ForestSaence, Vol 37, No 4, pp 973-986
Soils, Seed-Zone Maps, and
Physiography: Guidelines for Seed
Transfer of Douglas-Fir in
Southwestern Oregon
ROBERT K. CAMPBELL
ABsTRACT. One procedure for guiding seed transfer is to partition the species habitat into zones
within which there is little genetic variation from location to location. This report corn­
pares soil types and the existing regional seed zones as bases for classifying geographic
genetic variation into zones. The data used were genotypic values of 135 Douglas-fir
(Pseudotsuga menziesii [Mirb.] Franco) trees from 80 locations in a 15,000 krn2 area in
southwestern Oregon. Genotypic values were estimated by measuring traits expressing
phenology and growth potential of 2-year seedlings in a common-garden experiment.
Neither the soils model nor the seed-zones model satisfactorily classified geographic
genetic variation (significant lack of fit). When physiographic variables (latitude, longi­
tude, elevation, etc.) were added to the models, the added variables accounted for 16%
to 23% of the total variation among locations. Gradients of genetic variation therefore
exist within soil types and present seed zones. These gradients reflect the overall
gradients of geographic variation with latitude, longitude, and elevation that occur in the
region. The results suggest that zones constructed from a physiographic model may
explain more of the genetic variation in southwestern Oregon than do either soils or
seed-zones models. FoR. Sci. 37(4):973-986.
ADDITIONAL KEY WORDS. Pseudotsuga numziesii, adaptation, ecogeographic center­
genetic variation, geneology.
o
T
DELIMIT SEED ZONES, a region is sometimes mapped into compartments
of homogeneous environments as indicated by geographic genetic variation
in an indigenous species (Eneroth 1926, Langlet 1945). Geographic vari­
ation of Douglas-fir in southwestern Oregon (Hermann and Lavender 1968, White
1981, Sorensen 1983, Campbell 1986) reflects the topographic and ecological
complexity of the region (Franklin and Dymess 1973). A map of Douglas-fir
variation therefore would be intricate, perhaps as complex as a soils map of the
region. This and other reasons suggest a possible association of Douglas-fir ge­
notypes with soils. If the association is strong enough, a soils map might be a
helpful and inexpensive alternative to other procedures for classifying genetic
variation into zones. In this paper, soil types and the existing regional seed zones
[Oregon (Tree Seed Zones) 1966] are compared for this purpose. A good model
for delimiting seed zones should account for geographic variation with no evidence
for lack of fit to the modeL Lack of fit to soil and seed-zone models is tested herein
by comparison with "pure error" obtained by repeat sampling of genotypes at
some locations. It was evaluated further by examining the effect of adding phys­
iographic indexes of environment (latitude, elevation, etc.) to the models.
SEPTEMBER 1991/ 973
Soils and tree populations might be expected to have similar geographic pat­
terns. Environment drives the evolution of soils Oenny 1941) in a manner anal­
ogous to its function in the evolution of geographic variation in long-lived plants.
Furthermore, tree populations may have evolved to reflect soil properties; such
evolution has occurred in shorter lived plants (Snaydon 1971, Hamrick and Allard
1975). Tree genotypes respond to various soil properties in a manner suggesting
specific adaptations; for example, to acidity (Teich and Holst 1974), aridity (van
Buijtenen 1978), soil chemistry Oenkinson 1974), soil nutrients (van den Dries­
sche 1974, Jahromi et a!. 1976, Bell et a!. 1979, Turner 1979, Maliondo and
Krause 1985, Wayancha and Morgenstern 1987a), and to identical additions of
nutrients to different soils (Wayancha and Morgenstern 1987b). The degree of
adaptation of trees to soils is difficult to anticipate; the correlation of soil type and
geographic variation in conifers has not been studied, except for associations
involving contrasts in disparate soil types (Millar 1989) or in soil parent materials,
namely, ultramafic vs. granitic Oenkinson 1974), and limestone vs. nonlimestone
(Teich and Holst 1974). Other plants growing in contrasting soils can diverge
genetically in remarkably few generations (Snaydon and Davies 1982), and the
divergence can occur on a microgeographic scale (Allard et al. 1972).
