZ3, and MZ4) from Chibougamau and one population (DZ6) from Abitibi were assigned to one cluster, even though more than 400 km separated DZ6 from the Chibougamau marginal populations. One explanation may be that these populations followed the same post-glacial migration route. Apparently, the four populations from Témiscamingue (CZ1, CZ2, CZ3, and CZ4) belonged to the same cluster, indicating that gene flow (via seed or pollen dispersal) was high among them. Some sub-branches of the NJT were significant (bootstrapped values ≥50), such as the sub-branch clustering of DZ1 and DZ5 or that of DZ2 and DZ3. These populations that clustered together are genetically closer and may have followed similar post-glacial migration routes. Fourteen populations (marginal: MZ5, MZ6, MZ7, MZ8, and MZ9; discontinuous: DZ1, DZ2, DZ3, DZ4, and DZ5; and continuous: CZ5, CZ6, CZ7, and CZ8) were assigned into a single (yellow) cluster.

Conservation implications

Our results converged to demonstrate that spatial isolation of marginal EWC populations is not associated with low genetic diversity. Therefore, increased inbreeding does not lead to a loss of genetic variation in northern EWC populations, and therefore they have the potential to respond and adapt to environmental changes. The actual distribution and expansion of white cedar at the northern edge of its range has been limited by climate in association with fires (Paul 2011). This limitation illustrates the complexity of the species' population dynamics and the difficulty of predicting future EWC distributions in a changing environment. If climate favors improved regeneration of this species and its northward migration, peripheral populations could play a major role as seed sources and in the further movement of the geographic range in response to climate changes. In contrast, if global warming triggers an increase in fire frequency (Bergeron et al. 2010), the EWC distribution could be negatively affected and reduced to lower latitudes. In such a context of uncertainty, the precautionary principle should apply, and marginal populations should be protected to allow continuity of natural evolutionary processes.

Acknowledgments

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Biosketches
  9. Conflict of Interest
  10. References
  11. Supporting Information

This study forms part of the PhD thesis of H. Xu. We would like to acknowledge Marie-Hélène Longpré and Danielle Charron for logistical support and Dr. Marc Mazerolle for statistical advice. Special thanks go to industrial supervisors from the company Tembec: Louis Dumas, Sonia Légaré, and Genevieve Labrecque. We thank two anonymous referees for their comments, which helped improve this manuscript. We also thank Dr. W. F. J. Parsons for correcting the references to match the journal. This research was supported by an Industrial Innovation Doctoral Scholarship from NSERC (Natural Sciences and Engineering Research Council of Canada), a BMP innovation doctoral scholarship from FRQ-NT the (Fonds de recherché du Québec- Natures et technologies) and Tembec, and a scholarship from FERLD (Forêt d'enseignement et de recherche du lac Duparquet) to H. Xu. This research was also funded by a NSERC strategic grant (STPGP 336871) to F. Tremblay.

Biosketches

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Biosketches
  9. Conflict of Interest
  10. References
  11. Supporting Information

