1 004
Microgeographic genetic variation of Sitka spruce in southeastern Alaska
ROBERT K. CAMPBELL
USDA Forest Service, Pacific Northwest Forest Experiment Station, Forestry Sciences Laboratory,
3200 Jefferson Way, Corvallis, OR 97330, U.S.A.
WILLIAM A. PAWUK
USDA Forest Service, Petersburg, AK 99833, U.S.A.
AND ARLAND S. HARRIS USDA Forest Service, Forestry Sciences Laboratory, P.O. Box 909, Juneau, AK 99802, U.S.A.
Received February 1 1, 1 988
Accepted March 3 1 , 1989
CAMPBELL,
R. K., PAWUK, W. A., and HARRIS, A. S. 1 989. Microgeographic genetic variation of Sitka spruce i n
southeastern Alaska. Can. J . For. Res. 19: 1 004- 1 0 1 3.
Microgeographic genetic variation among populations of Sitka spruce on Mitkof Island in southeastern Alaska i s
described. I n two common-garden environments, w e evaluated genotypes o f 208 parent trees from 1 14 locations in
a 1 7 000-ha area. Two principal components accounted for most of the variation among locations in 1 1 traits measured
to evaluate growth vigor and rhythm of 2-year-old seedlings. Regression analyses of factor scores derived from prin­
cipal components revealed genetic gradients associated with elevation, slope, aspect, and west-east and north-south
direction. Large amounts of additive genetic variation in factor scores occurred among trees within locations. When
this variation within locations was used as a scale, variation among locations was also large. In an extreme case, locations
differed in factor scores of the first principal component by about 3.0 units of the standard deviation of additive genetic
variation in factor scores. Of the total differentiation in this case, elevational range (600 m) contributed 0.7 units o f
standard deviation, aspect contributed 0.9 units, and distance ( 1 6 km) from north central t o southeastern parts o f the
island contributed 1 .4 units.
CAMPBELL,
R. K., PAWUK, W. A., et HARRIS, A. S. 1 989. Microgeographic genetic variation of Sitka spruce i n
southeastern Alaska. Can. J . For. Res. 19: 1 004- 1 0 1 3 .
Les variations genetiques microgeographiques presentes dans des populations d'Epinette de Sitka sur l'lle Mitkof
dans le sud-est de l ' Alaska sont decrites. Dans deux environnements de jardins communs ont ete evalues les genotypes
de 208 parents provenant de 1 1 4 localites reparties sur une superficie de 1 7 000 ha. Deux principaux constituants ont
ete a Ia source de Ia plupart des variations parmi les localites en ce qui concerne 1 1 traits mesures dans le but d'evaluer
Ia vigueur et le rythme de croissance des semis de 2 ans. Des analyses de regression de scores factoriels deduits des
constituants principaux ont montre qu'il existait des gradients genetiques lies a l'altitude, a Ia pente, a l'exposition
et aux directions ouest-est et nord-sud. De grands effets des variations genetiques additives dans les scores factoriels
ont ete rencontres parmi les arbres dans les diverses localites. Lorsque ces variations parmi les localites etaient utilisees
comme une echelle de mesure, les variations entre les localites etaient egalement grandes. Dans un cas extreme, les
localites differaient quant aux scores factoriels du premier constituant principal par environ 3,0 unites d'ecart-type
des variations genetiques additives des scores factoriels. Parmi Ia differentiation totale dans ce cas, les differences d'altitude
(600 m) ont contribue pour 0,7 unite d'ecart-type, !'exposition a contribue pour 0,9 unite et Ia distance ( 1 6 km) du
centre-nord aux sections du sud-est de !'lie a contribue pour I ,4 unite.
[Traduit par Ia revue]
Introduction
Few studies of genetic differentiation in tree species have
been done at a microgeographic scale, in which the area
sampled is usually smaller than the area represented by a
single provenance in a macrogeographic study (Rehfeldt
1974; Campbell1979). Microgeographic studies, therefore,
can detect deviations in genetic variation patterns that are
beyond the resolution of macrogeographic studies. Further­
more, the deviations may reveal adaptation to local com­
ponents of the environment that should be considered in
seed-transfer guidelines or breeding-zone delimitation. This
paper describes genetic differentiation of Sitka spruce on
a small, offshore island in southeastern Alaska.
In Sitka spruce (Picea sitchensis (Bong.) Carr.),
macrogeographic clinal patterns have been reported for
several traits. Provenances with early bud burst come from
northern, high-elevation, and inland origins (Lines and
Mitchell 1966; Falkenhagen 1977). Provenances with early
bud set (Roche and Fowler 1975) and small seedlings
Printed in Canada I lmprime au Canada
(Falkenhagen 1977; Birot and Christophe 1983) come from
northern origins. These major trends of genetic variation
with latitude, elevation, and continentality vary with local
conditions. Using five seed and cone traits, Falkenhagen and
Nash (1978) grouped provenances of western Canada and
southeastern Alaska into five subregions. Within subregions,
variation among provenances was correlated with local envi­
ronment (Falkenhagen 1978). From the analyses of seed and
cone traits, and from evidence of correlations among seed
and growth traits, Falkenhagen hypothesized, " ...that there
exist stepped ecoclines, with discontinuities at the ecological
region level. These regions themselves change according to
ecogeographical gradients, and within each region, regional
clines exist."
Although data are reported that hint at genetic diversity
among local populations (Yeh and Rasmussen 1985), sampl­
ing of Sitka spruce usually has been less intensive than is
necessary to discover microgeographic differentiation.
Falkenhagen and Nash's (1978) subregion of southeastern
CAMPBELL ET AL.
Alaska, for example, includes only eight provenances. In
a study in which some sampling may have been done at
microgeographic intensity (Roche 1969), the sampling
occurred within a hybridization zone of Sitka spruce and
white spruce (Picea glauca (Moench) Voss).