Seed transfer in Oregon has been guided for 2 decades by a seed-zone map
prepared by the Western Forest Tree Seed Council [Oregon (Tree Seed Zones)
1966]. The map partitions forest regions into areas ostensibly homogeneous in
physiography and forest type. Several local committees of forest and research
officers established the zone boundaries by consensus. Without having informa­
tion on genetic variation, they based their decisions partly on the phenotypic
characteristics of local forests. Zones were intended to reflect geographically
related adaptive variation, but whether geographic variation is classified, in fact,
by the seed zones has not been studied.
The physiographic model used in this paper comes from a previous analysis of
genetic variation in southwestern Oregon (Campbell 1986). The model describes
geographic variation among sampled populations in the region with no evidence of
lack of fit. It therefore was useful as a guide for seed transfer but had a disad­
vantage in that practical seed zones could not easily be constructed from it. A
model based on soils or on the existing zones would be preferable in this respect.
In this paper, soils and seed-zone models are compared by using the data analyzed
previously in the physiographic model; therefore, many details of experimental
and analytical procedures and results have been reported (Campbell 1986) and are
not repeated here.
PROCEDURES
Comparisons among models are based on estimated genotypic values in a previous
study (Campbell 1986). The procedure for estimating values included five steps:
(1) obtaining seed from 135 Douglas-fir trees at 80 locations sampling southwest­
em Oregon, (2) growing progeny of these trees in common gardens to estimate
family mean performance for several traits, (3) estimating components of variance
(and covariance) associated with parent-tree location and with trees within loca­
tions for each trait, (4) calculating genetic correlations at the location level among
all combinations of traits, and (5) transforming the original set of traits into a
smaller set of new variables (PCs-principal components) with an estimated ge­
notypic value (factor score) for each parent for each significant PC.
The sample area was roughly square with west and east boundaries 58 and 192
km from the Pacific Ocean. South to north, the area extended 111 km from the
northern California border to the parallel at 43° north latitude. Origin of parent
trees was described by seven location variables: elevation, latitude, distance from
the ocean, vertical height of the main slope, vertical distance to the bottom of the
slope below the parent tree, slope percent, and minutes of sun exposure on April
3 (Campbell 1986). For this paper, origins of parent trees were further classified
by soil series and seed zones.
Soil series were taken from soil inventories (deMoulin et a!. 1975, Wert et a!.
1977). The soils in a series are similar in kind, thickness, and arrangement of
horizons including their structure, color, texture, and other important character­
istics. In heterogeneous regions, soil series often occur individually in areas too
small to delineate separately, so two or more series with a distinctive proportional
pattern are mapped collectively as a soil association (deMoulin et a!. 1975). A soil
series may occur in more than one association. For this study, a parent tree
mapped in a soil association was classified as occurring in the dominant series of
the association. Parent trees, chosen without regard to soils, occurred in 27 soil
series in numbers very nearly proportional to the areas occupied by the series in
the region.
Classification of parent trees by seed zone was taken from the standard seed
zone map [Oregon (Tree Seed Zones) 1966]. Zones were further subdivided into
elevation bands of 305 m (1000 ft) width, the width of bands in present breeding
zones. Parent trees were represented in 21 subzones (elevation band within seed
zone).
To estimate the additive portion of parent-tree genotypes, the offspring result­
ing from wind pollination (families) were planted as newly germinated seed in two
nursery beds. Each family was represented by a 5-seedling row plot in each of 4
replications in a bed (20 seedlings). In one nursery bed, air and soil temperatures
were increased to provide warm temperatures in early spring and late fall. Heating
cables were buried at 15-cm depth and spacing. A polyethylene tent over the bed
created warm air by the greenhouse effect. Temperatures in heated and unheated
beds differed depending on radiation and time of day and year but ranged from ooc
to 10°C in both soil and air. Traits expressing timing of the vegetative cycle and
growth potential (bud burst and bud set, second flushing, height, and diameter)
were measured for two growing seasons in the two beds. Seedling responses
were evaluated as separate traits in the two beds and growing seasons because
genotypes may be expressed somewhat differently in the diverse environments of
beds and years. Each of the resulting 13 traits exhibited statistically significant
genetic variation among parent trees of different origin (Campbell 1986).
Because two parent trees were chosen in 55 of the 80 locations, two sets of
genetic correlations could be estimated. The 13 x 13 matrix of correlations at the
family level summarizes the genetic variation and covariation (in 13 traits) among
trees within locations. The matrix at the location level represents the remaining
genetic variation after subtraction of error and of genetic effects specific to trees
within locations. The location matrix therefore summarizes the genetic attributes
associated with geography; it was used as data for principal component analysis.