Huaitong Xu is doing a Ph.D in Environmental Science at University of Quebec (UQAT-UQAM) in Canada. His research interests focus on plant population genetics including the impacts of fragmentation on tree population genetic diversity, tree regeneration patterns following forest succession, population genetic structure of tree species at their distribution limits, and landscape genetics. Dr. Yves Bergeron leads the research team in sustainable forest management, details can be found at http://web2.uqat.ca/cafd/. Author contributions: F.T., Y.B. and H.X. conceived the ideas; V.P. collected the samples; H.X. did the lab work; H.X. and F.T. analyzed the data; H.X. and F.T wrote the manuscript. C.C. gave some suggestions during the manuscript preparations.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Biosketches
  9. Conflict of Interest
  10. References
  11. Supporting Information
  • Aguilar, R., M. Quesada, L. Ashworth, Y. Herrerias-Diego, and J. Lobo. 2008. Genetic consequences of habitat fragmentation in plant populations: susceptible signals in plant traits and methodological approaches. Mol. Ecol. 17:51775188.
  • Aizawa, M., H. Yoshimaru, H. Saito, T. Katsuki, T. Kawahara, K. Kitamura, et al. 2009. Range-wide genetic structure in a north-east Asian spruce (Picea jezoensis) determined using nuclear microsatellite markers. J. Biogeogr. 36:9961007.
  • Albani, M., D. W. Andison, and J. P. Kimmins. 2005. Boreal mixedwood species composition in relationship to topography and white spruce seed dispersal constraint. For. Ecol. Manage. 209:167180.
  • Ali, A. A., C. Carcaillet, and Y. Bergeron. 2009. Long-term fire frequency variability in the eastern Canadian boreal forest: the influences of climate vs. local factors. Glob. Change Biol. 15:12301241.
  • Archambault, S., and Y. Bergeron. 1992. An 802-year tree-ring chronology from the Quebec boreal forest. Can. J. For. Res. 22:674682.
  • Asselin, H., M. J. Fortin, and Y. Bergeron. 2001. Spatial distribution of late-successional coniferous species regeneration following disturbance in southwestern Québec boreal forest. For. Ecol. Manage. 140:2937.
  • Beaulieu, J., and J. P. Simon. 1995. Mating system in natural populations of eastern white pine in Quebec. Can. J. For. Res. 25:16971703.
  • Beaumont, M. A. 1999. Detecting population expansion and decline using microsatellites. Genetics 153:20132029.
  • Bergeron, Y., S. Gauthier, M. Flannigan, and V. Kafka. 2004. Fire regimes at the transition between mixedwood and coniferous boreal forest in northwestern Quebec. Ecology 85:19161932.
  • Bergeron, Y., D. Cyr, M. P. Girardin, and C. Carcaillet. 2010. Will climate change drive 21st century burn rates in Canadian boreal forest outside of its natural variability: collating global climate model experiments with sedimentary charcoal data. Int. J. Wildland Fire 19:11271139.
  • Cabin, R. J. 1996. Genetic comparisons of seed bank and seedling populations of a perennial desert mustard, Lesquerella fendleri. Evolution 50:18301841.
  • Carcaillet, C., Y. Bergeron, P. J. H. Richard, B. Fréchette, S. Gauthier, and Y. T. Prairie. 2001. Change of fire frequency in the eastern Canadian boreal forests during the Holocene: does vegetation composition or climate trigger the fire regime? J. Ecol. 89:930946.
  • Chapuis, M. P., and A. Estoup. 2007. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24:621631.
  • Cornuet, J. M., and G. Luikart. 1996. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:20012014.
  • Diniz-Filho, J. A. F., J. C. Nabout, L. M. Bini, T. N. Soares, M. P. De Campus Telles, P. De Marco Jr., et al. 2009. Niche modelling and landscape genetics of Caryocar brasiliense (“Pequi” tree: Caryocaraceae) in Brazilian Cerrado: an integrative approach for evaluating central-peripheral population patterns. Tree Genet. Genom. 5:617627.
  • Dudaniec, R. Y., S. F. Spear, J. S. Richardson, and A. Storfer. 2012. Current and historical drivers of landscape genetic structure differ in core and peripheral salamander populations. PLoS ONE 7:e36769.