For a scale to measure differentiation among populations
(locations), we used the standard deviation of additive
genetic variation within locations. Estimates of additive
genetic variation in seedling traits of Sitka spruce have dif­
fered widely from report to report (Samuel et a!. 1972;
Falkenhagen 1977; Samuel and Johnstone 1979; Birot and
Christophe 1983), perhaps because mating and test designs
have differed. We, therefore, decided to estimate additive
genetic variation within locations, in addition to describing
variation among locations associated with elevation, slope,
aspect, and position of local populations on Mitkof Island.
To accomplish both objectives, we evaluated genotypes of
208 parent trees from 1 14locations in two common-garden
environments at a forest nursery on the island.
1005
100 km
f-----i
5 km
L------
----
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1
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---
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l,
' .... _ .. ___
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f
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f
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Materials and methods
Mitkof Island (56°40'N, 132°49'W) occupies a roughly
triangular area of about 52 000 ha. Three larger islands to the west
and the much larger Prince of Wales Island to the south shield
Mitkof Island from the Pacific Ocean. Frederick Sound, 10 km
wide, separates the island from the mainland mountain range to
the northeast. Peaks on the mainland adjacent to Frederick Sound
rise to 3000 m and have permanent glacial mantles agglomerating
in extensive ice fields. One of these glaciers, the southernmost
tidewaters glacier in Alaska, chokes Leconte Bay with icebergs in
the summer. Just south of Laconte Bay, the Stikine, the largest
river in southeastern Alaska, drains the high plateau behind the
coastal mountains and empties into Frederick Sound directly east
of the southern part of the sample area. These various sources of
cold air to the east of the island seem to influence its climate.
Petersburg has about 6o/o fewer degree-days in the year than does
Wrangell, the nearest comparable weather station (Farr and Harris
1979). Petersburg also has fewer frost-free days (140) than any of
the five surrounding stations on the interior islands by an average
of 27 days (Farr and Hard 1987).
Our study objectives determined the analysis procedures and
sampling design. We first wished to test the hypothesis that clinal
variation existed on the island. A less important goal was to describe
clinal patterns if such existed. For these goals, regression is a prefer­
red analytical tool given an appropriate sampling design. Our
hypotheses about population responses on the island dictated a
regression model that included several site variables with additional
terms for curvature and interaction. We anticipated finding a com­
plex clinal relationship of genotypes with temperature gradients
if natural selection has adapted Sitka spruce to growing season
length. Because Mitkof Island is mountainous and near a cold con­
tinental coastline, growing seasons might be expected to be com­
paratively shorter at high elevations and on the eastern side of the
island. Growing season might also be influenced by aspect and
slope; solar radiation arrives at low angles in spring and fall at
high latitudes. These several site variables may affect growing
season in ways that are neither linear nor independent of one
another.
The various constraints arising from our resources for experi­
mentation, a large regression model, and our objectives determined
the sampling design. Our resources, for example, limited the
number of parent genotypes we could evaluate. The regression
model called for a large number of site variables and related terms,
and our objective required minimization of errors connected with
FIG. 1. Mitkof Island in southeastern Alaska. Broken lines
represent sampling transects, usually from mountain peak or
ridgeline to valley floor. Solid lines running west to east across the
island are north and south transects along which genetic trends
are illustrated in succeeding figures. The cross near the inlet
represents the origin from which all north and east departures were
measured.
sions in hypothesis testing becomes progressively higher. The prob­
lem is not serious if the ratio of observations to variables is high
and if the correlations among variables are small. We, therefore,
sampled only a few parents per location to ensure a satisfactory
ratio of locations to site variables, and sampling was designed to
keep locations as nearly uncorrelated as is possible in a sample of
wild populations.
The study sampled an area of about 17 000 ha in the southeastern
third of the island. The northern part of the area stretched almost
completely across the triangular island, the southern part about
halfway. Seed was collected from 208 trees in 28 transects spread
fairly evenly within the area (Fig. 1). Twenty-five transects extended
from ridgeline to valley bottom or down to a clear-cut backline.
Elevational spacing of collections in these transects ranged from
100 to 150 m. Three other transects followed low-elevation roads.
The total collection provided samples from two trees at 94 locations
and one tree at an additional 20 locations for a total of 114 loca­
tions, or about one location per 1500 ha. We sampled transects
rather than systematic areas because Sitka spruce grows primarily
on the better drained mountain slopes, and transects offered viable
routes for sampling slopes in roadless areas.
Five site variables were measured at each location:
(1) South to north departure, ranging from -7.45 to
9.65 km, was measured from the southwestern corner
of sec. 31, tp. 60 S, rge. 80 E.
(2) West to east departure, ranging from - 1 to 21 km, was
measured from the same point.
statistical tests of regression coefficients. In stepwise regression,
analytical steps are sequential, each test conditional on a previous
(3) Elevation was sampled fairly evenly over the elevational
range of Sitka spruce on the island, from about 15 to
7QO ll},
step. In each step, therefore, the probability of erroneous conclu­
(4) Aspect, measured in radians of azimuth, was sampled
.. ---··-··-·--··
1006
CAN. J. FOR. RES . VOL . 19, 1989
TABLE
1. Classification design for analysis of variance of Mitkof
Island families
Source of
variation
Replication
Location
Families within
locations
Plot error
Within plot
df
3
113
94
624
a
Expected mean squares
(J /k
+
(J2p
+
4a (s)
(J /k
(J /k
(]2w
+
(J2
p
(J2
+
4af(s)
+
+
7 . 2954a;
p
other species (Campbell
1986).
The analysis involved six steps:
(i) analyzing variance and covariance of data from each environ­
ment according to the classification design (Table 1); (ii) estimating
components of variance and covariance, additive genetic variance,
and heritability at the individual seedling level; (iii) estimating
genetic correlation coefficients at the location and family levels
of variance and covariance; (iv) reducing the dimensions in the
data by a principal component analysis of a genetic correlation
matrix; ( v) calculating factor scores from the eigenvectors for each
parent tree for each principal component; and (vi) describing
patterns of genetic variation in factor scores among parent trees
by regression on site variables.