All subsequent references to variation in factor scores, whether among locations
SEPTEMBER 1991/ 975
or within locations (i.e., pure error), apply to factor scores calculated from eigen­
vectors of the location matrix. The analysis reduced the complexity of the highly
correlated system of growth and vegetative-cycle traits by extracting two prin­
cipal components, which explained 96% of the variation (in all traits) attributable
to parent location (Campbell 1986). The two factor scores for a parent tree (one
score for each principal component) estimated the genotypic value of the tree.
The first component (PC-1) expressed mainly growth vigor; the second (PC-2)
expressed aspects of growth timing not correlated with seedling size. The smaller
the factor score for PC-1, the earlier the bud set and the smaller the seedling.
Also, the smaller the factor score, the fewer the seedlings that added a second
flush of height growth and the less the variation in height among seedlings within
families. The smaller the factor score in PC-2, the earlier the bud burst and the
earlier the component of bud set which was not expressed in the first principal
component and therefore is weakly correlated with seedling size (Campbell 1986).
The geographic variation in factor scores has been described previously by a
physiographic regression model; details are omitted here. The variables in this
model were quantitative; e.g., elevation, latitude, distance from the ocean, and
other physiographic indexes of environment (Campbell 1986). In this study, vari­
ables were categorical (21 subzones or 27 soil types), and a one-way analysis of
variance with random effects was an appropriate statistical procedure. But one
objective of this study was to determine if the physiographic model explained
variation not explained by subzones or soils, and regression procedures permit
the easy combination of categorical and quantitative variables in a single model.
Data therefore were analyzed by regression. Comparisons of the three models
were based on 15 regression analyses of factor scores for each principal compo­
nent. The first used the physiographic variables of the model selected by Camp­
bell (1986). This set of variables was then included with subzones in an analysis
and again with soils in another analysis. Each of the remaining 12 analyses included
categorical variables and a single physiographic term: elevation(E) + subzones,
latitude(L) + subzones, distance from the ocean(D) + subzones, EL + sub­
zones, ED + subzones, LD + subzones, and a similar set for soils. Analysis of
categorical variables in a regression model requires the use of "dummy" indicators
(Draper and Smith 1966). Lack of fit to models is estimated by deviations of
locations from the regression plane in the physiographic model and by the corre­
sponding terms of variation among locations within subzones or within soils in the
other models. The "pure error" used to test lack of fit is estimated by variation
among trees within locations. To make clear the connections among models, sum
squares as well as mean squares are presented in tables. Null hypotheses were
evaluated by testing the additional reduction in sums of squares caused by adding
a new variable (or set of variables) to the regression model (Snedecor and
Cochran 1967). In models combining physiographic and categorical variables, all
variables were "forced" into the combined models, but coefficients were not fixed.
Elimination of variables by stepwise procedures was deemed inappropriate in view
of the study objectives.
RESULTS
Several physiographic variables and their interaction terms described the geo­
graphic distribution of parent-tree factor scores in southwestern Oregon (Table
976/ FQRESJSCIENCE
1-from Campbell1986). Coefficients in the resulting regression equations were
highly significant and had small standard errors, about 25% as large as coeffi­
cients. As indicated by standardized coefficients, elevation, latitude, and distance
from the ocean explained most of the variation (Table 1).
The physiographic model seemed to account for geographic variation in PC-1;
it explained 68% (R2
0.68) of sums of squares. No evidence existed for lack of
fit to the model (Table 2) given the amount of variation among trees at a location
(pure error). In comparison, the subzones model explained 61% of sums of
squares and the soils model 58% (Table 2); significant lack of fit (Table 2) suggests
that neither model adequately classified the variation in factor scores of PC-1
among locations.
Adding all variables in the physiographic model to the subzones or soils models
added significantly to the explanation of sums of squares (Table 3). On the other
hand, adding the categorical variables to the physiographic model did not improve
the model. The latter models explained more of variation than the physiographic
model alone, but the added amount was not statistically significant for either
subzones or soils (Table 3). The size of mean squares for lack of fit in the
combined model did not change (Table 3, soils) from that in the physiographical
model (Table 2), or it increased enough (Table 3, subzones) to render lack of fit
statistically significant.
