American Journal of Botany 95(1): 66–76. 2008.
Movement of seeds from their collection site to other environ-
ments within a species range for reforestation or restoration may
increase the risk of maladaptation ( Campbell, 1979 ). Reduced
growth or mortality resulting from maladaptation could reduce
the success of restoration projects, and gene fl ow from maladapted
planted trees into adjacent native populations could negatively
affect adaptation to local conditions ( McKay et al., 2005 ). How-
ever, the planting of individuals adapted to new environmental
conditions, e.g., a warmer climate, could be a method to facilitate
migration and provide a source of genotypes well adapted to local
populations. Seed transfer should be guided by natural levels of
genetic variation and local adaptation in quantitative traits spe-
cifi c to the species in question ( Morgenstern, 1996 ; Hufford and
Mazer, 2003 ; McKay et al., 2005 ). Understanding genetic struc-
ture is also necessary for managing breeding programs, evaluat-
ing conservation of genetic resources, and predicting the possible
effects of climate change ( St. Clair et al., 2005 ).
The ranges of many trees species are predicted to shift higher in
latitude and elevation as a result of climate change ( Davis and
Shaw, 2001 ; Hamann and Wang, 2006 ). However, at a local scale,
projected vegetation responses include a combination of eleva-
tional, aspect, and microsite adjustments because the location of
suitable conditions for each taxon shifts within a region ( Bartlein
et al., 1997 ). The potential impacts of predicted warming under-
score the importance of understanding genetic structure and adap-
tation of populations to their local environment. For species
threatened by pests and diseases in addition to climate change,
minimizing maladaptation may mean the difference between es-
tablishing or maintaining viable populations and local extirpation.
Whitebark pine ( Pinus albicaulis Engelm., Pinaceae) is a
high elevation, fi ve-needle pine, and the only North American
member of the stone pines ( Pinus subsection Cembrae ) ( Arno
and Hoff, 1989 ; Price et al., 1998 ; but see Gernandt et al., 2005 ).
Although of little commercial value, it has tremendous ecologi-
cal value and is considered a keystone species ( Tomback et al.,
2001 ). The large, wingless seeds of whitebark pine are an im-
portant food source for the Clark ’ s nutcracker ( Nucifragia co-
lumbiana Wilson), which is its primary dispersal agent and
mutualist ( Tomback, 1978 ; Hutchins and Lanner, 1982 ; Lanner,
1982 ; Tomback, 1982 ). However, whitebark pine is in decline
throughout most of its range from a synergism of natural
and human-driven causes. Outbreaks of mountain pine beetle
( Dendroctonus ponderosae Hopkins) and decades of fi re sup-
pression have led to mortality and successional replacement by
shade-tolerant species. However, the greatest agent driving the
current decline is the introduced disease white pine blister rust
(caused by the fungus Cronartium ribicola J. C. Fisch. ex
Rabh.). Scientists agree that whitebark pine ecosystems require
immediate restoration to reduce the effects of fi re exclusion and
blister rust ( McCool and Freimund, 2001 ). Silvicultural tech-
niques can be used to encourage natural regeneration, but in
stands with a compromised seed source or those that need to be
regenerated quickly, planting seedlings (if available) is the sug-
gested restoration practice ( Hoff et al., 2001 ), using blister-rust-
resistant seedlings when they are available. There is a widespread
need for restoration and often a limited supply of seed for white-
bark pine, thus geographic guidelines on seed transfer are
needed for restoration and conservation of this species.
1 Manuscript received 21 September 2006; revision accepted 8 November
The authors thank the USDA Forest Service regions one, fi ve, and six;
the British Columbia Ministry of Forests; E. C. Manning and Tweedsmuir
Provincial Parks of British Columbia; and B. Brett of Snowline Ecological
Consulting, Whistler, B.C. for seed. Many people provided assistance to
this project, including D. Kolotelo, J. Tuytel, C. Chourmouzis, D. Watson,
K. Keir, M. Harrison, D. Szohner, P. Smets, J. Krakowski, S, Trehearne,
and all of the members of the Aitken laboratory at UBC. Climate data were
provided by Drs. T. Wang and G. Rehfeldt. Funding for this study came
from the British Columbia Forestry Investment Account through the Forest
Genetics Council of B.C. to the Centre for Forest Conservation Genetics
at UBC. Thank you to Drs. A. Yanchuk, M. Whitlock, J. Whitton, Y. El-
Kassaby, S. Graham, D. Tomback, B. St. Clair, and an anonymous reviewer
for their helpful comments on earlier drafts of this manuscript.
2 Author for correspondence (e-mail: firstname.lastname@example.org)
ECOLOGICAL GENETICS AND SEED TRANSFER GUIDELINES FOR
PINUS ALBICAULIS (PINACEAE) 1
ANDREW D. BOWER 2 AND SALLY N. AITKEN
Centre for Forest Conservation Genetics, Department of Forest Sciences, University of British Columbia, 3401-2424 Main Mall,
Vancouver, British Columbia V6T 1Z4 Canada
Whitebark pine ( Pinus albicaulis Engelm.) has greatly declined throughout its range as a result of introduced disease, fi re sup-
pression, and other factors, and climate change is predicted to accelerate this decline. Restoration is needed; however, no informa-
tion regarding the degree of local adaptation is available to guide these efforts. A seedling common-garden experiment was
employed to assess genetic diversity and geographic differentiation ( Q ST ) of whitebark pine for traits involved in growth and ad-
aptation to cold and to determine climatic variables revealing local adaptation. Seedlings from 48 populations were grown for two
years and measured for height increment, biomass, root to shoot ratio, date of needle fl ush, fall and spring cold injury, and survival.
Signifi cant variation was observed among populations for most traits. The Q ST was low (0.07 – 0.14) for growth traits and moderate
(0.36 – 0.47) for cold adaptation related traits, but varied by region. Cold adaptation traits were strongly correlated with mean
temperature of the coldest month of population origins, while growth traits were generally correlated with growing season length.
We recommend that seed transfer for restoration favor seed movement from milder to colder climates to a maximum of 1.9 ° C in
mean annual temperature in the northern portion of the species range, and 1.0 ° C in the U. S. Rocky Mountains to avoid maladapta-
tion to current conditions yet facilitate adaptation to future climates.
Key words: genetic variation; geographic differentiation; local adaptation; Pinus albicaulis ; quantitative traits; seed transfer;
whitebark pine; white pine blister rust.
BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
eight replications had ambient soil temperature (ambient treatment) and the re-
maining four replications (cold treatment) had cooled water pumped through
hoses buried approximately 25 cm below the surface, which kept soil tempera-
ture consistently ~8 ° C cooler during the warmest part of the day. Populations
that were represented in fewer than half of the replications (i.e., < 4 in the ambi-
ent or < 2 in the cold treatment) because of mortality were excluded from the
analysis. The fi nal data set included 40 populations in the ambient treatment
and 37 in the cold treatment, with 33 populations common to both treatments
( Table 2 ). The AlphaPlus program ( Mann, 1996 ) was used to design the plant-
ing layout and assign seedlings randomly within replications. Seedlings were
planted at 9.5 × 10 cm spacing, with one row of buffer trees surrounding each
raised nursery bed for which data were not collected. They were kept well wa-
tered and were fertilized and weeded as needed to provide conditions optimal
for growth for most temperate conifers. Timing of initiation of growth in the
spring was observed for 2003 and 2004, and at the end of the 2004 growing
season, survival, height growth, aboveground and belowground oven-dry bio-
mass of seedlings were measured on all replications. Artifi cial freeze testing
was performed on 5-mm needle segments from all seedlings in the ambient
treatment in three replications in the fall of 2003 and four replications in the
spring of 2004. The electrolyte leakage method was used to quantify cold in-
jury. Details of cold hardiness testing are given in Bower and Aitken (2006) .