  • Eckert, C. G., K. E. Samis, and S. C. Lougheed. 2008. Genetic variation across species' geographical ranges: the central-marginal hypothesis and beyond. Mol. Ecol. 17:11701188.
  • Ellstrand, N. C., and D. R. Elam. 1993. Population genetic consequences of small population size: implications for plant conservation. Annu. Rev. Ecol. Syst. 24:217242.
  • Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14:26112620.
  • Excoffier, L., P. E. Smouse, and J. M. Quattro. 1992. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131:479491.
  • Excoffier, L., G. Laval, and A. S. Schneider. 2005. Arlequin ver. 3.0: an integrated software package for population genetics data analysis. Evol. Bioinform. Online 1:4750.
  • Falush, D., M. Stephens, and J. K. Pritchard. 2003. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164:15671587.
  • Fowells, H. A. 1965. Silvics of forest trees of the United States. US. Dep. Agric. Agric. Handb. 271:11891209.
  • Gajewski, K., S. Garralla, and V. Milot-Roy. 1996. Postglacial vegetation at the northern limit of lichen woodland in northwestern Québec. Géog. Phys. Quatern. 50:341350.
  • Gamache, I., J. P. Jaramillo-Correa, S. Payette, and J. Bousquet. 2003. Diverging patterns of mitochondrial and nuclear DNA diversity in subarctic black spruce: imprint of a founder effect associated with postglacial colonization. Mol. Ecol. 12:891901.
  • Gapare, W. J., S. N. Aitken, and C. E. Ritland. 2005. Genetic diversity of core and peripheral Sitka spruce (Picea sitchensis (Bong.) Carr) populations: implications for conservation of widespread species. Biol. Conserv. 123:113123.
  • Garralla, S., and K. Gajewski. 1992. Holocene vegetation history of the boreal forest near Chibougamau, central Quebec. Can. J. Bot. 70:13641368.
  • Gauthier, S., J. P. Simon, and Y. Bergeron. 1992. Genetic structure and variability in jack pine populations: effects of insularity. Can. J. For. Res. 22:19581965.
  • Girod, C., R. Vitalis, R. Leblois, and H. Fréville. 2011. Inferring population decline and expansion from microsatellite data: a simulation-based evaluation of the msvar method. Genetics 188:165179.
  • Goudet, J. 2001. FSTAT, a program to estimate and test gene diversities and fixation indices (version 2.9.3). http://www2.unil.ch/popgen/softwares/fstat.htm, Updated from Goudet (1995).
  • Hamilton, W. D. 1971. Selection of selfish and altruistic behavior in some extreme models. Pp. 5791 in E. A. Dillon, ed. Man and beast: comparative social behavior. Smithsonian Inst. Press, Washington, DC.
  • Hamrick, J. L., M. J. W. Godt, and S. L. Sherman-Broyles. 1992. Factors influencing levels of genetic diversity in woody plant species. New Forest. 6:95124.
  • Hély, C., M. Flannigan, Y. Bergeron, and D. McRae. 2001. Role of vegetation and weather on fire behavior in the Canadian mixedwood boreal forest using two fire behavior prediction systems. Can. J. For. Res. 31:430441.
  • Heuertz, M., J. Teufel, S. C. González-Martínez, A. Soto, B. Fady, R. Alía, et al. 2010. Geography determines genetic relationships between species of mountain pine (Pinus mugo complex) in western Europe. J. Biogeogr. 37:541556.
  • Hewitt, G. 2000. The genetic legacy of the quaternary ice ages. Nature 405:907913.
  • Hoban, S. M., D. S. Borkowski, S. L. Brosi, T. S. McCleary, L. M. Thompson, J. S. McLachlan, et al. 2010. Range-wide distribution of genetic diversity in the North American tree Juglans cinerea: a product of range shifts, not ecological marginality or recent population decline. Mol. Ecol. 19:48764891.
  • Honnay, O., and B. Bossuyt. 2005. Prolonged clonal growth: escape route or route to extinction? Oikos 108:427432.
  • Kettle, C. J., P. M. Hollingsworth, T. Jaffré, B. Moran, and R. A. Ennos. 2007. Identifying the early genetic consequences of habitat degradation in a highly threatened tropical conifer, Araucaria nemorosa Laubenfels. Mol. Ecol. 16:35813591.
  • Lamy, S., A. Bouchard, and J. P. Simon. 1999. Genetic structure, variability, and mating system in eastern white cedar (thuja occidentalis) populations of recent origin in an agricultural landscape in southern quebec. Can. J. For. Res. 29:13831392.