For PC-2, physiographic, subzone, and soil models each accounted for 45% to
48% of sums of squares (Table 2), but a statistically significant lack of fit (Table
2) indicated that subzones and soils models explained only part of the variation
among locations. Addition of physiographic variables to subzones and soils im­
proved the models significantly. For subzones the amount of variation explained
was increased from 48% to 60%, and for the soils model, from 45% to 60%. In
both models, mean squares for lack of fit were decreased substantially and were
no longer significant (compare Table 2 and Table 4).
The improvements in models obtained by adding physiographic variables in the
above analyses indicated gradients in genotypic value that were not accounted for
by either subzones or soils. These gradients may be associated with major phys­
iographic variables-elevation, latitude, distance from the ocean-or some com­
bination of these or with factors of local topography, such as slope or aspect. Only
the major physiographic variables were examined here by adding each variable
individually (or in an interaction term) to the subzones (or soils) model. The
contribution of this added variable was tested, and the regression coefficient
interpreted. The regression coefficient measured the average or expected change
in a tree's genotypic value (factor score) when the physiographic variable in­
creased one unit, given no change in category (subzone or soil series) of the tree's
location. The coefficient therefore estimated the trend of change in genotypic
value within subzones (or soil series). The trend was conditional on the classifi­
cation used; it was not necessarily consistent from model to model or with overall
trends within the region.
The genetic gradients existing within subzones and soils models were quite
similar from model to model. The trends of gradients within zones can be ascer­
tained from the regression coefficients in Table 5. Within the average subzone or
soil series, the farther north a parent's origin, the more vigorous its progeny and
the longer the growing season of its progeny; that is, the larger the factor scores
for PC-1 (Table 5). Furthermore, the higher the elevation of origin, the smaller
=
SEPTEMBER 1991/ 977
<.0
-..:1
I
!
TABLE 1. Regression analyses of factor scores from principal components (from Campbell 1986). Principal component 1
Variable•
E
w
VE2
DE2
ED
PV2
EL
D
DP
CONST
Partial
coefficient
Significance
P< ...
Principal component 2
Standard
coefficient
0.5470£-01
0.002
33.53
0.8847£-01
0.000
39.44
0.6821£-10
0.000
0.46
-0.6983£-08
0.000
-2.786
0.7302£-04
0.000
5.13
-0.5741£-09
0.000
-0.37
-0.1358£-02
0.002
-34.90
-3.9728
0.000
-42.16
0.027
0.51
0.5 19£-04
21.4381
Variable"
E
ED2
EDT
ET
DV
ED
DVP
DP
CONST
Partial
coefficient
Significance
P< . ..
Standard
coefficient
0.38888£-02
0.000
4.33
0.2973£-06
0.000
4.46
0.2662£-04
0.001
1.44
-0.1832£-02
0.001
-1.48
0.6842£-04
0.000
2.83
- 0.6814£-04
0.000
-8.72
- 0.1032£-06
0.000
-3.07
0.6881£-04
0.000
1.13
2.5275
0.004
0.000
Probability of lack of fit for PC-1 is 0.055; R2 = 0.68. Probability of lack of fit for PC-2 is 0.090; R2 = 0.48. • Where E = elevation in feet (0.3047 m), L = latitude in degrees, D = distance from the ocean in miles (1.609 km), P = sun exposure in minutes exposed
to direct sun on April 3, V = vertical height of the major slope in feet (0.0347 m), T = slope in degrees/45, and CONST = constant.
TABLE 2.
Regression analyses in physiographic, subzones, and soils models of factor scores in the first principal component (PC-1) and
the second principal component (PC-2).
Model
--
Physiographic
Source
Sum
squares
d.f.
Subzones
Mean
squares
Sum
squares
d.f.
Soils
Mean
squares
d.f.
Sum
squares
Mean
squares
PC-1
Total
Regression
Residual
134
446.96
9
304.15
(125)
33.79**
134
446.96
134
446.96
20
273.35
13.67**
26
259.66
9.99**
(108)
187.31
1.73
142.81
1.14
( 1 14)
173.62
1.52
2.12**
53
138.57
0.89
55
48.72
Lack of fit
70
94.08
1.34
59
124.88
Pure error
55
48.73
0.89
55
48.73
R2
=
R2
0.681
=
0.612
R2
=
2.61 **
0.89
0.581
PC-2
Total
134
135.25
8
65.57
8.20**
(126)
69.69
0.55
Lack of fit
71
45.05
0.63
Pure error
55
24.63
0.45
Regression
Residual
U'l
t'l
t'l
::<:1
......