The fi nal data set contained 10 quantitative variables; data from all trees in
both temperature treatments were available for third-year height increment,
root biomass, shoot biomass, total biomass, root to shoot ratio, date of needle
fl ush in 2003 and 2004 ( Table 3 ). Measurements of fall and spring cold injury
were available from the ambient treatment only. In addition, the percentage
survival in each soil temperature treatment was tested for treatment effects.
Data analysis — SAS version 8 ( SAS Institute, 1999 ) was used for all statisti-
cal analyses. Preliminary analysis showed an increase in variability of residuals
with an increase in predicted values, so a natural-log transformation was ap-
plied to height increment, root, shoot, and total biomass, and root to shoot ratio
for all analyses, which helped to equalize variance. For testing for differences
between soil temperature treatments and genotype-by-environment interactions
in the quantitative traits, PROC MIXED was used with the following model for
populations included in both treatments:
y ijklmn = µ + t i + r ( t ) ij + b ( rt ) ijk + p l + pt il + pr ( t ) ijl + f ( p ) l m + e ijklmn , (Eq. 1)
where y ijklmn is the observed value for tree n in family m w ithin population l
in incomplete block k in rep j in soil temperature i , µ is the overall mean, t i is
the effect of temperature i, r ( t ) ij is the effect of rep j nested within temperature
i , b ( rt ) ijk is the effect of incomplete block k nested within rep j within tem-
perature i, p l is the effect of population l , pt il is the interaction of temperature
i and population l , pr ( t ) ijl is the interaction of population l and rep j within
temperature i , f ( p ) lm is the effect of family m nested within population l , and
e ijlkmn Temperature, population, and population-by-temperature interaction
were considered fi xed, while all other effects were considered random. The
same model was used to analyze each geographic region separately. Population
Genetic variation and population differentiation have been
assessed in whitebark pine using molecular markers and mono-
terpenes ( Yandell, 1992 ; Jorgensen and Hamrick, 1997 ; Brued-
erle et al., 1998 ; Stuart-Smith, 1998 ; Rogers et al., 1999 ;
Krakowski et al., 2003 ), and results have indicated average to
above average expected heterozygosity compared to other pines
(average H e of 0.16 for whitebark pine vs. 0.13 – 0.16 for pines
in general [Ledig, 1998]). Population differentiation in white-
bark pine was reported to be low to moderate in all studies ( F ST
or G ST < 0.09) ( Table 1 ), with signifi cant evidence of inbreed-
ing ( F is signifi cantly greater than zero). Populations in the
northern (western British Columbia), eastern (Rocky Moun-
tains), and southern regions of the species range (California and
Oregon) are differentiated for monoterpenes ( Zavarin et al.,
1991 ), isozymes ( Yandell, 1992 ) and organelle DNA ( Richard-
son et al., 2002b ). However, levels of genetic variation and
population differentiation in phenotypic traits potentially in-
volved in local adaptation in whitebark pine have not previ-
ously been determined.
In this study, we analyze geographic variation and genetic
differentiation in phenotypic seedling traits in a common-gar-
den experiment in whitebark pine and evaluate degree of local
adaptation to climate for the purpose of developing seed trans-
fer recommendations and predicting the ability of whitebark
pine to adapt to climate change.
MATERIALS AND METHODS
Sample materials — Open-pollinated seeds from 48 populations of white-
bark pine from across most of the species range ( Table 2, Fig. 1 ) were germi-
nated in 2002 following seed stratifi cation using the protocol described by Burr
et al. (2001) . Germinants were sown into individual 10 in 3 (164 cm 3 ) Ray Leach
Cone-tainer super cells (Stuewe and Sons, Corvallis, Oregon, USA) for their
fi rst growing season. In November 2002, 10-mo-old seedlings were trans-
planted into a raised nursery bed common garden in Vancouver, British Colum-
bia (49 ° 13 ’ N, 123 ° 6 ’ W) and grown for two growing seasons. Seedlings were
planted in an incomplete block alpha design ( Patterson and Williams, 1976 )
with 12 replications, and 10 four-tree by four-tree incomplete blocks within
replications. Each replication contained 160 test trees, with populations repre-
sented by 1 – 18 families (mean 7.9, SE 0.37), with each family usually repre-
sented once per replication. Because temperatures in Vancouver are higher than
those in the native environment, two temperature treatments were imposed:
TABLE 1. Reported values of genetic differentiation for whitebark pine ( Pinus albicaulis) and other stone pine ( Pinus subsection Cembrae ) species.
F ST or G ST
BC, ID, MT, OR
USA rangewide and northern AB
USA Great Basin
A. Bower unpublished manuscript
Jorgensen and Hamrick 1997
Krakowski et al. 2003
Richardson et al. 2002b
Russian far east
Kamchatka penn., Russia
Alps and eastern Carpathians, Ukraine
Goncharenko et al. 1993b
Krutovskii et al. 1995
Potenko and Velikov 2001
Potenko and Velikov 1998
Krutovskii et al. 1995
Goncharenko et al. 1993a
Krutovskii et al. 1995
Tani et al. 1996
Belokon et al. 2005
Notes: AB = Alberta, BC = British Columbia, Canada; ID = Idaho, MT = Montana, USA.
a ! ST from cpDNA microsatellite data
AMERICAN JOURNAL OF BOTANY
genetic variance. In this study the variance component for population ( " 2 p ) was
used as the among-population variance, and three times the variance compo-
nent for family within-population (3 " 2 f ( p ) ) was used as the within-population
variance. The within-population genetic variation was approximated as three
times the family variance instead of four as is used for true half-sibs, because
open-pollinated progeny of whitebark pine are more closely related than half-
sibs due to moderate inbreeding and correlated paternity ( Squillace, 1974 ; Kra-
kowski et al., 2003 ; Bower and Aitken, 2007 ). Values of Q ST were compared to
all published estimates for whitebark pine for genetic markers ( F ST or G ST ).
Climatic variables used in the analyses were mean annual tempera-
ture, mean temperature of the coldest month, mean temperature of the
warmest month, mean annual precipitation, mean summer precipitation, annual
heat : moisture index, summer heat : moisture index, and frost-free period. Cli-
matic variables for populations north of 48 ° N were obtained from PRISM cli-
matic data corrected for local elevation using the Climate BC model described
by Wang et al. (2006a) . For populations south of 48 ° N, climatic data were
means were used to test for differences between treatments for survival percent-
age using the above model with only the temperature and population effects,
and their interaction.
To test differences among populations within each soil temperature, PROC
MIXED was used with the REML variance component estimator and the fol-
y ijklm = µ + r i + b ( r ) ij + p k + rp ik + f ( p ) kl + e ijklm , (Eq. 2)
where terms for each effect are the same as in Eq. 1 without the effect of soil
temperature. All terms were considered random except for population, which
was fi xed. To obtain estimates of variance components, the analysis was re-
peated with all effects considered random.