  • Leberg, P. L. 2002. Estimating allelic richness: effects of sample size and bottlenecks. Mol. Ecol. 11:24452449.
  • Ledig, F. T., B. Bermejo-Velazquez, P. D. Hodgskiss, D. R. Johnson, C. Flores-Lopez, and V. Jacob-Cervantes. 2000. The mating system and genic diversity in Martinez spruce, an extremely rare endemic of Mexico's Sierra Madre oriental: an example of facultative selfing and survival in interglacial refugia. Can. J. For. Res. 30:11561164.
  • Liu, K. B. 1990. Holocene paleoecology of the boreal forest and Great Lakes-st. Lawrence forest in northern Ontario. Ecol. Monogr. 60:179212.
  • Loveless, M. D., and J. L. Hamrick. 1984. Ecological determinants of genetic structure in plant populations. Annu. Rev. Ecol. Syst. 15:6595.
  • Lowe, A. J., D. Boshier, M. Ward, C. F. E. Bacles, and C. Navarro. 2005. Genetic resource impacts of habitat loss and degradation; reconciling empirical evidence and predicted theory for neotropical trees. Heredity 95:255273.
  • Luikart, G., F. W. Allendorf, J. M. Cornuet, and W. B. Sherwin. 1998. Distortion of allele frequency distributions provides a test for recent population bottlenecks. J. Hered. 89:238247.
  • Mantel, N. 1967. The detection of disease clustering and a generalized regression approach. Cancer Res. 27:209220.
  • Matthes-Sears, U., S. C. Stewart, and D. W. Larson. 1991. Sources of allozymic variation in Thuja occidentalis in southern Ontario. Canada Silvae Genet. 40:100105.
  • Mitton, J. B. 1983. Conifers. Pp. 443472 in S. D. Tanksley and T. J. Orton, eds. Isozymes in plant genetics and breeding. Elsevier, Amsterdam.
  • Nakao, Y., H. Iwata, A. Matsumoto, Y. Tsumura, and N. Tomaru. 2001. Highly polymorphic microsatellite markers in Chamaecyparis obtusa. Can. J. For. Res. 31:22482251.
  • Nei, M. 1987. Molecular evolutionary genetics. Columbia Univ. Press, New York.
  • Nei, M., T. Maruyama, and R. Chakraborty. 1975. The bottleneck effect and genetic variability in populations. Evolution 29:110.
  • O'Connell, L. M., and C. E. Ritland. 2000. Characterization of microsatellite loci in western redcedar (Thuja plicata). Mol. Ecol. 9:19201922.
  • van Oosterhout, C., W. F. Hutchinson, D. P. M. Wills, and P. Shipley. 2004. MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol. Ecol. Notes 4:535538.
  • Page, R. D. M. 1996. Treeview: an application to display phylogenetic trees on personal computers. Comput. Appl. Biosci. 12:357358.
  • Pamilo, P. 1984. Genotypic correlation and regression in social groups: multiple alleles, multiple loci and subdivided populations. Genetics 107:307320.
  • Pamilo, P. 1985. Effect of inbreeding on genetic relatedness. Hereditas 103:195200.
  • Paul, V. 2011. Les facteurs écologiques limitant la répartition nordique du thuja de l'est (thuja occidentalis L.). Master in Biology Master's thesis, Université du Québec en Abitibi-Témiscamingue, Rouyn-Noranda, Quebec, Canada.
  • Peakall, R., and P. E. Smouse. 2006. GENALEX 6: Genetic analysis in excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6:288295.
  • Perry, D. J., and P. Knowles. 1989. Inheritance and linkage relationships of allozymes of eastern white cedar (Thuja occidentalis) in northwestern Ontario. Genome 32:245250.
  • Perry, D. J., and P. Knowles. 1990. Evidence of high self-fertilization in natural populations of eastern white cedar (Thuja occidentalis). Can. J. Bot. 68:663668.
  • Perry, D. J., and P. Knowles. 1991. Short-note: are inferred out-crossing rates affected by germination promptness? Silvae Genet. 40:3536.
  • Perry, D. J., P. Knowles, and F. C. Yeh. 1990. Allozyme variation of Thuja occidentalis L. in northwestern Ontario. Biochem. Syst. Ecol. 18:111115.
  • Piotti, A. 2009. The genetic consequences of habitat fragmentation: the case of forests. iForest 2:7576.
  • Piry, S., G. Luikart, and J. M. Cornuet. 1999. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90:502503.
  • Plessas, M. E., and S. H. Strauss. 1986. Allozyme differentiation among populations, stands, and cohorts in Monterey pine. Can. J. For. Res. 16:11551164.
  • Pritchard, J. K., M. Stephens, and P. Donnelly. 2000. Inference of population structure using multilocus genotype data. Genetics 155:945959.