18
......
..._
<.0
....:]
<.0
R2
*
=
0.05< p
>
0.01; **
=
=
0.485
p< 0.01.
134
135.25
20
65.42
3.27**
69.83
0.61
59
45.20
0.77**
55
24.63
0.45
(114)
R2
=
0.484
134
135.25
26
61.07
2.35**
74.18
0.69
53
49.55
0.93**
55
24.63
0.45
(108)
R2
=
0.452
TABLE3. Regression analysis of factor scores of the first principal component (PC-1).a Model
Physiographic
and subzones
Physiographic
and soils
Source
d.f.
Sum
squares
Mean
squares
d.f.
Sum
squares
Mean
squares
Total
Regression
Regression partitioned: (1) Subzone (or soil)
contribution
Physiographic after above
or
(2) Physiographic
contribution
Subzone (or soil)
after above
Residual
Lack of fit
Pure error
134
(29)
446.963
321.672
11.09**
134
(35)
446.963 339.304
9.69** 20
9
273.346
48.326
5.37**
26
9
259.656
76.648
8.85**
9
304.153
9
304.153
26
(99)
44
55
R2
35.151
107.659
58.926
48.733
0.760
*
=
0.05< p
>
0.01; **
20
(105)
50
55
R2
=
=
17.519
125.291
76.558
48.733
0.720 0.88
1.19
1.53*
0.89
=
1.35
1.09
1.34
0.89
p< 0.01.
• Test of the significance of the added sums of squares explained by (1) adding variables of the
physiographic model to other models or (2) adding variables of the subzones or soils models to the
physiographic model.
the factor score; or the farther east and north, the greater the decrease in factor
scores with increasing elevation (Table 5). In addition, the subzones model inad­
equately accounted for a gradient in factor scores of PC-1 that decreased from
west to east and did so more rapidly in the northern part of the region (Table 5)
than in the southern. Within a given subzone or soil series, the higher the ele­
vation of the parent's origin, the later the vegetative period of its seedling prog­
eny; that is, the larger the factor scores for PC-2 (Table 5). The farther east and
north the parent's origin, the more pronounced was this trend.
The trends suggested above for gradients within zones seem to mirror
the overall trends within the region as described by the physiographic model
(Campbell 1986). Coefficients in Tables 1 and 5 cannot be compared, however,
because those in Table 1 depend strongly on other physiographic variables. Co­
efficients therefore were compared from models having all physiographic variables
included with subzones or soils. The resulting coefficients for physiographic vari­
ables indicated that trends within categories (combined models) were indeed
similar to overall trends (physiographic model alone) with one exception; coeffi­
cients were almost an order of magnitude smaller in the combined soils and
physiographic model for the analysis of factor scores of the second principal
component (Table 6). Some changes in sign were even included in the difference
between this and other models.
The physiographic gradients within subzones of this study accounted for 11% of
variation in PC-1 among all parent trees in the sample as indicated by the reduc­
980/FQRESJSCIENffi
TABLE 4.
Regression analyses of factor scores of the second principal
component (PC-2).3
Model
Physiographic
and subzones
Physiographic
and soils
Source
d.f.
Sum
squares
Mean
squares
d.f.
Sum
squares
Mean
squares
Total
Regression
Regression partitioned: (1) Subzone (or soil)
contribution
Physiographic after above
or
(2) Physiographic
contribution
Subzone (or soil)
after above
Residual
Lack of fit Pure error
134
(28)
135.251
80.639
2.88**
134
(34)
135.251 81.433
2.40** 20
8
65.420
15.219
1.90**
26
8
61.068
20.365
2.55**
8
65.565
8
65.565
*
=
0.05
<
p
>
0.01; **
20
(106)
51
55
R2
=
p
<
=
15.074
54.612
29.980
24.632
0.596
0.75
0.52
0.59
0.45
26
(100)
45
55
R2
=
15.868
53.818
29.186
24.632
0.602
0.61
0.54
0.65
0.45
0.01.