Additive genetic variance was calculated as 3a;(s)· The multiplier
3 assumes 0.33 as the genetic correlation among offspring of open­
NoTE: a = variance of within-plot effects including genetic variance within openvariance of plot effects; a1(•l
pollinated familes; a
variance of family effects
variance of location effects.
averaged across locations; and a ;
"Degrees of freedom varied (from 4086 to 4116), as did the harmonic mean of
individuals per plot, k (from 5. 89to 5. 93), depending on the trait and environment
being analyzed.
pollinated parents; it reflects the greater likelihood of pollination
by adjacent, related trees (Namkoong 1966; Squillace 1974) and
production of seed by self-pollination (Sorensen 1973). Heritability,
on an individual seedling basis, was therefore calculated as
3af(s/(a!
+
fJ
+
af(s))
where the terms are as defined in Table
evenly over the range in aspect with some under­
representation in the northeastern quadrant. For
analysis, aspect (A ) was transformed, as suggested by
Stage (1976), to sin A and cos A.
1.
A matrix of genetic correlation coefficients summarizes the total
genetic variation and covariation existing in a multivariate system
of traits. Usually most of the variation in the original variates of
multivariate systems can be described by a few derived variables.
(5) Slope was sampled fairly evenly over the range in slope
Derived variables are called principal components, and correspond­
Site variables were only slightly correlated, with one exception;
ing derived variates for each principal component and genetic entry
are factor scores (Morrison 1967). The set of coefficients of the
equation used to calculate factor scores from original observations
(0 to 800Jo) with some under-representation of the steepest
(>60%) and flattest ( <30%) slopes.
because of the lay of the island, southern locations were usually
farther east than northern locations (r
0 73). A small correlation
between slope and elevation of locations (r
0.39) reflected the
tendency for steeper slopes to occur at higher elevations. Correla­
0.30.
tions among other site variables were all smaller than r
We estimated genotypic values of parent trees by growing open­
.
pollinated progeny in two common-garden environments in a
nursery about 1.3 km west of the northwestern corner of the sample
area. One was the greenhouse environment (I), which is normally
used for growing tubed seedlings in the USDA Forest Service con­
tainerized nursery. The other was the Mitkof Island environment
(0) adjacent to the greenhouse at the nursery. Seedlings were grown
from seed in 65-cm 3 Leach cells in the greenhouse until the middle
of the first growing season when the seedlings in the 0 environ­
ment were moved outside.
A randomized block design was used in which six seedlings per
family made up a row plot randomly allocated to position within
a block. The design specified 4992 seedlings in each of two envi­
ronments (208 families x 4 replications x
6 seedlings per plot).
Each block was surrounded by two rows of seedlings used only
as a border.
We measured four primary traits in each environment to evaluate
differences among families in growth rhythm and growth vigor.
We recorded bud-burst status of each seedling twice weekly and
bud-set status once weekly. Total height was measured at the end
of the first and second growing seasons and diameter at the end
of the second season. For most primary traits, a secondary trait
was created by calculating the standard deviation in primary trait
scores among the six seedlings in a plot.
Seedling size is often influenced by seed weight. To estimate this
effect, we weighed one 50-seed lot from each family and correlated
this weight with the family mean height at the end of the second
growing season. Three types of correlations were calculated: among
all families (208), among families within locations, and among loca­
tions. The lst two types were calculated from components of
variance and covariance derived from one-way analyses of variance
(and covariance) of locations and families nested within locations
(Table 1, but omitting effects of replications, plot error, and within­
plot error).
We analyzed data by procedures used in similar experiments with
is the eigenvector; each principal component has a characteristic
eigenvector. Factor scores for parent trees calculated from the
eigenvector of any principal component are uncorrelated with
parent-tree scores calculated from the eigenvector of any other prin­
cipal component.
To relate factor scores to site variables, the final regression model
was selected by backward stepwise regression from an initial model
that included 13 terms: linear and quadratic terms for south to
), west to east departure
and elevation (E);
north departure
D2L
L 2D;
(L
(D),
and
slope ( 1); sine and cosine of aspect (sin A and
cos A); and T sin A and T cos A. Because correlations among site
variables were small, thus minimizing statistical problems associated
with multicollinearity, a satisfactory ratio of variables to observa­
tions could be insured by restricting the initial model to 13 terms.
Terms for interactions of elevation with other variables, for
example, were omitted from the model for this reason. The final
regression was selected on two criteria: the standard errors (and
significance) of partial regression coefficients and the examination
of residuals. Lack of fit was tested using the factor scores for two
trees at a location as repeats and the variation among trees within
locations as an estimate of pure error (Draper and Smith 1966).
Coefficients in complex regression equations are difficult to inter­
pret. To illustrate differentiation on the island, we graphed predic­
tions from regression equations. Predictions were limited to two
west to east transects across the sample area, one each near the
northern and southern borders of the area (Fig. l).
Results
In the first growing season, seedlings grown in the
greenhouse set buds 2 days earlier and grew about 6o/o taller
than seedlings grown outside. In the second growing season,
greenhouse seedlings burst buds 17 days earlier, set buds
7 days earlier, and ended by being 24% taller and 13% larger
in diameter than seedlings grown outside (Table 2).
Except for bud burst in the greenhouse (IN-BB2), genotypic
values of parent trees varied among island locations for all
primary traits (Table 2, ab .
Genetic variation also existed among trees within locations
for all traits (Table 2, af(s)). Although estimates of variance
1007
CAMPBELL ET AL.
TABLE
2.
