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Ecological genetics of western white pine (Pinus monticola) from the
Lake Tahoe Basin
PATRICIA E. MALONEY1, DETLEV R. VOGLER2, ANDREW J. ECKERT3, CAMILLE E. JENSEN1,
ANNETTE DELFINO MIX2, AND DAVID B. NEALE4
1
Department of Plant Pathology and Tahoe Environmental Research Center, University of
California, Davis, California 95616 USA
2
USDA, Forest Service, Pacific Southwest Research Station, Institute of Forest Genetics,
Placerville, California 95667 USA
3
Department of Biology, Virginia Commonwealth University, Richmond, Virginia 23284
4
Department of Plant Sciences, University of California, Davis, California 95616
INTRODUCTION
An ecological genetic approach was used to determine genetic diversity and identify
adaptive phenotypic variation in Pinus monticola Dougl. populations in the Lake Tahoe Basin
(LTB). Forest tree species are currently challenged by climate change (Iverson and Prasad 2002,
Aitken et al. 2008, Alberto et al. 2013), so that knowledge of standing levels of genetic variation
and their relation to environmental gradients is needed to understand evolutionary responses by
natural populations (Anderson et al. 2012, Sork et al. 2013). Knowledge of genetic variation is
often based on range-wide studies (Rehfeldt et al. 1984, Rehfeldt et al. 1999, Howe et al. 2003,
St Clair et al. 2005, Bower and Aitken 2008, Richardson et al. 2009), yet conservation and
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management activities relying on this knowledge are implemented at local spatial scales. This
creates a fundamental gap between the source of scientific knowledge and the implementation of
conservation strategies. The LTB is characterized by heterogeneous environmental conditions
that occur over short physical distances (USDA NRCS 2007). For example, strong precipitation
gradients occur from west to east across the region; these are primarily driven by rain-shadow
effects due to the LTB being flanked to the west by the crest of the Sierra Nevada and to the east
by the Carson Range. Similarly, temperature conditions vary over short physical distances due to
differences in elevation, geography, vegetation structure, and other landscape features. Gradients
in soil properties also exist within the LTB, and abrupt changes in parent material are common
(USDA NRCS 2007), so that these soil properties interact with local climates to determine the
distribution of forest trees. Spatial heterogeneity in environmental variables, even over fine
spatial scales, often facilitates local adaptation (Savolainen et al. 2013, Richardson et al. 2014),
especially if selective pressures are strong relative to levels of gene flow or if gene flow is
constrained among populations − be it by distance or by forest structure (Dyer and Sork 2001,
Gram and Sork 2001, Lenormand 2002, Blanquart et al. 2012). Although an open question
remains as to the spatial scale at which local adaptation occurs for forest trees (Sork et al. 2013),
the standing level of genetic variation, regardless of it being identified as either neutral or
adaptive, is the raw material from which future responses to climate change will emerge (Hansen
et al. 2011, Anderson et al. 2012). Climate change is predicted to affect temperature and
precipitation gradients within the LTB, as well as in much of California (Lenihan et al. 2003).
Given that western white pine is close to its southern range limit, and the observation that edge
populations often harbor genetic diversity important for future responses to environmental
change (Hampe and Petit 2005, Kawecki 2008), baseline estimates of standing levels of genetic
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variation for fitness-related traits for this species are fundamental to understanding future
responses to climate change (e.g., as in Parisod and Joost 2010, Zardi et al. 2013).
Studies evaluating genetic variation within common gardens for fitness-related traits
provide quantifications of the evolutionary potential of populations (Brakefield 2003, Leinonen
et al. 2009), as they provide direct estimates of the relevant parameters describing this potential
(e.g., h2, QST, see Leinonen et al. 2013). Here, we use a common garden approach to evaluate
genetic variation for phenotypic traits related to growth, phenology, water-use efficiency, and
resource allocation for western white pine. Our objectives were to, (1) evaluate genetic variation
for phenotypic traits related to growth, phenology, water-use efficiency, and patterns of resource
allocation existing within and among populations of western white pine at a spatial scale of 1,300
km2, (2) determine the genetic structure and diversity of extant western white pine populations,
and (3) identify interactions between soil and climatic factors that influence standing levels of
genetic variation for western white pine.