Genetic differentiation among populations was estimated for all quantitative
traits by calculation of Q ST ( Spitze, 1993 ): Q ST = " 2 b / ( " 2 b + 2 " 2 w ), where " 2 b is
the among-population variance and " 2 w is the within-population additive
TABLE 2. Pinus albicaulis populations, number of seedlings tested, geographic and climatic data.
Site no. RegionName
Lat. o NLong. o WElev. (m) MAT ( ° C)MTWM ( ° C)MTCM ( ° C) FFP (d)SH:M Ambient Cold
Notes: Region: N = northern, R = Rocky Mountain, S = southern; Lat. = latitude, Long. = longitude, Elev. = elevation, See Table 3 for abbreviations and
explanation of variables.
BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
treatment had fewer replications, lack of cold injury testing, and the absence of
a few key populations at the northern and southern ends of the range compared
to the ambient treatment, thus only data from the ambient treatment were used
in the canonical correlation analysis. Climatic data and least-squares population
means for each seedling phenotypic trait demonstrating signifi cant ( P # 0.05)
population differentiation were included in this analysis. Canonical redundancy
analysis was used to determine the proportion of variation in phenotypic traits
accounted for by canonical correlations with the climatic or geographic data
sets. To assess potential differences in relationships between seedling pheno-
typic traits and climatic variables between the two soil temperature treatments,
canonical correlation analysis was repeated for the two treatments separately
using only the populations common to both.
To develop predictive equations for the construction of seed transfer guide-
lines, values of signifi cant canonical variables for the seedling phenotypic traits
were regressed on the standardized climatic variable with the highest loading
for that canonical variable. The slope of this regression estimates the rate of
change in the phenotypic canonical variable relative to the selected climatic
variable. Rates of differentiation along climatic gradients were interpreted rela-
tive to the least signifi cant difference among populations at the 20% level (least
signifi cant difference: LSD 0.2). This conservatively reduces type II error (ac-
cepting no differences among populations when differences actually exist) and
minimizes maladaptation risk accordingly ( Rehfeldt, 1991 ). Values of LSD for
the phenotypic canonical variables were obtained from a Duncan ’ s multiple
range test in PROC GLM using the model for testing variation among popula-
tions described. The fl oating seed transfer model developed by Rehfeldt (1991 ,
1994 ) was used to determine seed transfer guidelines for restoration programs
of whitebark pine. The maximum recommended environmental transfer dis-
tance between seed collection population and planting site was calculated as the
difference in the standardized climate variable associated with the LSD ( P =
0.20) value of the phenotypic canonical variable multiplied by the standard
deviation of the climate variable. Univariate regressions of climate variables on
latitude, longitude, and elevation were used to determine the geographic dis-
tances associated with the rates of differentiation in climate variables to make
simple seed transfer recommendations.
Soil temperature effects — Height increment and survival were
signifi cantly greater, on average, in the cold treatment than in
the ambient treatment (least squares mean = 6.7 and 8.9 mm,
P = 0.04 for height increment and 66.9 and 82.3%, P < 0.001
for survival, in the ambient and cold treatment respectively).
Means for biomass traits were also greater in the cold treatment,
and the temperature treatment difference greater for root mass
than shoot mass, although the difference between treatments for
these traits was not signifi cant. The date of needle fl ush did not
differ signifi cantly between treatments. Population-by-treat-
ment interaction was not signifi cant for any of the traits. The
foliage of seedlings in the cold temperature treatments gener-
ally appeared darker green and healthier than those in the ambi-
ent treatment. No treatment-specifi c geographic trends were
evident, and separate canonical correlation analyses of individ-
ual treatments yielded the same results.
Geographic patterns across the species range — In general,
seedlings from populations originating from colder climates
had less overall growth, earlier needle fl ush in spring, and less
cold injury in fall than seedlings originating from milder cli-
mates when grown in the common garden. Populations differed
signifi cantly in the ambient soil temperature treatment for all
variables except root : shoot ratio and spring cold injury ( Table
5 ). Despite a lack of signifi cant differences among populations
in the ANOVA, root : shoot ratio differed signifi cantly among
populations in a Duncan ’ s multiple range test.
Growth-related traits generally had low levels of population
differentiation (0 # Q ST # 0.14), while the cold-adaptation re-
lated traits (date of needle fl ush and fall cold injury) showed
obtained from a model using the thin plate splines of Hutchinson (2000) as il-
lustrated for North America by McKinney et al. (2001) . Clines in quantitative
traits can be obscured when there are correlations among traits or if geographi-
cal structure is complex. In these cases, canonical correlation analysis is more
effi cient than regressing each trait on environmental variables separately ( West-
fall, 1992 ). Several of the seedling phenotypic traits and climatic or geographic
variables were strongly intercorrelated ( Table 4 ), so canonical correlation anal-
ysis was used to examine the relationships among these variables. The cold
Fig. 1. Distribution of Pinus albicaulis and locations of populations
tested in common-garden experiment. Dashed lines separate the southern,
Rocky Mountain and northern regions.
TABLE 3. Description of (A) quantitative and (B) climatic variables.
A) Quantitative traitAbbreviationUnit
3rd year height increment
Total dry biomass
Root dry biomass
Shoot dry biomass
Root : shoot ratio
2003 Date of needle fl ush
2004 Date of needle fl ush
Fall cold injury
Spring cold injury
days from Jan. 1
days from Jan. 1
index of injury (%)
index of injury (%)
B) Climatic variable
Mean annual temperature
Mean temperature, warmest month
Mean temperature, coldest month
Mean annual precipitation
Mean summer precipitation
Annual heat : moisture index
Summer heat : moisture index
[(MAT + 10)/(MAP/1000)]
AMERICAN JOURNAL OF BOTANY
nifi cant ( P = 0.006) and accounted for an additional 15% of the
variation. The second pair of variables demonstrates the posi-
tive relationship between the length of the frost-free period and
growth, both height and biomass ( Table 6 ). The regression of
the second phenotypic canonical score on frost-free period was
also signifi cant ( P = 0.001) but weak ( r 2 = 0.24). Canonical re-
dundancy analysis showed that the fi rst two climatic canonical
variables account for 24 and 17% (41% total) of the variation in
population phenotypic trait means, indicating substantial ge-
netic structure along climatic gradients.
Regional patterns of variation — When populations were
analyzed separately by region, some broad-scale geographic
differences in patterns of population differentiation emerged
( Table 7 ). In the ambient soil temperature treatment, in the
northern region, signifi cant differences were detected among
populations for all three biomass traits. In the Rocky Mountain
region, only date of needle fl ush in 2004 varied signifi cantly
among populations, while in the southern region, only date of
moderate to strong differentiation among populations regard-
less of treatment (0.36 # Q ST # 0.65). A comparison of Q ST
values with previously published values of F ST for whitebark
pine ( Table 1 ) shows that the phenotypic traits with the weakest
differentiation are similar to the highest estimates of differen-
tiation in presumably neutral molecular markers from rangewide
studies ( Jorgensen and Hamrick, 1997 ; Richardson et al.,
2002b ), and the quantitative traits with the strongest differentia-
tion have substantially higher Q ST estimates.
In the canonical correlation analysis of population means for
seedling phenotypic traits and climatic variables for population
origins, the fi rst canonical correlation was signifi cantly differ-
ent from zero ( P < 0.0001) and explained 72% of the variance
in the data. The fi rst pair of canonical variables summarizes
relationships between cold-related phenotypic traits and mean
temperature of the coldest month ( Table 6 ). Mean temperature
of the coldest month explained a substantial proportion of the
variance in the fi rst phenotypic canonical score ( r 2 = 0.79, P <
0.0001, Fig. 2 ). The second canonical correlation was also sig-
TABLE 4. Correlations among population means for quantitative, climatic, and geographic variables. See Table 3 for explanation of variables.