Analysis of variance for traits, seed weight (so WT) , and factor scores
of the first and second principal components (PCI and PC2)
Components of variance as a O?o of total variance x
Trait a
IN-BSI
IN-HI
IN-BB2
IN-H2
IN-D2
IN-BS2
OUT-BSI
OUT-HI
OUT-BB2
OUT-H2
OUT-D2
OUT-BS2
SD WT
19.65
15.41 31.07
46.69
3.81
15.40
21.17
14.54
65.55
37.70
3.37
16.40
11.40 PC!
PC2
as2
a;(s)
a2p
a
8.6***
5.2***
2.5* 6.1***
3.8***
10.0** 7.2***
9.0***
5.0***
8.7***
9.4***
31.7***
28.9*** 45.7***
51.6***
11.5***
4.7***
8.1***
8.3***
7.0***
28.4***
12.7***
5.5***
6.9***
7.3***
5.9***
32.9*** 71.1
54.3
48.4
19.1***
32.5*** 16.6***
15.0***
19.1***
61.6
15.8***
16.0***
17.0***
20.4*** 18.0***
35.4 60.8 57.6
72.8 70.6 -70.1 b
Total variance
29.7161
12.9615 39.3128 217.0926
0.8746
4.5584c 55.5302 9.8262
29.8117
39.9254 0.2928 0.4365c
3.3793d 2.3497d
2.0751d
64.2 69.5 71.1 63.6
66.8 "Trait codes: IN and OUT refer to whether seedlings were grown inside or outside the greenhouse;
BB, BS , H, and D are, respectively, bud-burst date, bud-set date, height , and diameter; I and 2are first
and second growing seasons, respectively. BSl was measured in days, BB2in half days, BS2in weeks,
H in centimetres, D in millimetres, and SD WT in milligrams (to obtain actual values for SD WT divide
by 5) .
bTotal variance = a + Ufts) + a + a for all values, except where otherwise indicated (symbols
are defined in Table 1).
'Total variance = a + <1fts) + a . dTotal variance = a + <1ftsJ·
*0.06 > p > 0.05. ••o. o5 > P > o.o1. •••p < 0.01. TABLE
Trait
IN-BSI
IN-HI
IN-BB2
IN-H2
IN-D2
OUT-BS!
OUT-HI
OUT-BB2
OUT-H2
OUT-D2
3.
Structural relations
(X 100)
in the variability among seedlings
(1)
a,IX
(2) (3)
aA/X a;/(a;+ 3a;(s
)
) 8.1
5.4
3.2
7.8
4.5
9.5 6.5
1.9 4.9
4.9
16.3
8.7
10.0
15.8
11.2
21.8 8.7 3.8 7.8 6.7 20
27
9
20
15
16 35
19 28 35
(4)
2ai<s/a 38
16
22
24
20
40
16
19
23
18
(6)
(5)
a/X awlX
12
13
8
12
11
14
9
3
8
7
22 18 17
27
21
28
18
7
13
13
(7
h
1
38
15 25
27
22
41
18
22
24
19 NoTE: Structural relations are illustrated by coefficients of variation for locations (1), additive genetic
variation (2), proportion of genetic variation due to location (3), within-family genetic variance as a
proportion of within-plot variance (4), plot variation (5), within-plot variation (6), and heritability (7) at
the individual seedling level (a /(a + a + af(s)); where afts) is the variance of families within
locations and additive genetic variance is 3af(sJl· See Table 2 for an explanation of trait codes.
among families within locations (and therefore estimates of
additive genetic variance) came from the pooled variance
of many two-tree samples, the variance among families
within locations probably did not differ greatly from loca­
tion to location. The evidence is indirect and the possibility
of type II error is large. The evidence is derived from an
expected relationship among genetic variances when geno­
types of parent trees are estimated by family means. In
estimating genetic variation among trees at a location, only
part of the variation is expressed in differences among family
means; the remainder is expressed in variation within
families. If, therefore, the within-location genetic variation
differs among locations, this difference should be reflected
in within-family variances. In this study, we assumed that
one-third of the additive genetic variance is expressed among
families and two-thirds within families. Given this assump­
tion, additive genetic variance within plots (2 o'f(s )) was
about 240Jo of total within-plot variance (a ) in both envi­
ronments (Table 3, column 4). The remaining variability
within plots is contributed by effects of microenvironment
and interactions among genes in genotypes. These effects
may have contributed "noise" in estimating genetic
variances within families. Unless the sizes of effects and
genetic variances are correlated, real differences in additive
CAN. J. FOR. RES. VOL. !9, !989 1 008
TABLE 4. Matrix of genetic correlation coefficients for correlation among locations (above diagonal) and families (below diagonal) IN-BB2
IN-BB2
!N-BS!
IN-HI
IN-H2
IN-D2
0UT-BB2
OUT-BSI
OUT-HI
OUT-H2
OUT-D2
OUT-BS2
1 .000
0.001
(0. 1 5 )
0.558
(0 . 2 1 )
0 . 1 82
(0. 1 5)
0 . 1 30
(0. 1 6)
0 . 842
(0.08)
0 . 1 96
(0. 1 4)
0. 1 1 3
!N-BS!