MATERIAL AND METHODS
The LTB is located in the north-central Sierra Nevada at 120°W and 39°N within the
States of California and Nevada. It is flanked to the west by the Sierra Nevada crest and to the
east by the Carson Range, with elevations ranging from 1890 m to 3292 m. The climate is
Mediterranean and characterized by warm, dry summers, and cool, wet winters. Most
precipitation falls as snow between the months of November and April, with a strong gradient
from west to east. East-side locations are influenced by the rain-shadow effect of the Sierra
Nevada crest (Table 1). Geology of the region is dominated by igneous intrusive rocks, mainly
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granodiorite, and igneous extrusive rocks, typically andesitic lahar, with small amounts of
metamorphic rock (USDA NRCS 2007). Western white pine (P. monticola) grows in mixedspecies stands and is an important associate in upper montane forests at elevations ranging from
1,200 m to 2,500 m. It can also be found up to 3,300 m in elevation in some locations of the
Sierra Nevada (Kinloch and Scheuner 1990).
Population sampling and environmental data
During the summer of 2008, we selected 10 study locations, establishing three permanent
plots per location in which western white pine cones were collected. Each of the 10 locations
was in a distinct watershed and distributed around the LTB on lands managed by the USDA
Forest Service (Lake Tahoe Basin Management Unit, LTBMU) and in California and Nevada
State Parks to capture variation in the physical environment (Maloney et al. 2011, 2012). Seed
sources from 10 study populations for western white pine were used in the common garden.
Wind-pollinated seeds were collected from 158 parent trees from the10 populations of western
white (see Table 1). In each location, cones were collected from parent trees separated by 30 to
100 m. Source populations of western white pine were, on average, 14 km apart. In addition,
needle samples were taken from 25 adult western white pine individuals from each of the 10
populations in the Lake Tahoe Basin, with sample trees approximately 30 m apart. Needle
samples were stored in vials containing three 0.5 g desiccant packets. DNA was extracted in the
laboratory from 50 mg of tissue using DNeasy® Plant 96 Kits following the manufacturer’s
protocol (Qiagen, Valencia, CA, USA). DNA from sampled trees was genotyped for 160 single
nucleotide polymorphisms (SNPs), which were used as genetic markers. These SNPs were
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discovered through resequencing of a diversity panel comprising 12 haploid megagametophytes
collected across the natural range of sugar pine (Jermstad et al. 2011). All genotyping was
carried out at the DNA Technologies Core at the UC Davis Genome Center. Genotypes were
called at each SNP locus for each tree using BeadStudio (Illumina) software. Quality thresholds
for inclusion of SNPs were the same as those given in Eckert et al. (2010).
GPS coordinates (UTM: NAD27), slope (in degrees), aspect, and elevation were
determined and climatic parameters of annual precipitation (mm), January minimum (Tmin, oC),
July maximum (Tmax, oC), monthly and annual temperature, and May and August growing
degree days (above 5°C) from the period 1971-2000 were provided by FHTET (USDA FS Forest
Health Technology Enterprise Team, Fort Collins, CO) using the PRISM climatic model (Daly et
al. 1994, Table 1). Parent material and soil survey data consisting of available water capacity
(AWC, defined as the volume of water that would be available to plants if the soil were at field
capacity) at 25 (AWC 0-25) and 50 (AWC 0-50) cm depth, relative sand, silt, and clay content
(percentage of sand:silt:clay), cation exchange capacity (CEC = total amount of exchangeable
cations that can be held by the soil at a given pH), and water capacity at -1/3rd bar (WC -1/3rd bar
= matric potential at -1/3rd bar soil moisture condition, equivalent to field capacity) and at -15 bar
(WC -15 bar = matric potential at -15 bar soil moisture condition, equivalent to permanent
wilting point) were provided by the South Lake Tahoe office of the USDA Natural Resources
Conservation Service (USDA NRCS 2007). Percentage maximum solar radiation input, based on
latitude 40°N, was calculated using estimates of slope and aspect (Buffo et al. 1972).
Common garden experimentation and trait evaluation
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Western white pine seeds were soaked for 38 hours at 21°C in aerated water, put into
cold stratification at 2°C for 120 days, and then sown at the Institute of Forest Genetics (IFG)
greenhouse (Placerville CA, elevation: 849 m; 38°44' 23" N, 120°44'32" W) on 25 April 2010.
Seed were sown into individual Ray Leach Cone-tainer™ cells (volume 66 ml) in Premier Pro
Mix BX® (general purpose peat-based growing medium with vermiculite and perlite). All
families were sown together in racks. During August of 2010, seedlings were transferred
outdoors to the lathhouse. Seedlings were assigned to racks with 7 replicate seedlings per family
per row, and placed into 5 randomized blocks, for a total of ~35 test seedlings per family for
each of 158 parent trees for western white pine. Seedlings were hand-watered once or twice a
week depending on weather and were allowed to dry-down between irrigations. Winter irrigation
occurred when natural rain and snowfall were inadequate. Seedlings were fertilized (Dyna-Grow
10-5-5) in July and October of 2011.