• Test of the significance of the added sums of squares explained by (1) adding variables of the
physiographic model to the other models or (2) adding variables of the subzones or soils models to the
physiographic model.
tion in sums of squares assigned to physiographic variables in the subzones model
(Table 3, 48/447
0.11). If the physiographic model, by itself, is assumed to
have accounted for the variation among all locations, then16% of that variation has
occurred along gradients within subzones (Tables 2, 3; 48/304
0.16). For
PC-2, 23% of variation among locations occurred on gradients within subzones
(Tables 2, 4; 15/66
0. 23). The gradients followed latitude, longitude, and
elevation (Table 5) and perhaps other physiographic factors of less importance
(Table 6).
=
=
=
DISCUSSION
The degree of adaptation to soil properties by Douglas-fir is difficult to assess. In
any study of association between genotype and habitat, characteristics of both
attributes must be estimated, thus introducing error. When habitats are catego­
rized, there are added questions connected with mapping procedures. In this
study, for example, were the soil categories mapped relative to all soil properties
that are adaptively important for Douglas-fir? How accurately were categories
mapped; that is, were all parent trees correctly assigned to categories? In this
study, the physiographic model appeared to provide a better index of genotypes
than did categories of soils. But soils and genotypes evolve and can be expected
SEPTEMBER 1991/ 981
I
TABLE 5.
Tests of the effect of adding individual physiographic variables to soils or subzones models.
Physiographic variable added to subzones model (d.f.
=
Physiographic variable added to soils model (d.f.
21)
PRINCIPAL COMPONENT-I
Eb
L
D
EL
ED
LD
PRINCIPAL COMPONENT-2
E
L
D
EL
ED
LD
27)
Physiographic variable
Physiographic variable
Code•
=
Code"
Regression
coefficient
Significance
p
...
Regression
sum of
squares
284.192
290.834
281.017
283.361
288.871
280.468
Eb
L
D
EL
ED
LD
-0.82E-03
2.93
-0.64E-02
-0.19E-04
-0.96E-05
-0.86E-04
0.05
0.00
0.66
0.00
0.00
0.80
299.786
300.681
259.999
299.003
292.322
259.771
74.738
65.606
65.464
75.159
67.919
65.469
E
L
D
EL
ED
LD
0.54E-03
-0.25E-01
0.71E-02
0.13E-04
0.59E-05
0.21E-03
0.00
0.95
0.50
0.00
0.00
0.44
78.252
61.070
61.490
78.607
73.708
61.486
Regression
coefficient
Significance
p
...
Regression
sum of
squares
-O.UE-02
2.47
-0.26E-01
-0.25E-04
-0.95E-05
-0.59E-03
0.01
0.00
0.02
0.01
0.00
0.01
O.lOE-02
0.25
0.19E-02
0.24E-04
0.38E-05
0.49E-04
0.00
0.58
0.79
0.00
0.04
0.78
Where E = elevation in feet (0.3047 m), L
latitude in degrees, D = distance from ocean in miles (1.609 km).
b Each line represents a separate regression model in which the physiographic variable was added individually to subzones or soils models as E + soils, L +
soils . . LD + soils, etc.
a
=
.
.
TABLE 6. Regression coefficients of physiographic variables in three models. Principal component 1
U)
tzl
Variable•
Physiographic
only
Subzones +
physiographic
Soils +
physiographic
E
LD
VE2
DE2
ED
PVZ
EL
D
DP
CONST
0.55E-01
0.88E-01
0.68E-10
-0.70E-08
0.73E-04
-0.57E-09
-0.14E-02
-3.97
0.57E-04
21.44
0.32E-01
0.55E-01
0. 77E-10
-0.74E-08
0.67E-04
-0.63E-09
-0.81E-03
-2.56
0.58E-04
21.27
0.6lE-01
0.98E-01
0.62E-10
-0.64E-08
0.69E-04
-0.62E-09
-0.15E-02
-4.37
0.51E-04
20.41
$
......
..._
ffi
Variable"
Physiographic
only
Subzones +
physiographic
Soils+
physiographic
E
ED2
EDT
ET
DV
ED
DVP
DP
CONST
0.39E-02
0.30E-06
0.27E-04
-0.18E-02
0.68E-04
-0.68E-04
-O.lOE-06
0.69E-04
2.53
0.32E-02
0.14E-06
0.16E-04
-0.11E-02
0.74E-04
-0.39E-04
-O.llE-06
0.52E-04
1.55
0.51E-03
-0.26E-07
-0.68E-05
0.27E-03
0.27E-04
0.53E-05
-0.43E-07
0.13E-04
5.01
Where E = elevation in feet (0.3047 m), L
latitude in degrees, D
distance from the ocean in miles (1,609 km), P
vertical height of the major slope in feet (0.0347 m), T
slope in degrees/45, and CONST
to direct sun on April 3, V
•
=
=
tzl
::<:1
......