A) Northern region
Variable HTINC a TDM a RM a SM a FL03FL04 FCISCI Survival Lat. Long. Elev.
− 0.81* 0.86*
B) Rocky Mountain region
Variable HTINC a TDM a RM a SM a FL03 FL04 FCI SCI SurvivalLat. Long. Elev.
− 0.61* 0.66*
C) Southern region
VariableHTINC a TDM a RM a SM a FL03 FL04 FCISCISurvival Lat.Long.Elev.
− 0.96* 0.67*
a Natural log transformed
* Signifi cant at $ = 0.05
BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
Scores for the fi rst pair of canonical variables from the south-
ern (Oregon and California) populations were clearly separated
from the Rocky Mountain and Canadian populations ( Fig. 3 ).
The southern populations had a large infl uence on the relation-
ship between the fi rst phenotypic canonical score and mean
temperature of the coldest month ( Fig. 2 ). Regressions con-
ducted within each region separately revealed a signifi cant rela-
tionship between the fi rst phenotypic canonical score and mean
temperature of the coldest month in both the northern region
( r 2 = 0.41, P = 0.05) and the Rocky Mountain region ( r 2 = 0.32,
P = 0.01), but not in the southern region ( r 2 = 0.08, P = 0.38).
The relationship between the second phenotypic canonical
score (largely representing growth traits) and frost-free period
was only signifi cant in the Rocky Mountain region ( P =
Limits to seed transfer — The interval in mean temperature of
the coldest month associated with a signifi cant difference in the
fi rst quantitative canonical variable (which largely refl ects date
of needle fl ush and fall cold injury) over all regions was esti-
mated as 1.1 ° C, which translates to a geographic distance
of approximately 2.8 ° latitude or 300 kilometers ( r 2 = 0.49,
needle fl ush (both years) varied signifi cantly among popula-
tions. In the cold treatment, height increment differed signifi -
cantly among populations in the northern region; all traits
except root : shoot ratio differed in the Rocky Mountain region;
and date of needle fl ush differed among populations in the
southern region. Estimates of population differentiation ( Q ST )
were lower, on average, within regions than those for all popu-
lations pooled across region ( Table 7 ). In the ambient treatment,
the northern region had higher estimates of Q ST, on average,
than other regions, while in the cold treatment, estimates of
population differentiation were highest for the Rocky Mountain
Correlations among quantitative traits and climatic variables
also varied by region ( Table 4 ). In the northern region, only the
date of needle fl ush had clinal variation that was positively as-
sociated with frost-free period. In the Rocky Mountain region,
growth traits (height growth and biomass) and spring cold in-
jury were positively associated with temperature variables. In
the southern region, survival was correlated positively with
summer precipitation and negatively with summer aridity in-
dex, and date of needle fl ush was positively correlated with
TABLE 5. Signifi cance level of population and family within-population effect in ANOVA, over all populations and by region, in two soil temperature
Variable a Population F Family in-population Z †
Population F by region
Northern Rocky Mtn.Southern
a A = ambient soil temperature treatment, C = cold soil temperature treatment, see Table 3 for explanation of variables
b Natural log transformed
* Signifi cant at $ = 0.05
** Signifi cant at $ = 0.01
† Wald test of covariance parameter = estimate/approximate standard error ( SAS Institute, 1999 )
TABLE 6. Correlations between quantitative canonical variables (Quant1 and Quant2) and the quantitative variables, and between climate canonical
variables (Clim1 and Clim2) and both climate and quantitative variables.
Variable a Quant1Quant2 VariableClim1Clim2 VariableClim1Clim2
a See Table 3 for explanation of variables.
b Natural log transformed
AMERICAN JOURNAL OF BOTANY
able seed prevented the replication of this experiment in differ-
ent environments, so the two temperature treatments were
intended to assess potential levels of genotype-by-environment
interaction. The common-garden environment (at Vancouver,
British Columbia; elevation ~100 m, MAT = 10 ° C, MTWM =
17.3 ° C, MTCM = 3.2 ° C) is considerably warmer than white-
bark pine ’ s native habitat, where frosts can occur during any
month of the year ( Arno and Hoff, 1989) ( Table 2 ). Although
milder air temperatures and warmer soil would enhance growth
for most tree species, for whitebark pine, the ambient soil tem-
perature appeared more stressful than the cold soil treatment,
even with the soil kept moist. The darker color and superior
health of the seedlings in the cold treatment indicated that
higher soil temperature was a stress that appeared to be cumula-
tive over the two growing seasons. However, it appears that all
populations suffered equally in the warm environment because
there was no evidence of genotype-by-environment interaction
between soil temperature treatments for any of the traits as-
sessed. Although this experiment was outside of the natural
range of the species, phenotypic differences in a common gar-
den among populations demonstrate genetic differences and
provide a better basis for estimating limits to seed transfer and
likelihood of maladaptation than do estimates from molecular
Genetic variation and population differentiation — We ob-
served signifi cant differences among population means in most
quantitative traits ( Table 5 ), similar to many other widespread
North American conifers ( Morgenstern, 1996 ). In the subalpine
environments where whitebark pine grows, traits affecting tol-
erance of abiotic stresses most likely play a larger role in deter-
mining fi tness than do growth traits related to competitive
The average level of population differentiation for quantita-
tive traits studied ( Q ST ) was within the range of previous esti-
mates for other conifers ( Merila and Crnokrak, 2001 ; McKay
and Latta, 2002 ; Howe et al., 2003 ; Savolainen et al., 2004 ; St.
Clair et al., 2005; St. Clair, 2006 ; Mimura and Aitken, 2007 ).
Population differentiation ( Q ST ) was strongest across all popu-
lations for traits related to phenology and cold injury. However,
patterns of population differentiation varied among regions,
with populations in the northern region differentiated most
P < 0.0001 for regression of mean temperature of the coldest
month on latitude). The difference for the northern region was
1.9 ° C and for the Rocky Mountain region was 1.0 ° C. Mean
temperature of the coldest month was most closely associated
with latitude in the northern region and with elevation in the
Rocky Mountain region ( Table 4 ). The interval in frost-free
period associated with the second canonical variable (which
primarily comprises growth traits) was 15 d over all regions, which
translated to a difference of 1010 m in elevation or 12.2 ° longi-
tude ( r 2 = 0.23, P = 0.001 for regression of frost-free period on
elevation) and 27.5 d in the Rocky Mountain region.
Effects of common-garden environments — Common-gar-
den experiments are often replicated in different environments
within a species ’ range to detect genotype-by-environment in-
teractions as well as population differentiation. This experiment
was outside of the natural range of whitebark pine; therefore
results may have differed from what would have been observed
in the natural habitat of this species. A limited supply of avail-
Fig. 2. Regression of fi rst quantitative canonical score (QS1) on stan-
dardized mean temperature of the coldest month (MTCM) for 41 popula-
tions of Pinus albicaulis in three geographic regions. Y-axis scale is
standard deviation, and bracket indicates value of LSD 0.20.
TABLE 7. Quantitative trait Q ST values over all populations and by region
for two temperature treatments.
Variable a All populations NorthernRocky Mtn.Southern
a See Table 3 for explanation of variables.