Growth, phenology, water-use efficiency, and resource partitioning were measured when
western white pine seedlings were 2 years old. Height was recorded in October of 2011 and
October of 2012 for western white pine and bud flush (growth initiation) was observed in the
spring of 2011 and 2012. In December of 2012, 10 replicate seedlings per family (2 from each
block) were harvested at 32 months and the soil was carefully rinsed off of the roots. Shoot tissue
was clipped above the root collar and dry weights of shoots and roots were determined. Root and
shoot tissues were oven-dried at 80°C for 48-hours before weighing. In November of 2012 (31
months after sowing), needle tissue was processed for carbon isotope analysis (δ13C) and foliar
nitrogen (δ15N) from 5 replicate seedlings (1 from each block) per family. Ten to 20 needles
were harvested from each seedling and placed into a mortar with liquid nitrogen and coarsely
ground. Needle tissue was then transferred into 20-ml glass vials and oven-dried at 60°C for 96
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hours. Two to 3 mg of ground and dried needle tissue from each seedling were subsequently
placed into an individual well of a 96-well microplate for analysis. Samples were analyzed for
δ13C and δ15N at the Stable Isotope Facility at UC Davis
(http://stableisotopefacility.ucdavis.edu/). Data are presented as carbon isotope ratios for δ13C
(‰) and weight for δ15N (µg).
Linear mixed models were employed to estimate the within and among population
components of genetic variation for each phenotypic trait. Families and populations were
considered as random effects, while experimental conditions (i.e., block, interaction terms) were
considered as fixed effects. Estimation was carried out separately for each phenotypic trait using
restricted maximum likelihood estimation (REML) as employed in the lme4 library in R (R Core
Team 2013). Two forms of linear mixed models were fit to the data (e.g., Savolainen et al. 2004,
see Lynch and Walsh 1998). First, narrow-sense heritability (h2) was estimated separately for
each population (h2 = 4σ2FAM/σ2TOTAL), where the additive genetic variance for each phenotypic
trait was estimated as four times the variance component associated with maternal tree identifiers
(σ2FAM). Second, a global linear mixed model was used to estimate mean h2 (mean h2 =
4σ2(FAM)POP/(σ2(FAM)POP + σ2RES)), based on four times the variance component for maternal trees
nested in populations (σ2(FAM)POP) as well as the degree of among population differentiation for
each trait (QST). For the latter, we used the variance component associated with population
identifiers (σ2POP) to account for among population effects, where QST = σ2POP /(σ2POP +
8σ2(FAM)POP).
Confidence in estimates of within and among population effects was assessed using a
simulation approach (O’Hara and Merila 2005). Specifically, we simulated data (n = 10,000) for
each phenotypic trait conditioned on the REML estimates of model parameters described
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previously and estimated 95% confidence intervals as the 2.5% and 97.5% quantiles of this
distribution. Simulations were conducted using the SIMULATE function in the lme4 library of R.
Effects due to family, population, and experimental design, as well as soil type (granitic versus
non-granitic) and populations within soil type were explored using fixed effects analysis of
variance (ANOVA) as employed in PROC GLM ANOVA (version 9.2, SAS Institute 2011).
Correlations between maternal tree phenotypes and source environments were explored
separately for each species using redundancy analysis (RDA) as employed in the vegan library of
R. In general, RDA is a form of multivariate linear regression (Legendre and Legendre 2012). As
with all regression methods, results are sensitive to outliers, missing data, and unmeasured
confounding variables. Partial RDA (pRDA) works by assessing the fit of a multivariate
response matrix to a set of explanatory variables after removing the effects of a set of
conditioning variables. Here, we used the method of principal coordinates of neighbor matrices
(PCNM) to create a set of conditioning variables with which to remove the effects of
autocorrelation at multiple spatial scales (see Dray et al. 2006). Input to the PCNM analysis was
a matrix of pairwise geographical distances (km). Specifically, we used the set of maternal tree
phenotypes estimated from the linear mixed models described previously as the response matrix
and the environmental data represented as principal components (PCs) as the explanatory
variables after removing effects due to geography. Principal components analysis (PCA) was
used to remove multicollinearity among the predictor variables, which were centered and
standardized, prior to RDA. We performed PCA on climate data alone, soil data alone, and
climate and soil data combined, and then used all available PCs as predictors in each case.
Significance of RDA models, as well as axes and terms within each model, was assessed using a
permutation-based ANOVA approach with 99,999 permutations (Legendre et al. 2011).
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Univariate correlations were also explored using all pairwise correlations between environmental
PCs and phenotypic traits for each species.
Genetic structure and diversity
For each SNP, we calculated observed (HO) and expected (HE) heterozygosity, Wright’s
inbreeding coefficient (FIS) and hierarchical F-statistics among plots nested within populations.