IN-HI
IN-H2
IN-D2
0.023
(0. 3 1 )
1 .000
0.280
(0. 33)
0 . 369
-0.366
-0.898
0.623
0 . 1 48
(0. 38)
0 . 39 1
(0. 2 1 )
1 .000
(0. 1 5)
0 . 782
(0. 1 1 )
1 .000
(0. 2 1 )
0 . 928
(0. 1 1 )
1 .049
(0.22)
0 . 225
(0.25)
0.31 1
0. 822
(0.07)
1 . 000
(0.06)
-0.092
0.495
(0. 1 8)
0.590
(0. 1 1 )
0.861
(0. 1 1 )
0 . 609
(0. 1 2)
-0.442
0 . 895
(0. 1 2)
-0.247
(0. 1 6)
0.970
(0.06)
(0.23)
0.490
(0. 1 8)
0 . 886
(0. 1 9)
0.61 1
(0. 1 6)
0. 1 13
(0. 1 6)
-0.256
0.236
(0. 1 5)
0.590
(0 . 1 2)
0 . 1 70
(0. 1 7)
0.1 60
(0. 1 4 )
(0. 1 6)
0.848
(0.07)
(0.33)
0.645
(0. 1 6)
0.520
(0. 1 2)
0.778
(0. 1 1 )
0 .754
(0. 1 1 )
0 . 376
(0.20)
0.232
(0. 1 9)
(0. 1 5 )
0.473
(0. 1 2)
(0.20)
OUT-BB2
OUT-BSl
OUT-HI
OUT-H2
OUT-D2
OUT-BS2
0.477
(0. 38)
1 .008
0 . 1 47
(0.22)
0 . 39 1
0 . 236
(0.29)
0.694
0 . 362
(0.27)
0.315
(0. 29)
1 .060
(0. 1 6)
1 .031
(0. 1 3)
0.936
(0. 1 1 )
0.689
(0. 1 1 )
(0. 1 5)
0.918
(0. 1 0)
0 .784
(0. 1 9 )
-0.387
0.897
(0. 1 1 )
-0. 1 39
0 . 92 1
(0. 1 3)
-0.277
(0 . 1 7)
0 . 889
(0 . 1 4)
0.777
(0. 1 3)
0.994
-1 . 1 90
(0.23)
-0. 9 1 6
(0.27)
1 .000
(0.06)
0.556
(0.21)
0.913
(0 . 1 5)
(0.27)
0.502
0.000
(0.24)
1 .000
(0.20)
0.913
(0. 1 3)
0.9 1 5
(0. 1 5)
0. 1 3 1
(0.20)
0.647
(0. 1 6)
1 .000
(0. 1 1 )
0.795
(0. 1 2)
0 . 809
(0. 1 7 )
-0.082
(0. 1 7)
0.010
(0. 1 3)
0.226
0 . 7 32
(0.09)
0.798
(0. 1 3)
0 . 404
(0. 1 3)
(0.17)
0.001
(0. 1 5 )
0.795
(0 . 06)
(0.08)
0 . 370
(0. 1 4)
(0. 1 6)
-0 . 1 36
(0. 1 5)
0 .264
(0. 1 5 )
0.465
(0. 1 3)
0 .904
(0.05)
1 .000
0.667
(0. 1 1 )
0.602
(0.1 1 )
0 . 368
(0. 1 3)
0.019
(0.20)
0 . 67 1
(0. 1 6)
0.952
(0.06)
0 . 579
(0.19)
0 . 849
(0. 1 2)
0 . 7 33
(0 . 1 8)
-0. 284
(0 . 20)
l.l05
(0.07)
0 . 540
(0. 1 3)
0.867
(0.04)
0.895
(0.07)
1 .000
(0. 09)
0 . 522
0.072
(0. 1 4)
1 .000
(0.15)
NoTE: SE in parentheses. See Table 2 for an explanation of trait codes.
genetic variances among locations may have been masked.
Nevertheless, when within-plot standard deviations (secon­
dary traits) were calculated for each plot for lO primary traits
and plot values were analyzed in the classification model
(Table 1, but omitting within-plot components), the standard
deviations differed among locations only for 1st-year bud
set in the outside environment (P
<0.01). In this case,
the difference may have been caused by starting seedlings
inside a greenhouse and moving them outside during the
growing period.
Almost three-quarters of the variation in seed weight
occurred among trees within locations (Table 2). Correla­
tions between seed weight and final height were as follows:
among all families, 0.256 (P(r
0)<0.01); among families
within locations, 0.141 (P(r
0)>0.05); and among loca­
tions, 0.478 (P(r = 0)< 0.01). The last two relationships
expressed as predictions of height (em) from seed weight
(mg) yielded regression coefficients of b1
1.1012 and
b2 = 4.508 for families within locations and among loca­
tions, respectively (P(b1 = b2)<0.01). In other words, for
each unit of seed weight, the predicted effect on height was
about 4.5 times greater at the location level than at the
within-location level.
Variances within plots were calculated for only 10 of the
12 traits because of an oversight. Of total variance among
seedlings in these traits, variation within plots accounted for
66.7o/o (percentage is the average of 10 traits), locations for
6.6%, families within locations for 7.8%, and plots for
18.9% (Table 2). Because location coefficients of genetic
variation (Table 3, column 1) were smaller relative to addi­
tive coefficients of variation (Table 3, column 2) inside the
greenhouse than outside, location variation was a smaller
proportion of total genetic variance in the greenhouse than
outside: 18 vs. 27% (Table 3, column 3). Plot and within­
plot coefficients of variation were larger in the greenhouse
than outside (Table 3, columns 5 and 6). But because the
additive genetic variance was also larger in the greenhouse
TABLE 5. Loadings and eigenvalues of principal
components (PC!, PC2) and the percentage of
location variation accounted for by the principal
component
Loading of
PC!
Trait
IN-BB2
IN-BSI
IN-HI
IN-H2
IN-D2
OUT-BB2
OUT-BSl
Loading of
PC2
-0. 1 6
0.72
0.96
0 . 22
0 . 87
0 . 95
0 . 96
0 . 30
0.95
0.40
-0. 1 2
0. 1 6
0.74
-0.36
0 . 38
OUT-D2
0 . 86
1 .00
0 . 86
OUT-BS2
0.90
0.31
7 . 39
67 . 2
2 . 35
21.3
OUT-HI
OUT-H2
Eigenvalue
Ofo of variation
NoTE: See Table
2 for
0.01
0.05
an explanation o f trait codes.
than outside, the average heritability of traits was 0.25 in
both environments (Table 3, column 7).