Fig. 3. Scatterplot of fi rst two quantitative canonical scores (QC1 and
QC2) based on eight quantitative traits for 41 populations of Pinus albi-
caulis . Axis scales are standardized values. Symbols refer to geographic
regions shown in Table 2 .
BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
North American conifers, including subalpine fi r [ Abies lasio-
carpa (Hook) Nutt.] ( Peterson et al., 2002 ), mountain hemlock
[ Tsuga mertensiana (Bong.) Carr.] ( Peterson and Peterson,
2001 ), and Douglas fi r ( St. Clair et al., 2005 ).
Whitebark pine has a high level of cold hardiness compared
to other conifers, but signifi cant differences exist among geo-
graphic regions ( Bower and Aitken, 2006 ). In the common-
garden environment, populations from colder (higher latitude)
populations (with lower mean temperature of the coldest month)
fl ushed earlier in the spring and suffered greater spring cold
injury. Earlier fl ushing of these cold-adapted populations may
be due to lower chilling requirements in winter, lower heat sum
requirements in spring, or both ( Howe et al., 2003 ). In their
natural environments, these populations are likely to experience
shorter growing seasons than populations further south, so de-
spite a higher risk of spring cold injury, earlier fl ushing in the
spring may be a mechanism to allow trees at these higher lati-
tudes to extend their growing season relative to trees from lower
latitudes ( Sagnard et al., 2002 ). In species that can tolerate frost
damage well or that have high recovery potential the length
of the growing season may be a more important driving force
in adaptation than the avoidance of damage ( Leinonen and
Hanninen, 2002 ). Worrall (1983) reported differences in both
threshold temperature and heat-sum needed for fl ushing in am-
abilis fi r [ Abies amabilis (Dougl.) Forbes] and subalpine fi r and
also found that populations from higher elevations fl ushed ear-
lier than warmer, lower elevation populations in a common gar-
den. A faster response to warming spring temperatures of higher
elevation or more northerly sources has also been reported for
a number of other conifer species (see references in Campbell
and Sugano, 1979 ; Morgenstern, 1996 ). For coastal Douglas fi r
( P. menziesii var. menziesii ), however, where growth may be
more limited by drought than cold in some environments, pat-
terns were opposite ( Campbell and Sugano, 1979 ).
Seed transfer guidelines and climate change — While white-
bark pine is distributed over a large latitudinal range, the clinal
variation observed indicates that trees from a particular popula-
tion are expected to be optimally adapted for only a portion of
the environmental conditions experienced across the species
range. We have used the fl oating seed transfer model developed
by Rehfeldt (1991 , 1994 ) to determine seed transfer guidelines
for restoration in current climates of whitebark pine. Of the
traits we assessed, the date of needle fl ush gives the strongest
signature for local adaptation because it has the highest Q ST es-
timates (0.43 – 0.63), thus it was considered fi rst for developing
regional estimates of maximum potential seed transfer distances
for restoration without substantial risk of maladaptation. In the
northern region, it should be possible to move seed from cone
collection sites to planting sites that differ by up to 1.9 ° C in
mean temperature of the coldest month and maintain growth
phenology suitable for the current local climate with acceptable
risk of fall cold injury. In the Rocky Mountain region, the cli-
matic transfer maximum is reduced to 1.0 ° C in current climates.
Restoration ecologists, park managers, and foresters can more
easily use seed transfer guidelines based on geographic dis-
tances than climatic differences and these differences in mean
temperature of the coldest month translate to approximately
4.6 ° latitude, or 505 km, for the northern region, and 320 m in
elevation in the Rocky Mountain region, based on signifi cant
correlations between climatic and geographic variables. In the
southern region, the lack of correspondence between the fi rst
seedling phenotypic canonical variable and mean temperature
strongly for growth traits (height growth and biomass), popula-
tions in the southern region differentiated by cold adaptation
traits (date of needle fl ush and cold injury), while populations in
the Rocky Mountain region were differentiated by both height
growth and date of needle fl ush ( Table 7 ). The Q ST values for
these traits in these regions were greater than estimates of dif-
ferentiation in neutral molecular markers ( F ST and G ST ) for
whitebark pine ( Table 1 ). Levels of population differentiation
( F ST ) previously reported in whitebark pine range from 0.034
to 0.088 and average 0.058, which is slightly higher than most
values reported for other stone pine species ( Pinus subsection
Cembrae ) ( Table 1 ). This value indicates that the vast majority
of selectively neutral genetic variation in whitebark pine is
among individuals within populations, as for most conifers.
Our results show that differentiation is stronger for about half
of the quantitative traits we studied than for the upper estimate
of differentiation for neutral genetic markers in whitebark pine,
indicating a moderate degree of local adaptation. Greater dif-
ferentiation based quantitative traits than on neutral markers
suggests a prominent role of natural selection driving local ad-
aptation of populations for many of these polygenic phenotypic
traits ( Lynch et al., 1999 ; Whitlock, 1999 ).
There is a general concordance of the patterns of variation
we detected for seedling phenotypic traits with those reported
by both Zavarin et al. (1991) using monoterpenes and Richard-
son et al. (2002b) using mtDNA. Both of these studies found
differentiation among the Rocky Mountain, northern, and
southern regions of the species range, which may indicate some
historical effects such as isolation, genetic drift, and migration
on quantitative genetic structure in addition to the effects of
selection for local adaptation to climate. Richardson et al.
(2002b) also suggested that genetic evidence supports two
Pleistocene refugia for whitebark pine, one in the Yellowstone
region and one in the southern Oregon Cascades, with subse-
quent northward postglacial colonization patterns that have re-
sulted in a secondary contact zone in the southern Washington
Cascades. An assessment of quantitative traits across the con-
tact zone suggested by Richardson et al. (2002a , b ) might help
support the molecular results, but we are unable to test for this
pattern due to the lack of representation of Washington Cascade
populations in our study.
Environmental gradients associated with phenotypic traits —
Populations differed signifi cantly for nearly all traits, and at a
broad geographic scale, those from higher latitude environ-
ments with lower winter temperatures fl ushed earlier in the
spring, suffered less cold injury in the fall, and allocated more
biomass to shoots in the common-garden environment than
those from milder environments. However, clinal variation pat-
terns that corresponded to climatic gradients varied by region
( Table 4 ), indicating local adaptation is driven by selection
pressures from different environmental factors in different re-
gions. In the northern region, local adaptation to available
growing season length appears to be important because the date
of needle fl ush has a clinal variation associated with frost-free
period. In the Rocky Mountain region, annual and seasonal
mean temperatures appear to be driving local adaptation, with
height growth increasing with temperature at population ori-
gins. In the southern region, where survival and date of needle
fl ush were both associated with rainfall patterns, water avail-
ability appears to be the factor associated with population dif-
ferentiation. Regional differences in relationships between
phenotypes and source climates have been reported for other
AMERICAN JOURNAL OF BOTANY
One strategy for selecting seed sources that may result in
successful establishment of seedlings under current climates, at
least some of which can tolerate warming over the next several
decades, is to transfer seed unidirectionally to the maximum
extent allowable from mild to cold climates on the basis of the
estimates from the fl oating seed transfer model. Most popula-
tions of temperate conifers have a reasonably broad tempera-
ture tolerance, although populations vary in breadth of reaction
norm (e.g., Wang et al., 2006b ). To balance the risks of malad-
aptation in both current and future environments, the challenge
is to plant seedlings into environments near their lower-temper-
ature limits to ensure adequate survival and growth, yet have at
least some of those trees as adults remain within their tempera-
ture tolerances on the warmer side of the reaction norm.