Significance of mean FIS and of mean hierarchical fixation indices across SNPs was assessed
using 99% bootstrap confidence intervals (CIs) (n = 10,000 replicates sampled over for each
SNP). We examined allelic correlations (r2) between all possible pairs of SNPs to validate our
assumption that SNPs were unlinked. Analyses were conducted using the GENETICS and
HIERFSTAT
(Goudet 2005) packages in R (R Development Core Team 2013).
We assessed population structure using the approach outlined by Nicholson et al. (2002)
as implemented in the POPGEN library of R (R Core Development Team 2013). This approach
models a set of populations that diverge instantaneously from an ancestral population. Allele
frequencies in those diverging populations drift away from those in the ancestral population, and
are modeled using a binomial-beta hierarchical structure (Balding and Nichols 1995). Here, we
used the 10 sampled populations of sugar pine. There are three parameters of interest in this
model: αilj, the frequency in population i at locus l of allele j; πlj, the ancestral frequency of locus
l of allele j; and ci, the variance parameter for population i describing the magnitude of drift
away from ancestral allele frequencies across loci. Since the ci parameters are defined in terms of
variance in allele frequencies across loci, they are analogous to population-specific estimates of
multilocus FST. Thus, a larger value for an estimate of the ci parameter indicates more drift away
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from ancestral allele frequencies, meaning that current allele frequencies within population i
(αilj) are less similar, in terms of variance across loci, to that in the ancestral population (πlj). As
with FST, there are no natural breaks to define easily what is high and what is low. A
comprehensive review of FST estimates for conifers is given elsewhere (Ledig 1998, Savolainen
et al. 2007).
Parameters were estimated in a Bayesian framework using Markov chain Monte Carlo
(MCMC), where uniform priors were placed on the ci and πlj parameters and a MetropolisHastings algorithm was used to sample from the posterior distribution of each parameter. The αilj
parameters were integrated out of the likelihood function and thus were not sampled (cf.
Nicholson et al. 2002). Markov chains were sampled 10,000 times post burn-in (n = 10,000) to
estimate posterior distributions, with point estimates reported as posterior means. Convergence
was assessed by comparing posterior distributions of the ci parameters across two replicated runs
of the MCMC sampler, using Kolmogorov-Smirnov tests. Posterior distributions of the ci
parameters were visualized using violin plots, with density estimates given, assuming a Gaussian
distribution for the smoothing kernel.
RESULTS
Phenotypic variation within and among populations
Families (maternal trees) accounted for a significant portion of the observed phenotypic
variance (Table 2). As such, mean narrow-sense heritabilities (h2), estimated using global linear
mixed models, were moderate to high (h2 = 0.1160 – 0.5898) for most traits (Table 2, Figure 1).
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Bud flush and δ13C had the highest h2 values for western white pine (Table 2, Figure 1).
However, there was considerable variation among populations around the mean h2 for each
phenotypic trait (Figure 2). For example, population-level heritabilities for δ13C (mean h2 =
0.3314) ranged from 0.0 to greater than 1.0 across populations of western white pine.
Populations accounted for a statistically significant portion of the observed phenotypic
variance (Table 2). Western white pine had consistent levels of population differentiation for the
measured phenotypic traits, as five of six traits had statistically significant population effects,
with values for QST ranging from 0.0179 (bud flush in 2012) to 0.1724 (δ13C) (Table 2, Figure
2).
Environmental correlations with genetic variation
Standing levels of genetic variation were structured spatially for western white pine
(Table 3), with geographic variables (PCNM axes) accounting for 21.30% of the observed
genetic variance across all phenotypic traits. Both climate and soil variables (PCs) were also
important drivers (i.e., statistically significant) of genetic variation with climate variables
explaining 20.16% and soil variables explaining 13.53% of the observed genetic variance across
traits for western white pine (Table 3). When combined, climate and soil variables jointly
explained more than 26.71% of the phenotypic variance across traits for western white pine
(Table 3).
Environmental correlations with standing levels of genetic variation were apparent for
western white pine after correcting for spatial autocorrelation (Tables 3-4). For western white
pine, environmental PCs composed of soil and climate variables, conditioned on spatial effects,
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predicted upwards of 10% or more of the variance in multivariate phenotypes. In each case, the
first three RDA axes were statistically significant (P < 0.05) and comprised approximately 80%
or more of the total effect (Tables 3-4), with 12 of the 14 environmental PCs needed to capture
these effects (Table 4). The first RDA axis was dominated by height, bud flush in the first year,
δ13C, and δ15N (Figure 3), with environmental PC5 and PC7 driving most of the correlation
structure. Mixtures of soil and climate variables dominated these two environmental PCs, with
PC7 having a larger contribution from climate variables. In contrast, the second RDA for western
white pine was largely dominated by bud flush in the second year (Figure 3), with environmental
PC7, PC11, and PC14 being the most important contributors to this axis. As with RDA axis 1,
PC7 and PC11 were dominated by mixtures of climate and soil variables, although
environmental PC14 was almost exclusively determined by silt, sand, and clay percentages. The
third RDA axis for western white pine was dominated by δ13C and height, with environmental
PC8 driving most of the correlation structure. This environmental PC was loaded strongly by
maximum temperature in July, soil water capacity, and cation exchange capacity.