Genetic correlations at the location level apparently were
stronger than those at the family level, although standard
errors of individual coefficients were sometimes large
(Table 4). Absolute values for 47 of 55 correlations among
traits for locations exceeded values of corresponding cor­
relations for families by an average of 8%. The size dif­
ferences occurred mainly in the outside environment, where
the average of absolute values was 0.58 for sources and 0.34
for families. Within the greenhouse, corresponding averages
were 0.50 and 0.51.
The correlation matrix at the family level summarizes the
genetic variation and covariation (in 11 traits) among trees
CAMPBELL ET AL.
19
19
Aspect
....
... ,...
....
...
,...
(.) 18
(.) 18
fl.
....
... Q)
...0 17
()
f/)
...
14
3
-1
5
7
fl.
....
...
Q)
...0
17
f/)
16
()
l
16
0
() 15
I'G
LL
...
....0()
I'G
LL
FIG. 2. Factor scores of the first principal component as
predicted on the north transect (see Fig. 1). Trends are for points
on steep slopes (800Jo) at elevations of 500 m, for three aspects.
West-east distance is measured from the baseline. The vertical bar
is one standard deviation (aA1) of within-location additive genetic
----r--.::S�o�ut�h�t� ansect
120
0
240
360
480
600
Elevation (m)
FIG. 4. Predicted factor scores of the first principal component
as influenced by elevation. Trends are for points on steep slopes
(80%) and east aspects. The north and south transects represent
the midway points of the transects. The vertical bar is one standard
deviation
(aA1)
of within-location additive genetic variation.
19
19
Aspect
(.) 18
fl.
Q) 17
0
()
f/) 16
...
I'G
LL
transect
14
variation.
...
....0()
North
15
9
West-east distance (km)
....
...
,...
1009
....
... ,...
(.) 18
...0Q)
17
f/)
16
()
...0
80%
1
() 15
I'G
LL
14
15
14
8
12
10
15
17
0
West-east distance (km)
FIG. 3. Factor scores of the first principal component as
predicted on the south transect (see Fig. 1). Trends are for points
on steep slopes (80%) at elevations of 500 m, for three aspects.
West-east distance is measured from the baseline. The vertical bar
is one standard deviation
variation.
(aAI)
of within-location additive genetic
within locations. Second-year bud set in the greenhouse
(IN-BS2) was not included in the matrix; several estimates of
correlation of IN-BS2 with other traits were excessively large
at the location level and created uninterpretable eigenvalues
in the principal component analysis. The matrix at the
location level represents the remaining genetic variation and
covariation after subtraction of error and genetic effects
specific to trees within locations. The location matrix,
therefore, summarizes the genetic attributes associated with
geography; it was used as data for the principal component
analysis. All subsequent references to variation in factor
scores, whether among locations or within locations, apply
to factor scores calculated from eigenvectors of the location
matrix.
Most of the variation among locations was consolidated
in the first two principal components of the location cor­
relation matrix. The first principal component accounted
for about 67o/o of location variation in all traits (Table 5).
Loadings (the correlations of original variables with factor
scores) indicated that larger seedlings with later bud-set dates
contributed to larger factor scores for this component. The
second principal component accounted for an additional
72
144
216
288
360
Aspect (degrees azimuth)
FIG. 5. Influence of aspect on predicted factor scores of the
first principal component. Trends are for points 4 km east of the
baseline on the north transect at an elevation of 500 m. The vertical
bar is one standard deviation
genetic variation.
(aA1)
of within-location additive
2 1% of total location-variation. Families with later bud burst
in the 2nd year had larger factor scores for this component.
When factor scores were calculated for each of the 208
families, about 50% of the variation in scores was associated
with location of parents (Table 2). Differences among
parents within locations accounted for the remaining varia­
tion. Part of the variation among locations followed gra­
dient trends on the island; results are presented first for
factor scores of the first principal component. A regression
equation, chosen as "best" from the preliminary model,
explained 22% of the sums of squares in factor scores of
208 families. Much of the unexplained variation can be
credited to differences between the two parents that were
sampled at 94 of the 1 14 locations. But some location
averages deviated from the regression surface more than
would have been expected given the variation within loca­
tions (Table 6, significant lack of fit). For lack of fit to have
been judged nonsignificant (for example, lack of fit,
P > 0. 10), the equation would have had to account for 38%
of total sums of squares.
The chosen model indicated trends in location variation
from north to south and west to east on the island. Locations
CAN. J. FOR. RES. VOL. 19, 1989
1010
TABLE 6. Regression analysis (by backward elimination in a 13-term preliminary model) of factor scores from
principal components
PC2
PC1
Variable a
D
D2
L
D2L
Standard
Partial
coefficient
0.1891
0.3711E-02
0.8747E-01
E2
Cos A
T sin A
Constant
coefficient
p
0.6754E-04
-0.3360E-02
0.5998
0.1099E-01
15.0924
<0.000
1.39
<0.000
<0.000
<0.017
<0.000
<0.002
<0.001
-1.28
0.63
0.34
-0.31
-0.27
-0.24
Variable a
£2
s
D
Constant
Partial
coefficient
b
0.3879E-02
0.3635E-01
0.1279E-01
13.4227
Standard
p
<0.000
<0.023
<0.061
coefficient
0.45
0.15
0.12
<0.000
<0.000
NoTE: Probability of lack of fit for PC! is < 0.001; R1 = 0.22; probability of lack of fit for PC2 is < 0.24; R1 = 0.31.
"D = distance (in units of 0.4 km ) east of the line between rges. 79 and 80 in tp. 60 S; L
distance (in units of 0.4 km ) north of the
line between tp . 60 and 61 S; E
elevation in units of 30.47 m; A = azimuth of aspect in radians; T = slope in percent; and S = slope
in units of 241.5/T.
b-0.3879E-02
-0.3879 X l0-1.
......
14
C\1
u
a..
f1)
..