The warming predicted by global circulation models will
likely cause phenotypes to shift northward and upward to track
conditions to which they are locally adapted ( Davis and Shaw,
2001 ), but climatic differences among regions should maintain
clinal variation. The guidelines we propose are established
based on a 20% risk of assuming no difference among popula-
tions when a difference actually exists. This threshold may be
too conservative, given the risks of climate change, and it may
become optimal to transfer seed greater distances than esti-
mated and accept a greater risk of type II error (assuming popu-
lations are the same when they are different). However,
exceeding these distances increases the chance of maladapta-
tion under current conditions and should only be done after
weighing this risk against the need for restoration. Mixing seeds
from different populations within the acceptable transfer range
may also be a reasonable strategy for mitigating uncertain fu-
ture climates because it may increase the probably that at least
some trees survive. Outbreeding depression from the mixing of
populations is unlikely to be a problem for whitebark pine;
there are no published examples of this phenomenon in conifers
that we are aware of, and it is unlikely to evolve in species with
high levels of gene fl ow (e.g., via wind-dispersed pollen).
Successful restoration plantings with preferential movement
of seeds from south to north or to higher elevations, both within
and exceeding the current species range, could facilitate popu-
lation migration, particularly if planted genotypes have some
resistance to white pine blister rust. Without such human inter-
vention, whitebark pine may continue to decline, and while
selection may result in adaptation by favoring survival and
reproduction of those local genotypes adapted to new condi-
tions, this may be insuffi cient demographically to maintain vi-
able populations. A comparison of predictions of the current
range of whitebark pine in British Columbia and the current
predicted range based on bioclimatic modeling indicates a
considerably poorer fi t of predicted to actual distribution than
is typical of most of the 49 forest tree species in the province
( Hamann and Wang, 2006 ). This lack of fi t between actual and
current predicted range of whitebark pine may refl ect a migra-
tional lag from the dependence on seed dispersal by the Clark ’ s
nutcracker and may be indicative of a slow potential rate of migra-
tion in response to climate change. This unoccupied potential
habitat is also predicted to be one of the few areas that have
climates that could support whitebark pine both currently and in
seven or eight decades. Facilitated migration via restoration
plantings that move blister-rust-resistant genotypes along envi-
ronmental gradients and into areas of new potential habitat may
be the only way that populations of this species can maintain
viability. Experimental fi eld plantings will be needed to test this
of the coldest month suggests that seed can be freely moved
within this region. However, in the absence of further data and
until populations from the Washington Cascades can be studied
and compared with other southern populations, we suggest that
transfer between mountain ranges (e.g., Sierra Nevadas and
Cascades) should be avoided. Height growth and biomass in
whitebark pine populations are signifi cantly correlated with the
length of the growing season (frost-free period); however, these
clines are gentle, and estimates of maximum seed transfer dis-
tances based on these traits are also too large to be of practical
use in a conservation or restoration program.
The problem with this approach for estimating symmetrical,
static seed transfer maxima based on climate is that it assumes
local genotypes are optimally adapted to current climates and
that these climates will remain constant. The evidence that we
are in a period of record rates of warming is mounting ( IPCC,
2001 ). While populations of most temperate and boreal tree
species have high levels of genetic variation that could enable
adaptation to new environments ( Hamrick, 2004 ), long genera-
tion lengths will greatly constrain their ability to adapt to rapid
climate change ( Burger and Lynch, 1995 ), and seed dispersal is
unlikely to be suffi ciently rapid to allow populations to migrate
and track climates spatially ( Davis and Shaw, 2001 ). One re-
sponse to modest climate change may be for trees to migrate
within local areas among microsites or aspects. However,
whitebark pine inhabits a relatively small range of aspects,
slopes, and microsites, so this is not a likely mechanism for
maintaining high levels of adaptive diversity. Whitebark pine
has reasonably high levels of variation; however, it requires
several to many decades to reach sexual maturity, its habitats
are discontinuous, largely consisting of high-elevation “ islands ”
separated by lower-elevation valleys, and its migration is de-
pendent on the Clark ’ s nutcracker ( Arno and Hoff, 1989 ). All
reduce the likelihood that this species can adapt or migrate suf-
fi ciently rapidly to avoid the collapse of many populations. Cli-
mate models predict a dramatic decrease in the range of habitat
suitable for whitebark pine with increases in temperature and
CO 2 ( Romme and Turner, 1991 ; Bartlein et al., 1997 ; Hamann
and Wang, 2006 ). While increasing temperature may result in
new habitat available north of its current range, it is also likely
to lead to an upward shift of the timberline and the range of
whitebark pine, resulting in a smaller potential area for it to oc-
cupy. Many populations currently have negative growth rates
due to white pine blister rust infection; fi re exclusion and re-
sulting competition from shade-tolerant, fi re-intolerant, faster-
growing conifer species; and mountain pine beetle (Kendall
and Keane, 2001). Interactions among this introduced disease,
the current mountain pine beetle epidemic, and climate change
could drive populations toward extirpation and the species to-
ward extinction ( Hamrick, 2004 ).
New approaches to developing seed transfer recommenda-
tions should balance anticipated future climates with the need
to restrict seed movement to environmental distances that can
lead to successful seedling establishment under current, albeit
transient, conditions. If seed is transferred an excessive distance
from warmer to colder climates in anticipation of future warm-
ing, cold injury, or mortality may result during establishment;
yet if predictions of future climate are ignored, local seed
sources that are fi t in current local environments may result in
restoration plantings of trees that are not adequately competi-
tive with other species or will never achieve reproductive matu-
rity as a result of slow growth rates under the conditions of the
decades to come.
BOWER AND AITKEN — ECOLOGICAL GENETICS OF WHITEBARK PINE
IPCC . 2001 . Third Assessment Report of the Intergovernmental Panel
on Climate Change IPCC (WG I & II). Cambridge University Press,
JORGENSEN , S . M . , AND J . L . HAMRICK . 1997 . Biogeography and popula-
tion genetics of whitebark pine, Pinus albicaulis. Canadian Journal of
Forest Research 27 : 1574 – 1585 .
KENDALL , K . C . , AND R . E . KEANE . 2001 . Whitebark pine decline: infection,
mortality, and population trends. In D. F. Tomback, S. F. Arno, and R.
E. Keane [eds.], Whitebark pine communities: Ecology and restora-
tion, 221 – 242. Island Press, Washington, D.C., USA.
KRAKOWSKI , J . , S . N . AITKEN , AND Y . A . EL - KASSABY . 2003 . Inbreeding
and conservation genetics in whitebark pine. Conservation Genetics
4 : 581 – 593 .
KRUTOVSKII , K . V . , D . V . POLITOV , AND Y . P . ALTUKHOV . 1995 . Isozyme
study of population genetic structure, mating system and phylogenetic
relationships of the fi ve stone pine species (subsection Cembrae , section
Strobi , subgenus Strobus ). In P. Baradat, W. T. Adams, and G. Muller-
Starck [eds.], Population genetics and genetic conservation of forest
trees, 279 – 304. SPB Academic Publishing, Amsterdam, Netherlands.
LANNER , R . M . 1982 . Adaptations of whitebark pine for seed dispersal
by Clark ’ s Nutcracker. Canadian Journal of Forest Research 12 :
391 – 402 .