For western white pine, an effect of soil type was found for bud flush in 2011, bud flush
in 2012 root:shoot, and δ13C (Maloney et al. in review). Seedlings from non-granitic (e.g.,
volcanic) soil types had lower height growth, early bud flushing in 2011 and late bud flushing in
2012, lower root:shoot ratios, more negative δ13C, and lower δ15N, whereas seedlings from
granitic soil types had greater height growth, late bud flushing in 2011 and 2012, higher
root:shoot, less negative δ13C, and higher δ15N (Maloney et al. in review).
Genetic structure and diversity
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Genetic diversity measures (HO and HE) ranged from 0.231 to 0.259 and 0.245 to 0.272,
respectively (Table 5). Mean expected heterozygosity was higher in the LTB (mean HE = 0.261)
than what was reported by Kim et al. (2011) in a range-wide sampling of western white pine
(mean HE = 0.235). The inbreeding coefficient (FIS) ranged from 0.025 to 0.146; most values
reported here are within range of zero and appear to be in Hardy-Weinberg equilibrium (Table
5). The drift parameter (ci) ranged from 0.006 to 0.024, with the highest value of ci at Incline
Lake (0.024), followed by Meiss Meadow (0.022) (Table 5, Figure 4).
DISCUSSION
Estimates of quantitative genetic parameters, such as heritability and population differentiation,
are crucial to assess the ability of natural populations to respond to novel selection pressures
(Savolainen et al. 2013). Common gardens are a powerful approach to obtain these estimates for
forest trees, as evidenced by the long history of their use (Langlet 1971). Most studies for forest
trees, however, cover broad spatial scales on the order of entire species’ ranges (e.g., Rehfeldt et
al. 1984, Rehfeldt et al. 1999, Howe et al. 2003, St Clair et al. 2005, Bower and Aitken 2008,
Richardson et al. 2009). Applicability of results from these range-wide studies to land
management decisions, however, is debatable, as quantitative genetic parameters have temporal
and spatial contexts that may matter for management decisions occurring at more local spatial
scales. For example, range-wide estimates of heritabilities may provide useful baselines for
species (Bower and Aitken 2006), but the heritabilities within local populations matter with
respect to management of adaptive potential in the context of environmental change.
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Our ability to identify environmental correlates to genetic variation for fitness-related
traits is dependent upon spatial scale; thus, conclusions from range-wide studies may differ from
those at regional or local scales. For example, environmental variables such as soil
characteristics, which are often overlooked in genecological studies of forest trees (but see
Wright 2007), may be important drivers of standing patterns of genetic variation, yet may not be
identified in broad-scale studies.
In our study, measured phenotypic traits were often correlated with climate variables,
even when using a multivariate approach (Tables 3-4, Figure 3). Correlations are one type of
evidence for local adaptation (Endler 1986, Nuismer et al. 2010) and have been used extensively
in forest genetics to identify putatively adaptive traits (Neale and Kremer 2011). Multivariate
correlations to climate and soil were found for western white pine, after correcting for spatial
autocorrelation. However, use of PCNMs can reflect statistical approaches (i.e., correction for
spatial autocorrelation versus accounting for unmeasured environmental variation). For example,
Manel et al. (2010) used the leading PCNM vectors to account for unmeasured environmental
variation, and the lower PCNM vectors as corrections for spatial autocorrelation, with the
assumption that the leading PCNM vectors captured broad spatial scales unlikely to be
influenced by spatial autocorrelation due to restricted gene flow. Here, we instead used all
PCNM vectors to correct for spatial autocorrelation, because the argument of Manel et al. (2010)
should hold only for equilibrium situations, whereas complicated, non-equilibrium historical
demographic models can create very strong correlations between genetic structure and climate at
broad spatial scales (e.g., Holliday et al. 2010). Interpretations of our results, however, lead to
the same conclusion regardless of which approach we assume – genetic variation is consistently
correlated with both climate and soil variables. From a gene conservation perspective, genetic
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variation identified as historically adaptive should continue to be adaptive under changing
climate regimes, so that management decisions may benefit from this knowledge.