0 13
u
!/)
..
0
u
tQ
LL
....
12
0
FIG.
120
240
360
480
600
Elevation (m)
6. Influence of elevation on factor scores of the second
principal component as predicted for a point at the baseline on
the north transect. The vertical bar is one standard deviation (aA2)
of within-location additive genetic variation.
with larger factor scores (larger seedlings and later bud set)
occurred in the eastern part of the northern transect (Fig. 2)
and in the western part of the southern transect (Fig. 3).
Factor scores decreased slightly with increasing elevation
(Fig. 4). On steep slopes, factor scores were largest on
aspects slightly south of west and smallest on aspects slightly
north of east (Fig. 5). On less steep slopes, factor scores were
largest on south slopes and smallest on north slopes (Fig. 5).
Position on the island did not influence the effects associated
with aspect or slope as indicated by lack of interaction
(Table 6).
Additive genetic variation within locations is a biologically
relevant scale for evaluating differentiation among locations
(additive genetic variation in factor scores of the first prin­
cipal component (ai,1) and the second principal component
(ai,2) equals three times the family component of variance
of the respective principal components). In this scale, fac­
tor scores decreased by 0.09 aA1/km along the axis of the
island, northwest to southeast between the midpoint of the
northern transect and the eastern edge of the southern
transect. The decrease was 0.06 a A/km in north-south
direction at 10 km east of the baseline. Factor scores also
decreased from low to high elevations by about
0.12 aA1/100 m. Whether the association of factor scores
with elevation was consistent within the sample area could
not be judged; terms for the interaction of elevation with
other variables were not included in the preliminary model.
The largest predicted genetic differentiation in the first
principal component occurred between north central and
southeastern areas in the sample. The north central areas
in this contrast occupied low-elevation, steep, southwest­
facing slopes. The southeastern areas were at high elevations
on steep, northeastern-facing slopes. Predicted mean factor
in these areas differed by about 2.9 to 3.0 aAI· Only a small
proportion of genotypes, therefore, are expected to be
common to the populations of the two areas.
Because the regression model adds the effects of indexes
of environment, the relative influence of individual indexes
can be judged. Of the difference of 2.9 to 3.0 aA1 between
the two areas, differentiation over the range of elevations
contributed about 0.7 aA1 (Fig. 4), aspect about 0.9 aA1
(Fig. 5), and distance from north central to southeastern
about 1.4 aA1 (Figs. 2 and 3).
A regression model using three environmental indexes
accounted for about 31o/o of variation among families in
factor scores of the second principal component (Table 6).
Factor scores decreased (earlier bud burst) with higher eleva­
tions (Fig. 6). Locations on steep slopes had smaller factor
scores (earlier bud burst) than locations on flatter slopes
(Fig. 6), irrespective of position on the island. Locations
from the western part of the island also had smaller factor
scores; factor scores at locations separated by 10 km from
west to east (not shown) differed by an amount almost
exactly equivalent to the difference between locations on
20 and 80% slopes (Fig. 6).
Of the environmental indexes, elevation contributed the
most toward explaining source variation in the second prin­
cipal component. The difference in factor scores at high and
low elevations on the island, however, was only about 0.85
aA2 (Fig. 6).
Discussion
Falkenhagen (1978) hypothesized hierarchical clinal
gradients to account for macrogeographic variation in Sitka
spruce. The hierarchy of clines apparently extends down to
the microgeographic scale. In macrogeographic studies, the
common environmental indexes are longitude, latitude, and
elevation. On Mitkof Island, these were not enough to
CAMPBELL ET AL.
account for source variation which was also influenced by
position on the island, slope, and aspect. Furthermore, gra­
dients depended on the part of the genotype being evaluated
and were different for growth traits and bud burst, for
example. This instance of microgeographic variation in the
species is probably not an isolated case. Yeh and Rasmussen
(1985) report significant genetic variation among stands of
Sitka spruce on northwestern Vancouver Island.
A clinal model using our site variables did not account
for all variation among locations on Mitkof Island. The
regression equation for the first principal component
explained only about half the variation among locations.
The sampling design and analysis procedures may indirectly
account for the poor fit. The design limited the analysis to
a few site variables, so one or more important ones may not
have been measured (e.g., soils). Because we were testing
hypotheses, we did not add variables or complex terms to
the model during analysis. Adding terms, however, probably
would not have reduced the bias; residuals from the model
did not suggest any trends. The unexplained variation among
locations, therefore, could have been associated with uniden­
tified components of environment, but other explanations
are just as feasible. Features of the mating system, stand
history, or genetic drift may have contributed randomness
to location genotypes.
Our model, despite its apparent deficiencies, indicated
trends consistent with our expectations in most respects. We
anticipated finding an association of clinal gradients with
length of growing season. Growing seasons, in turn, were
expected to be shortest at high elevation and on the eastern
side of the island. Genotypes expressed as small seedlings
with late bud burst and early bud set usually originate in
locations with short growing seasons, but some exceptions
occur. Where seasons are severely limited by drought or
cold, early flushing is apparently advantageous, genotypes
from such habitats usually break buds earlier than genotypes
from milder habitats when grown in common gardens
(Campbell and Sorensen 1978; Campbell and Sugano 1979).
In this experiment, genotypes for early bud burst came from
high elevations, as expected (Lines and Mitchell 1966;
Falkenhagen 1977). Genotypes coding for later bud burst
came from lower elevations, flatter slopes, and eastern parts
of the island. Flatter slopes may lead to pooling of cold air
and frost pockets, and growing seasons were expected to
be shorter on the eastern side of the island than on the
western side. Genotypes that coded for small seedlings and
early date of bud set came from the southeastern part of
the island and from eastern sides of mountains. The Stikine
River empties into Frederick Sound east of the southern part
of Mitkof Island, so the southeastern part of the island may
experience more unpredictable cold from continental air
streams than does the northeastern part. This difference
could explain the character of genotypes in the southeast
and on eastern sides of the mountains. It does not, however,
explain the genotypes for small seedlings in the northwestern
part of the sample area (Fig. 2), where we had no reason
to expect short growing seasons. With one exception,
therefore, the gradients on the island appeared to reflect the
expected trends in growing season length.