LEINONEN , I . , AND H . HANNINEN . 2002 . Adaptation of the timing of bud burst
of Norway spruce to temperature and boreal climate. Silva Fennica
36 : 695 – 701 .
LITTLE , E . L . JR . 1971 . Atlas of United States trees, vol. 1, Conifers and im-
portant hardwoods. USDA Forest Service Miscellaneous Publication
1146. Washington, D.C., USA.
LYNCH , M . , M . PFRENDER , K . SPITZE , N . LEHMAN , J . HICKS , D . ALLEN , L .
LATTA , M . OTTENE , F . BOGUE , AND J . COLBOURNE . 1999 . The quan-
titative and molecular genetic architecture of a subdivided spe-
cies. Evolution; International Journal of Organic Evolution 53 :
100 – 110 .
Mann, A. D. 1996 . Alpha+: Experimental designs for variety trials and
many-treatment experiments. Biometrics and Statistics Scotland,
MCCOOL , S . F . , AND W . A . FREIMUND . 2001 . Threatened landscapes and
fragile experiences: Confl ict in whitebark pine restoration. In D. F.
Tomback, S. F. Arno, and R. E. Keane [eds.], Whitebark pine com-
munities: ecology and restoration, 263 – 284. Island Press, Washington,
MCKAY , J . K . , AND R . G . LATTA . 2002 . Adaptive population divergence:
markers, QTL and traits. Trends in Ecology & Evolution 17 : 285 – 291 .
MCKAY , J . K . , C . E . CHRISTIAN , S . HARRISON , AND K . J . RICE . 2005 . “ How
local is local? ” A review of practical and conceptual issues in the ge-
netics of restoration. Restoration Ecology 13 : 432 – 440 .
MCKINNEY , D . W . , M . F . HUTCHINSON , J . L . KESTEVEN , AND L . A . VENIER .
2001 . Canada ’ s plant hardiness zones revisited using modern cli-
mate interpolation techniques. Canadian Journal of Plant Science 81 :
129 – 143 .
MERILA , J . , AND P . CRNOKRAK . 2001 . Comparison of genetic differentiation
at marker loci and quantitative traits. Journal of Evolutionary Biology
14 : 892 – 903 .
MIMURA , M . , AND S . N . AITKEN . 2007 . Adaptive gradients and isolation by
distance with postglacial migration in Picea sitchensis. Heredity 99 :
224 – 232 .
MORGENSTERN , K . E . 1996 . Geographic variation in forest trees. UBC Press,
Vancouver, British Columbia, Canada.
PATTERSON , H . D . , AND E . R . WILLIAMS . 1976 . A new class of resolvable
incomplete block designs. Biometrika 63 : 83 – 92 .
PETERSON , D . W . , AND D . L . PETERSON . 2001 . Mountain hemlock growth
responds to climatic variability at annual and decadal time scales.
Ecology 82 : 3330 – 3345 .
PETERSON , D . L . , D . W . PETERSON , AND G . J . ETTL . 2002 . Growth responses
of subalpine fi r to climatic variability in the Pacifi c Northwest.
Canadian Journal of Forest Research 32 : 1503 – 1517 .
POTENKO , V . V . , AND A . V . VELIKOV . 1998 . Genetic diversity and differen-
tiation of natural populations of Pinus koraiensis (Sieb. et Zucc.) in
Russia. Silvae Genetica 47 : 202 – 208 .
ARNO , S . F . , AND R . J . HOFF . 1989 . Silvics of whitebark pine ( Pinus albi-
caulis ), General Technical Report. INT-GTR-253. U.S. Department of
Agriculture, Forest Service, Intermountain Research Station, Ogden,
BARTLEIN , P . J . , C . WHITLOCK , AND S . L . SHAFER . 1997 . Future climate in the
Yellowstone National Park region and its potential impact on vegeta-
tion. Conservation Biology 11 : 782 – 792 .
BELOKON , M . M . , Y . S . BELOKON , D . V . POLITOV , AND Y . P . ALTUKHOV .
2005 . Allozyme polymorphism of Swiss stone pine Pinus cembra L.
in mountain populations of the Alps and eastern Carpathians. Russian
Journal of Genetics 41 : 1268 – 1280 .
BOWER , A . D . , AND S . N . AITKEN . 2006 . Geographic and seasonal variation in
cold hardiness of whitebark pine ( Pinus albicaulis Engelm.). Canadian
Journal of Forest Research 36 : 1842 – 1850 .
Bower , A. D. , and S. N. Aitken . 2007 . Mating system and inbreeding de-
pression in whitebark pine ( Pinus albicauls Engelm.). Tree Genetics
and Genomes 3: 379 – 388. [Online] DOI
BRUEDERLE , L . P . , D . F . TOMBACK , K . K . KELLY , AND R . C . HARDWICK .
1998 . Population genetic structure in a bird-dispersed pine, Pinus albi-
caulis (Pinaceae). Canadian Journal of Botany 76 : 83 – 90 .
BURGER , R . , AND M . LYNCH . 1995 . Evolution and extinction in a changing
environment: a quantitative genetic analysis. Evolution; International
Journal of Organic Evolution 49 : 151 – 163 .
BURR , K . E . , A . ERAMIAN , AND K . EGGLESTON . 2001 . Growing whitebark pine
seedlings for restoration. In D. F. Tomback, S. F. Arno, and R. E. Keane
[eds.], Whitebark pine communities: Ecology and restoration, 325 – 345.
Island Press, Washington, D.C., USA.
CAMPBELL , R . K . 1979 . Genecology of Douglas-fi r in a watershed in the
Oregon cascades. Ecology 60 : 1036 – 1050 .
CAMPBELL , R . K . , AND A . I . SUGANO . 1979 . Genecology of bud-burst phe-
nology in Douglas-fi r: response to fl ushing temperature and chilling.
Botanical Gazette (Chicago, Ill.) 140 : 223 – 231 .
DAVIS , M . B . , AND R . G . SHAW . 2001 . Range shifts and adaptive responses to
Quaternary climate change. Science 292 : 673 – 679 .
GERNANDT , D . S . , G . GEADA L Ó PEZ , S . ORTIZ GARC Í A , AND A . LISTON .
2005 . Phylogeny and classifi cation of Pinus. Taxon 54 : 29 – 42 .
GONCHARENKO , G . G . , V . E . PADUTOV , AND A . E . SILIN . 1993a . Allozyme
variation in natural populations of Eurasian pines. 1. Population struc-
ture, genetic variation, and differentiation in Pinus pumila (Pall) Regel
from Chukotsk and Sakhalin. Silvae Genetica 42 : 237 – 246 .
GONCHARENKO , G . G . , V . E . PADUTOV , AND A . E . SILIN . 1993b . Allozyme vari-
ation in natural populations of Eurasian pines. 2. Genetic variation, di-
versity, differentiation, and gene fl ow in Pinus sibirica DuTour in some
lowland and mountain populations. Silvae Genetica 42 : 246 – 253 .
HAMANN , A . , AND T . WANG . 2006 . Potential effects of climate change on
ecosystem and tree species distribution in British Columbia. Ecology
87 : 2773 – 2786 .
HAMRICK , J . L . 2004 . Response of forest trees to global environmental
changes. Forest Ecology and Management 197 : 323 – 335 .
HOFF , R . J . , D . E . FERGUSON , G . I . MCDONALD , AND R . E . KEANE .