Our results indicate also that soil variables are as important as climate variables in
structuring genetic variation. This was shown by the similar amounts of genetic variation
explained by each variable type (Table 3), as well as by the significant effects of soil type on
traits such as bud flush and δ13C (Maloney et al. in review). Soil and climate variables, however,
change on disparate temporal scales, so that anthropogenic environmental change needs to be
modeled on the background of soil processes and characteristics (i.e., interactions between soil
and climate may change with a warming climate), especially since populations nested in soil type
were also significantly differentiated for many phenotypic traits (Maloney et al. in review).
Responses to novel selection pressures are proportional to the additive genetic variances
that exist for phenotypic traits (Savolainen et al. 2013), thus making estimates of narrow-sense
heritability (h2) central to understanding the evolutionary potential of species to ongoing
environmental change (Brakefield 2003, but see Hansen et al. 2011 for caveats). Western white
pine exhibited substantial segregating variation for each trait on average, with mean values of h2
often exceeding 0.20 and all of them statistically different from zero. Variation around these
mean values was large, with phenotypic traits having moderate to large mean heritabilities and
some traits exhibiting zero or near zero estimates in local populations (Figure 1). For example,
loss of four of the 10 populations of western white pine would remove a large portion of the
segregating genetic variation for δ13C within the LTB. Since land management in the LTB
occurs at the level of local populations, gene conservation strategies should emphasize the spatial
variation in heritability across populations. Results from studies such as ours are a first step
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towards providing this information and can be a baseline against which other systems (e.g., those
with differing patterns of environmental heterogeneity) can be compared.
At a regional level, among population components of genetic variation will also facilitate
evolutionary responses (Yeaman and Otto 2011, Blanquart et al. 2012). For example, the
previous discussion about the hypothetical loss of populations for western white pine could
change depending on which populations were lost relative to their genetic connectivity with
those that remain, thus highlighting the need to consider connectivity among populations with
respect to fitness-related genetic variation (Dyer and Nason 2004, Blanquart et al. 2012). This is
important because a significant portion of the genetic variance for δ13C resides among
populations of western white pine (QST = 0.1724). Across the Lake Tahoe Basin, we documented
significant among-population components of genetic variation. Populations accounted for a
statistically significant amount of phenotypic variance, even though all phenotypic traits had QST
values statistically greater than zero. Although QST being statistically greater than zero is not
evidence of local adaptation, comparison of western white pine FST (estimated from 160 singlenucleotide polymorphisms (SNPs) genotyped in the same source materials) revealed that QST
values were often greater than expected relative to neutral expectations (i.e., FST), using the
method of Whitlock and Guillaume (2009) (Figure 5). For western white pine, this included
values for bud flush in 2011 (P < 0.0001), root:shoot allocation (P < 0.0001), δ13C (P < 0.0001),
and δ15N (P < 0.0001; Figure 5). When combined with the environmental correlations observed,
populations of western white pine in the LTB are likely to be locally adapted. Gene conservation
strategies should recognize both within and among population patterns, especially since the most
structured variable for these two species, δ13C, is correlated with climate variables assumed to be
changing rapidly (e.g., growing degree days in August).
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Conclusions
We have shown that there is significant segregating genetic diversity for western white
pine within the 1,300 km2 of the LTB. As shown by Le Corre and Kremer (2003, 2012), it is this
segregating genetic diversity that can allow rapid responses to novel selection pressures through
re-assortment of adaptive alleles across loci determining a quantitative trait. For western white
pine this segregating genetic diversity appears to have been structured by natural selection in the
past; hence, if change continues to occur in the environmental variables that were important
selective forces in the past, it is reasonable to hypothesize that these traits will continue to be
important components of biotic responses to ongoing and future changes. Our results also
emphasize the importance of considering comprehensive environmental datasets including both
climate and soil when studying phenotypic variation and evolutionary potential, so that models
constructed to understand future responses use appropriate environmental predictors of fitnessrelated genetic variation. Our information provides a perspective on evolutionary potential in the
face of ongoing environmental change, indicating that a more spatially nuanced view is needed
for gene conservation activities within the LTB and potentially other regions with similarly
complex landscapes, steep environmental gradients, and existing genetic diversity.
ACKNOWLEDGEMENTS
The authors thank T. Burt and M. Frye for field, greenhouse, and laboratory assistance as well as
cone collections; W. Loftis (USDA NRCS, South Lake Tahoe) for invaluable soil survey data;
Anne Liston for TERC laboratory support; UC Davis Stat Lab for statistical consultation; and
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UC Davis SIF lab for stable isotope analysis. This work was supported by funds from Southern
Nevada Public Lands Management Act 10.
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28
TABLE 1. Environmental summaries for ten western white pine source populations (geographic
orientation relative to the Lake in parentheses) in the Lake Tahoe Basin.