Gradient trends in variation among locations conceivably
could have any one of several origins: random association
of genes in small populations (genetic drift), gene migration
from founder populations, maternal effects (seed precon­
1011
ditioning), or natural selection. Although it is possible to
envision nonrandom exceptions, genetic drift and migration
are essentially random events with respect to geography. The
mountains on the island were scattered throughout the
sampling area. Chance events (genotypes arising from drift
or migration) on one mountain should not have been con­
nected with chance events on other mountains. Genetic drift
and gene migration, therefore, probably did not contribute
to the gradients indexed by elevation, aspect, and slope.
These gradients reflect a consistent, nonrandom, associa­
tion of seedling performance with attributes of individual
mountains. The directional gradient, on the other hand,
could have resulted from gehe migration in a northwesterly
or southeasterly direction. The gradients of other origin then
could have been superimposed on this main gradient. As
discussed earlier, though, the directional gradient on the
island appeared to be associated with environmental trends,
as were the gradients with elevation, slope, and aspect. It
seems likely, therefore, that the directional and super­
imposed gradients were caused either by maternal effects
or natural selection. Environments in similar positions on
scattered mountains are likely to be correlated, and envi­
ronments could either precondition seeds or underlay natural
selection.
In studies of wild populations, the results of natural selec­
tion are difficult to distinguish from maternal effects. Varia­
tion in seedling size is often associated with maternal factors,
principally weight of seeds (Perry 1976). The nutritional (and
other metabolic) status of seeds may also vary and may influ­
ence seedling size (Rowe 1964; Ries and Everson 1973), but
effects are usually negligible in comparison with those of
seed size (Sweet et at. 1975). To distinguish between natural
selection and maternal effects, environmental influences
must be separated from hereditary ones.
Although seed size is labile, the seed is one of the least
plastic organs on a plant (Palmblad 1968). In conifers, seed
size (and seedling size) has been altered by treatments that
differed among trees (Mergen and Voigt 1960) and within
trees (Sorensen and Campbell 1985). In conifers, seed size
varies by origin of cones within trees (Simak 1960) and
within cones (Wright 1945), and by seed years (Sorensen and
Franklin 1977). But the general homeostatis of seed size
suggests it may be of crucial importance in adaptation. Seed
size varies notably by species, but also among populations
within species and individuals within populations (Khalil
1981, 1986). Seed size is highly heritable (Fehr and Weber
1968) and responds quickly to selection (Christie and Kalton
1960; Draper and Wilsie 1965). Where variation in seed size
does occur, it often is adaptively advantageous (Harper
1977).
In Sitka spruce, as in other species, a correlation exists
between seed size and seedling size. The correlation appar­
ently has been induced by natural selection as a correlated
response; that is, at some locations, selection for increased
seed size and seedling size, for example, has happened con­
jointly. In this study, the correlation of the two traits
between locations was qualitatively different from the one
within locations: the predicted effect of an increase in seed
size on seedling size was much greater between locations than
within locations. In the absence of location effects, the cor­
relation almost disappeared. The correlation was small or
negligible despite two factors: half of the variation in seed­
ling size and most of the variation in seed weight was
CAN. J. FOR. RES. VOL. 19, 1989
1012
expressed among trees within locations. Any variation in
seed size that occurred between locations, therefore, also
occurred within locations. This included the variation caused
by crop size, crown position, cone position, and the many
other environmental factors that can affect seed size in wild
populations. A small correlation between seed and seedling
size, despite the large range of variation in each trait,
suggests that maternal effects played a minor role in deter­
mining seedling size in this study.
In the absence of significant maternal effects, natural
selection is the most likely cause of the gradients among loca­
tions on the island. A seed transfer even at constant elevation
on this small island, therefore, may introduce risk in
reforestation. A transfer inland from the coast entails the
risk that transferred genotypes may not be as productive as
indigenous genotypes. A transfer from inland to the coast
increases the risk of damage by frost in late spring or early
fall. In either case, any additional transfer in elevation or
aspect adds to the risk.
Although rotation-length field tests are necessary to
estimate absolute risk in seed transfer, relative risk can be
calculated by procedures given elsewhere (Campbell 1986,
1987). If transferred and indigenous seed lots differ in the
mixture of genotypes they contain, some genotypes in the
transferred lot may not be adapted to planting site. Calcula­
tions comparing mixtures of genotype indicate that 38, 68,
and 870Jo of seedlings may be poorly adapted in transfers
between areas separated, respectively, by 1, 2, and 3aA l ·
Figures 2-6 suggest that transfers leading to large propor­
tions of poorly adapted seedlings might easily occur on the
island.
Even though risks exist for seed transfer on Mitkof Island,
small seed zones may not be necessary in southeastern
Alaska. Genetic differentiation may be far greater between
the edge and the center of one island, for example, than
between the edges of widely scattered islands. The results
do have a bearing, however, on seed-transfer guidelines for
Sitka spruce in Alaska. Aspect and slope as well as eleva­
tion and latitude apparently must be heeded in developing
the guidelines. Whether the effects of west-east direction
or of aspect are consistent among islands will require further
study.
Acknowledgements
We thank Mr. Dennis Murphy, U.S. Forest Service,
Washington, DC, for cheerfully and efficiently expediting
the cone collections for our confusing design, and Mr. Wenjin
Li, Peking College of Forestry, People's Republic of China,
for computer applications during analysis. We also thank
Dr. John Alden, Dr. Don Fowler, Dr. Don Lester, Dr.
Cheng Ying, and two anonymous referees for their helpful
comments in review.
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