2001 . Strategies for managing whitebark pine in the presence of white
pine blister rust. In D. F. Tomback, S. F. Arno, and R. E. Keane [eds.],
Whitebark pine communities: Ecology and restoration, 346 – 366. Island
Press, Washington, D.C., USA.
HOWE , G . T . , S . N . AITKEN , D . B . NEALE , K . D . JERMSTAD , N . C . WHEELER ,
AND T . H . H . CHEN . 2003 . From genotype to phenotype: unraveling the
complexities of cold adaptation in forest trees. Canadian Journal of
Botany 81 : 1247 – 1266 .
HUFFORD , K . M . , AND S . J . MAZER . 2003 . Plant ecotypes: Genetic differentia-
tion in the age of ecological restoration. Trends in Ecology & Evolution
18 : 147 – 155 .
HUTCHINS , H . E . , AND R . M . LANNER . 1982 . The central role of Clark ’ s
Nutcracker in the dispersal and establishment of whitebark pine.
Oecologia 55 : 192 – 201 .
HUTCHINSON , M . F . 2000 . ANUSPLIN version 4.1 user ’ s guide. Australian
National University, Centre for Resource and Environmental Studies,
AMERICAN JOURNAL OF BOTANY
POTENKO , V . V . , AND A . V . VELIKOV . 2001 . Allozyme variation and mat-
ing system of coastal populations of Pinus koraiensis Sieb. et Zucc in
Russia. Silvae Genetica 50 : 117 – 122 .
PRICE , R . A . , A . LISTON , AND S . H . STRAUSS . 1998 . Phylogeny and classifi -
cation of Pinus . In D. M. Richardson [ed.], Ecology and biogeography
of Pinus , 49 – 91. Cambridge University Press, Cambridge, UK.
REHFELDT , G . E . 1991 . A model of genetic variation for Pinus ponderosa
in the Inland Northwest (U.S.A.): applications in gene resource man-
agement. Canadian Journal of Forest Research 21 : 1491 – 1500 .
REHFELDT , G . E . 1994 . Adaptation of Picea engelmannii populations to the
heterogeneous environments of the Intermountain West. Canadian
Journal of Botany 72 : 1197 – 1208 .
RICHARDSON , B . A . , S . J . BRUNSFELD , AND N . B . KLOPFENSTEIN .
2002a . Assessing Clark ’ s nutcracker seed-caching fl ights using ma-
ternally inherited mitochondrial DNA of whitebark pine. Canadian
Journal of Forest Research 32 : 1103 – 1107 .
RICHARDSON , B . A . , S . J . BRUNSFELD , AND N . B . KLOPFENSTEIN . 2002b . DNA
from bird-dispersed seed and wind-disseminated pollen provides in-
sights into postglacial colonization and population genetic structure of
whitebark pine ( Pinus albicaulis ). Molecular Ecology 11 : 215 – 227 .
ROGERS , D . L . , C . I . MILLAR , AND R . D . WESTFALL . 1999 . Fine-scale genetic
structure of whitebark pine ( Pinus albicaulis ): Associations with wa-
tershed and growth form. Evolution; International Journal of Organic
Evolution 53 : 74 – 90 .
ROMME , W . H . , AND M . G . TURNER . 1991 . Implications of global climate
change for biogeographic patterns in the Greater Yellowstone ecosys-
tem. Conservation Biology 5 : 373 – 386 .
SAGNARD , F . , C . BARBEROT , AND B . FADY . 2002 . Structure of genetic diver-
sity in Abies alba Mill. from southwestern Alps: multivariate analysis
of adaptive and non-adaptive traits for conservation in France. Forest
Ecology and Management 157 : 175 – 189 .
SAS Institute . 1999 . The SAS system for Windows, version 8.0. SAS
Institute, Cary, North Carolina, USA.
SAVOLAINEN , O . , F . BOKMA , R . GARC Í A - GIL , P . KOMULAINEN , AND T . REPO .
2004 . Genetic variation in cessation of growth and frost hardiness and
consequences for adaptation of Pinus sylvestris to climatic changes.
Forest Ecology and Management 197 : 79 – 89 .
SPITZE , K . 1993 . Population structure in Daphnia obtusa : Quantitative ge-
netic and allozymic variation. Genetics 135 : 367 – 374 .
SQUILLACE , A . E . 1974 . Average genetic correlations among offspring
from open-pollinated forest trees. Silvae Genetica 23 : 149 – 156 .
ST . CLAIR , J . B . 2006 . Genetic variation in fall cold hardiness in coastal
Douglas-fi r in western Oregon and Washington. Canadian Journal of
Botany 84 : 1110 – 1121 .
ST . CLAIR , J . B . , N . L . MANDEL , AND K . W . VANCE - BORLAND .
2005 . Genecology of Douglas fi r in western Oregon and Washington.
Annals of Botany 96 : 1199 – 1214 .
STUART - SMITH , G . J . 1998 . Conservation of whitebark pine in the Canadian
Rockies: Blister rust and population genetics. M.Sc. thesis, University
of Alberta, Edmonton, Alberta, Canada.
TANI , N . , N . TOMARU , M . ARAKI , AND K . OHBA . 1996 . Genetic diversity and
differentiation in populations of Japanese stone pine ( Pinus pumila ) in
Japan. Canadian Journal of Forest Research 26 : 1454 – 1462 .
TOMBACK , D . F . 1978 . Foraging strategies of Clark ’ s Nutcracker. Living
Bird 16 : 123 – 161 .
TOMBACK , D . F . 1982 . Dispersal of whitebark pine seeds by Clark ’ s
Nutcracker: A mutualism hypothesis. Journal of Animal Ecology 51 :
451 – 467 .
TOMBACK , D . F . , S . F . ARNO , AND R . E . KEANE . 2001 . The compelling case
for management intervention. In D. F. Tomback, S. F. Arno, and R. E.
Keane [eds.], Whitebark pine communities: Ecology and restoration,
3 – 25. Island Press, Washington, D.C., USA.
WANG , T . , A . HAMANN , D . L . SPITTLEHOUSE , AND S . N . AITKEN .
2006a . Development of scale-free climate data for western Canada
for use in resource management. International Journal of Climatology
26 : 383 – 397 .
WANG , T . L . , A . HAMANN , A . YANCHUK , G . A . O ’ NEILL , AND S . N . AITKEN .
2006b . Use of response functions in selecting lodgepole pine popula-
tions for future climates. Global Change Biology 12 : 2404 – 2416 .
WESTFALL , R . D . 1992 . Developing seed transfer zones. In L. Fins, S. T.
Friedman, and J. V. Brotschol [eds.], Handbook of quantitative forest
genetics, 313 – 398. Kluwer, Dordrecht, Netherlands.
WHITLOCK , M . C . 1999 . Neutral additive genetic variance in a metapopula-
tion. Genetical Research 74 : 215 – 221 .
WORRALL , J . 1983 . Temperature-bud burst relationships in Amabalis and
subalpine fi r provenance tests replicated at different elevations. Silvae
Genetica 32 : 203 – 209 .
YANDELL , U . G . 1992 . An allozyme analysis of whitebark pine ( Pinus albi-
caulis Engl.). M.Sc. thesis, University of Nevada, Reno, Nevada, USA.
ZAVARIN , E . , Z . RAFII , L . G . COOL , AND K . SNAJBERK . 1991 . Geographic
monoterpene variability of Pinus albicaulis. Biochemical Systematics
and Ecology 19 : 147 – 156 .