  • Provan, J., and C. A. Maggs. 2011. Unique genetic variation at a species' rear edge is under threat from global climate change. Proc. R. Soc. B Biol. Sci. 279:3947.
  • Queller, D. C., and K. F. Goodnight. 1989. Estimating relatedness using genetic markers. Evolution 43:258275.
  • Raftery, A. E., and S. M. Lewis. 1992. One long run with diagnostics: implementation strategies for Markov chain Monte Carlo. Stat. Sci. 7:493497.
  • Raftery, A. E., and S. M. Lewis. 1995. The number of iterations, convergence diagnostics and generic Metropolis algorithms. Pp. 115130 in W. R. Gilks, D. J. Spiegel-Halter and S. Richardson, eds. Practical Markov chain Monte Carlo. Chapman and Hall, London, U.K.
  • Rice, W. R. 1989. Analyzing tables of statistical tests. Evolution 43:223225.
  • Richard, P. 1980. Postglacial history of the vegetation, south of Lake Abitibi, Ontario and Quebec. Géog. Phys. Quatern. 34:7794.
  • di Rienzo, A., A. C. Peterson, J. C. Garza, A. M. Valdes, M. Slatkin, and N. B. Freimer. 1994. Mutational processes of simple-sequence repeat loci in human populations. Proc. Natl. Acad. Sci. USA 91:31663170.
  • Rosenberg, N. A. 2004. DISTRUCT: a program for the graphical display of population structure. Mol. Ecol. Notes 4:137138.
  • Rosenberg, M. S., and C. D. Anderson. 2011. PASSaGE: Pattern Analysis, Spatial Statistics and Geographic Exegesis. Version 2. Methods Ecol. Evol. 2:229232.
  • Sagarin, R. D., and S. D. Gaines. 2002. The ‘abundant centre’ distribution: to what extent is it a biogeographical rule? Ecol. Lett. 5:137147.
  • Sagarin, R. D., S. D. Gaines, and B. Gaylord. 2006. Moving beyond assumptions to understand abundance distributions across the ranges of species. Trends Ecol. Evol. 21:524530.
  • Saitou, N., and M. Nei. 1987. The neighbor-joining method: a new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4:406425.
  • Sexton, J. P., P. J. Mcintyre, A. L. Angert, and K. J. Rice. 2009. Evolution and ecology of species range limits. Annu. Rev. Ecol. Evol. Syst. 40:415436.
  • Storz, J. F., and M. A. Beaumont. 2002. Testing for genetic evidence of population expansion and contraction: an empirical analysis of microsatellite DNA variation using a hierarchical Bayesian model. Evolution 56:154166.
  • Templeton, A. R., and D. A. Levin. 1979. Evolutionary consequences of seed pools. Am. Nat. 114:232249.
  • Thomas, C. D., A. Cameron, R. E. Green, M. Bakkenes, L. J. Beaumont, Y. C. Collingham, et al. 2004. Extinction risk from climate change. Nature 427:145148.
  • Tollefsrud, M. M., J. H. Sønstebø, C. Brochmann, Ø. Johnsen, T. Skrøppa, and G. G. Vendramin. 2009. Combined analysis of nuclear and mitochondrial markers provide new insight into the genetic structure of North European Picea abies. Heredity 102:549562.
  • Weir, B. S., and C. C. Cockerham. 1984. Estimating F-statistics for the analysis of population structure. Evolution 38:13581370.
  • Yakimowski, S. B., and C. G. Eckert. 2008. Populations do not become less genetically diverse or more differentiated towards the northern limit of the geographical range in clonal vaccinium stamineum (Ericaceae). New Phytol. 180:534544.
  • Young, A., T. Boyle, and T. Brown. 1996. The population genetic consequences of habitat fragmentation for plants. Trends Ecol. Evol. 11:413418.

Supporting Information

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Biosketches
  9. Conflict of Interest
  10. References
  11. Supporting Information
FilenameFormatSizeDescription
ece3371-sup-0001-TableS1.docxWord document48KTable S1. Primer characteristics, frequencies of null alleles (r), and F-statistics over all populations at each locus. Fst was estimated by harboring and excluding the null allele (INA).
ece3371-sup-0002-TableS2.docxWord document48KTable S2. Allele frequencies and the number of alleles (N) by group.
ece3371-sup-0003-FigureS1.docxWord document48KFigure S1. Detection of the number of clusters, K, using STRUCTURE for eastern white cedar (Thuja occidentalis L.) populations according to Evanno et al. (2005).

Please note: Wiley Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.