Population (orientation)
n
Elev
Ann ppt
Tmin
Tmax
GDDMay
GDDAug
AWC50
sand
WC1/3
CEC
Soil type
Incline Lake (N)
24
2578
1394
-7.6
21.9
0
251
3.6
79.0
8.0
2.9
granite
Flume Trail (NE)
15
2414
797
-7.8
23.4
0
288
3.3
82.0
8.5
2.9
granite
Montreal Cyn (E)
19
2439
710
-6.8
24.3
9
311
6.4
35.0
18.0
22.5
metamorphic
Heavenly high (SE)
18
2503
815
-7.4
22.5
0
251
2.6
83.0
6.9
1.1
granite
Armstrong Pass (SE)
15
2675
1100
-9.0
21.6
0
207
2.7
91.0
9.8
2.9
granite
Meiss Meadow (S)
14
2687
1310
-8.3
21.3
0
214
2.9
66.0
7.3
12.5
andesite, tuff breccia
Echo Lake (SW)
7
2292
1292
-6.6
23.0
10
332
2.6
84.0
10.5
5.0
granite
Jakes Peak (SW)
14
2370
1218
-6.8
22.7
6
275
3.7
88.0
11.6
2.7
granite
Blackwood Cyn (W)
11
2155
1472
-6.7
23.1
4
265
3.8
62.0
8.3
21.0
tuff/lahar, volcanic
Mt Watson (NW)
21
2413
1035
-7.2
23.7
10
209
7.4
66.0
12.3
16.3
andesite
Notes: n, number of parent trees from each source population of common garden. Elev, elevation a.s.l.
(m); Ann ppt, total annual precipitation in mm; Tmin, minimum January temperature (°C); Tmax,
maximum July temperature (°C); GDDMay, growing-degree days in May above 5°C; GDDAug, growingdegree days in August above 5°C; AWC50, available water capacity in top 0-50 cm depth; sand,
percentage sand content; WC, soil water capacity at -1/3rd bar; CEC, cation exchange capacity.
29
TABLE 2. Results from the global linear mixed models used to estimate quantitative genetic
parameters for western white pine. Significance of model terms (σ2POP, σ2(FAM)POP) assuming
fixed effects (α = 0.05) is marked by an asterisk.
Phenotypic trait
Height
Bud flush1
Bud flush2
Root:shoot
δ13C
δ15N
Trait mean
(sd)
σ2POP
σ2(FAM)POP
σ2RES
9.35
(6.73)
132.00
(13.96)
134.00
(10.38)
1.69
(1.06)
-30.84
(0.95)
35.54
(10.98)
0.278*
2.355*
41.844
16.120*
24.680*
152.25
2.283*
15.613*
90.267
0.032*
0.033*
1.0945
0.105*
0.067*
0.7362
4.799*
7.564*
105.712
h2
(95% CI)
QST
(95% CI)
0.2132
(0.1397-0.2937)
0.5580
(0.4171-0.6944)
0.5898
(0.4595-0.7294)
0.1160
(0.0029-0.2459)
0.3314
(0.0749-0.6029)
0.2671
(0.0481-0.5162)
0.0145
(0.0000-0.0496)
0.0754
(0.0159-0.1709)
0.0179
(0.0000-0.0517)
0.1096
(0.0113-0.8498)
0.1724
(0.0316-0.4979)
0.0734
(0.0015-0.3498)
Notes: Height, height growth (mm), year 1 height minus year 2 height; Bud flush1 and Bud flush2, bud
flush in 2011 and 2012 (Julian day), first visible sign of new flushed needles that were green and emerged
from bud scales; δ13C, 13C (‰), carbon stable isotope ratio – a measure of water-use efficiency;
Root:shoot, root:shoot ratio (g g-1), ratio of dry weights after 2.5 years; δ15N, 15N (ug), foliar nitrogen
content.
30
TABLE 3. Results from redundancy analyses (RDA) where the response matrices were the
genetic components of the phenotypes and the explanatory variables were geography,
climate, and soil.
Summary
Western white pine
Geography
F-ratio (df1, df2)
P-value
R2
R2adj
7.7953 (5,144)
< 0.0001
0.2130
0.1857
F-ratio (df1, df2)
P-value
R2
R2adj
6.0173 (6, 143)
< 0.0001
0.2016
0.1681
F-ratio (df1, df2)
P-value
R2
R2adj
2.7570 (8, 141)
< 0.0001
0.1353
0.0862
F-ratio (df1, df2)
P-value
R2
R2adj
(Climate & Soil)|Geography
F-ratio (df1, df2)
P-value
R2
2
R adj
3.5143 (14, 135)
< 0.0001
0.2671
0.1911
Climate
Soil
Climate & Soil
1.4398 (14, 130)
0.0244
0.1056
0.0334
31
TABLE 4. Results of variable selection to produce final pRDA models including effects of both
climate and soil variables for only significant pRDA models from Table 3.