Principal component 2
=
=
=
=
sun exposure in minutes exposed
constant.
to be correlated with physiographic variables. Are physiographic variables there­
fore potential indexes of adaptively important soil properties and better, perhaps,
than the variables presently used in soil classification? Answers to these questions
are not available but might explain why 27 categories of soil did not adequately
account for the variation among genotypes at 80 locations. Twenty-one subjec­
tively delineated subzones did as well in this respect. Soils, on the other hand,
explained more of genetic variation (PC-1
58%, PC-2
45%) than any single
physiographic variable. Based on simple correlations of physiographic variables
with factor scores, latitude accounted for the largest amount of variation in PC-1
(45%) and elevation for the largest amount in PC-2 (39%).
Gradient trends of genetic variation existed within subzones and soils, and the
trends reflected mainly the overall genetic gradients within the region. The clas­
sification models apparently cannot account for the steepness of the genetic gra­
dients with latitude, elevation, and distance from the coast, which in turn follow
gradients of precipitation and temperature (Campbell 1986). Plant communities
are highly responsive to moisture and temperature, especially when these factors
are limiting for many species (Franklin and Dyrness 1973), as they are in parts of
southwestern Oregon. Categorizing areas by plant community, as well as by soil
series, therefore might produce a better regional model than does either model
tested here. Plant communities and genetic variation in Douglas-fir are not closely
associated, however, in northern Idaho (Rehfeldt 1979) or in central Oregon
(Campbell and Franklin 1981).
Foresters commonly attempt to minimize the potential maladaptation caused by
seed transfer in one of two ways: (1) by constructing guidelines to suggest the
risk involved in transfer of a specific seed lot from one site to another, or (2) by
constructing zones within which any transfer involves risk less than some accept­
able maximum. The physiographic model serves well for the first purpose in
mountainous regions, as in northern Idaho (Rehfeldt 1983) and in southwestern
Oregon (Campbell 1986). The physiographic model used here may be better than
subzones or soils models for the second purpose too, provided that extrapolation
beyond the sample area in southwestern Oregon is not attempted. Gradients that
account for a significant fraction of geographic variation exist within present sub­
zones. Zones constructed from a physiographic model therefore may explain more
of genetic variation with fewer categories than either the subzones or soils mod­
els. This hypothesis was not tested because the complexity of the present phys­
iographic model makes constructing zones difficult and expensive for Douglas-fir
in southwestern Oregon.
The hypothesis does not agree with conclusions recently reported by Loopstra
and Adams (1989). They found little adaptive differentiation along latitudinal,
longitudinal, or elevation gradients within breeding zones. This clear difference in
results from the two studies is not readily explained. Both studies sampled the
same region. The breeding zones sampled by Loopstra and Adams were, on
average, somewhat larger than the subzones sampled in this study, and bound­
aries of the two zone-types only partially coincided. These differences were not
large enough to account for the results, though. One possible explanation is the
ages at which seedlings were measured: 1 year in the Loopstra-Adams study and
2 years in this study. In common-garden experiments at the Forestry Sciences
Laboratory in Corvallis, Sorensen (personal communications) and I have observed
that the ratio of variation among locations to variation within locations tends to
=
984/ FQRESTSCIENCE
=
increase with seedling age. This has occurred in Douglas-fir, pine species, and
true firs. A 1-year experiment therefore may not provide a representative char­
acterization of variation among locations, perhaps because important adaptive
differences first appear in the distribution of predetermined and free growth (Kaya
et al. 1989), and predetermined growth does not occur until the second
year. Therefore, first-year traits and those from later years may be imperfectly
correlated, and weakly correlated traits may follow different gradients with phys­
iographic variables or within zones (compare PC-1 vs. PC- 2, this paper). In true
firs, gradient trends were changed remarkably from the first to the third years
(Sorensen et al. 1990).
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Copyright 1991 by the Society of American Foresters
Manuscript received December 18, 1989
AUTHOR AND ACKNOWLEDGMENTS
The author is Research Geneticist, USDA Forest Service, Pacific Northwest Research Station,
Forestry Sciences Laboratory, 3200 Jefferson Way, Corvallis, OR 97330.
986/ FQRESJSCIENCE