Species
Western white pine
PCs
Axes (rPVE)a
R2 (R2adj)
F (df1, df2)
P-value
12
3 (0.7963)
0.0919 (0.0297)
1.4550 (12, 132)
0.0285
Notes: arPVE, relative percent variance explained refers to the relative amount of variance accounted for
by the full pRDA model using the number of significant axes listed in the column. The total amount of
variance explained by the full pRDA model is the R2.
32
TABLE 5. Genetic summaries for P. monticola from the Lake Tahoe Basin. Genetic and diversity
patterns from 180 marker loci with mean and variance (in parentheses) for HO, HE, FIS, ci,
with 95% credible intervals for ci (in parentheses).
Population
Incline Lake
Flume Trail
Montreal Cyn
Heavenly
Armstrong Pass
Meiss Meadow
Echo Lake
Jakes Peak
Blackwood Cyn
Mt Watson
HO
HE
FIS
ci
0.244
0.247
0.025
0.0244
(0.038)
(0.031)
(0.048)
(0.0149-0.0362)
0.253
0.262
0.037
0.0185
(0.039)
(0.032)
(0.055)
(0.0096-0.0296)
0.238
0.257
0.075
0.0066
(0.034)
(0.031)
(0.066)
(0.0008-0.0144)
0.235
0.267
0.109
0.0062
(0.030)
(0.030)
(0.070)
(0.0009-0.0138)
0.243
0.259
0.067
0.0120
(0.036)
(0.031)
(0.058)
(0.0049-0.0210)
0.231
0.245
0.066
0.0216
(0.034)
(0.031)
(0.061)
(0.0124-0.0332)
0.259
0.266
0.049
0.0166
(0.041)
(0.030)
(0.067)
(0.0088-0.0260)
0.252
0.272
0.112
0.0115
(0.035)
(0.026)
(0.094)
(0.0053-0.0189)
0.244
0.272
0.146
0.0178
(0.038)
(0.031)
(0.091)
(0.009-0.272)
0.253
0.262
0.050
0.0090
(0.036)
(0.029)
(0.068)
(0.0024-0.0176)
33
Figure legends
FIGURE 1. Narrow-sense heritabilities (h2) varied by trait, species, and population. Mean
heritabilities from the global linear mixed models are given as vertical black lines with their 95%
confidence intervals shown in gray. Points give population-specific heritabilities (± 1 standard
deviation). Values for bud flush for western white pine include both 2011 and 2012 plotted
sequentially by population (2011 on bottom and 2012 on top for each population). Pial, Pinus
albicaulis (whitebark pine); Pila, P. lambertiana (sugar pine); Pimo, P. monticola (western white
pine). Figure from Maloney et al. (in review) with western white pine highlighted in yellow.
FIGURE 2. Phenotypic traits varied by population (points) and species (vertical black line). Gray
areas denote 95% confidence intervals and population means are ± 1 standard deviation. Values
for bud flush for western white pine include both 2011 and 2012 plotted sequentially by
population (2011 on bottom and 2012 on top for each population). Pial, Pinus albicaulis
(whitebark pine); Pila, P. lambertiana (sugar pine); Pimo, P. monticola (western white pine).
Figure from Maloney et al. (in review) with western white pine highlighted in yellow.
FIGURE 3. Summaries of the loadings by climate and soil variables onto environmental principal
components (PCs) used in the partial Redundancy Analysis (pRDA).
34
FIGURE 4. Posterior distributions are plotted for parameter ci. The larger the value of ci the more a
population has drifted (i.e. diverged) from a set of ancestral allele frequencies as estimated from
the data. Posterior means are given by the black points.
FIGURE 5. Simulated null distributions (n = 100,000 simulations) for the difference between QST
and FST for western white pine.
35
Figure 1.
36
Figure 2.
37
Figure 3.
38
Figure 4.
Population
Names
Population
names
0.04
0.03
0.02
0.01
0.00
Population Specific Drift Parameter (ci)
0.05
Blackwood Cyn 6: 6:
Heavenly
Valley
1:1:Blackwood
Heavenly
2: Flume Trail
7: Montreal Cyn
Canyon
2:3:Flume
Incline Lake
8: 7:
MtMontreal
Watson
Echo Lake
Armstrong
Pass
Mt. Watson
3:4:Incline
Lake 9: 8:
Jakes Peak
10: Meiss Meadow
9: Armstrong Pass
4:5:Echo
10: Meiss Medow
5: Jakes
1
2
3
4
5
6
7
Population Identifier
8
9
10
39
Figure 5.