United States

Department

of Agriculture

Forest Service

Rocky Mountain

Research Station

General Technical

Report RMRS-GTR-134

August 2004

Interspecifi c and Intraspecifi c 

Variation in Picea engelmannii 

and its Congeneric Cohorts: 

Biosystematics, Genecology, and 

Climate Change

Gerald E. Rehfeldt
Abstract

Rehfeldt, Gerald E. 2004. Interspecific and intraspecific variation in Picea engelmannii and its congeneric 

cohorts: biosystematics, genecology, and climate change. Gen. Tech. Rep. RMRS-GTR-134. Fort Collins, 

CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research. 18 p.

A series of common garden studies of 336 populations representing Picea engelmannii, P. pungens, P. glauca, P. 

mexicana, and P. chihuahuana provided as many as 13 growth and morphologic characters pertinent to biosystematics and genecology. Canonical discriminant analyses discretely segregated populations of P. pungens and P. 

chihuahuana while positioning P. engelmannii populations along a continuum anchored by Southwestern United 

States populations at one extreme and those classified as hybrids of P. engelmannii with P. glauca on the other. A 

population of P. mexicana was closely aligned with Southwest populations of P. engelmannii, while populations of 

P. glauca were intermixed with and peripheral to those identified as hybrid. While consistent with most taxonomic treatments of these taxa, the analyses nonetheless suggested that Southwestern United States populations 

should be considered as a variety of P. engelmannii that most likely should include P. mexicana. 

Genecological analyses detected ample genetic variation among the 295 populations in the P. engelmannii complex. The analyses demonstrated that populations were distributed along clines driven primarily by the winter 

temperature regime of the provenance. For northern populations, summer temperatures also became a key factor in accounting for genetic differences among populations. Analyses also detected clines for the 19 P. pungens 

and 23 P. glauca populations. 

An assessment of the effects of global warming according to the IS92a scenario of two general circulation models demonstrated for the current century: (1) an increasingly favorable climate for P. pungens as its distribution 

moves upward in elevation throughout much of the Great Basin, Colorado Rockies, and mountain islands of the 

Southwest; (2) a widespread reduction in the areal extent of P. engelmannii in the inland Northwestern United 

States to the extent that Picea may become rare in the local flora; (3) extirpation of P. glauca from the Black Hills 

and Cypress Hills; and (4) a widespread redistribution of genotypes across the landscape as contemporary populations adjust genetically to change.

Key words: common garden studies, quantitative traits, climatic clines, response to climate

The Author

Gerald E. Rehfeldt is a retired Plant Geneticist with the Rocky Mountain Research Station at the Forestry 

Sciences Laboratory in Moscow, ID. He completed M.S. and Ph.D. degrees by 1968 at the University of 

Wisconsin and has spent 38 years studying genetic variation in Rocky Mountain conifers.

Acknowledgments

The successful completion of these studies was dependent on the excellent technical assistance of S. P. Wells. 

Also, D. T. Lester and D. G. Joyce graciously provided thoughtful criticism of an early version of the manuscript. 

I also thank B. C. Jaquish, N. K. Dhir, and F. T. Ledig for supplying seeds. 
USDA Forest Service RMRS-GTR-134. 2004 1

Introduction

Picea engelmannii Parry ex Engelm. dominates subalpine forests throughout much of the Interior West of 

North America. Yet this broad-ranging species maintains 

such complex inter- and intra-specific relationships 

with its congeneric cohorts that it remains genetically an 

enigma. To the north, hybridization and introgression 

between P. engelmannii and P. glauca (Moench) Voss is 

so prevalent (Daubenmire 1974, Ogilvie and von Rudloff 

1968, Rajora and Dancik 1999, Taylor 1959) that much 

of the Picea of Interior British Columbia is considered 

to be hybrid (Roche 1969). Toward the south, P. engelmannii frequently co-occurs with the montane P. pungens 

Engelm. Although the phenotypic resemblance between 

these species is so close that hybrids have been reported 

(Taylor and others 1975), careful studies have failed to 

uncover incidences of natural hybridization (Ernst and 

others 1990, Kossuth and Fetchner 1973, Mitton and 

Andalora 1981). Of the Picea of Mexico, moreover, one, P. 

mexicana Martinez, is so similar phenotypically (Taylor 

and Patterson 1980, Taylor and others 1994) and 

molecularly (Ledig and others 2004, Sigurgeirsson and 

Szmidt 1993) to P. engelmannii that its nomenclature 

is disputed; yet, a second, P. chihuahuana Martinez, is 

regarded as a distinct taxonomic entity by a unanimous 

consensus of the same authors. 

At the intraspecific level, genetic variation among P. 

engelmannii populations is arranged along geographic 

clines (Rehfeldt 1994). Populations from the mildest 

climates at low elevations tend to have high growth 

potential and low tolerance to the cold, while those from 

the severe environments at high elevation display high 

cold tolerance and low growth potential. The clines are 

sufficiently steep that on average, populations separated by about 400 m of elevation tend to be genetically 

different. Although genetically similar populations tend 

to recur across the landscape presumably in association with the recurrence of similar climates, populations 

from the high mountains of the Southwestern United 

States nonetheless differ from those to the north to such 

an extent that they may deserve subspecific recognition 

(Ledig and others 2004, Rehfeldt 1994). For this paper, 

these southern populations are considered to be the 

species’ southern ecotype (fig. 1) and are referenced as 

southwest P. engelmannii.

This paper deals with the biosystematics, genecology, 

and response to climate change of populations of seven 

taxa: P. engelmannii, southwest P. engelmannii, P. glauca, 

hybrids of P. engelmannii and P. glauca, P. pungens, P. 

mexicana, and P. chihuahuana. The focus, however, is on 

the taxa of the P. engelmannii complex: P. engelmannii, 

southwest P. engelmannii, and hybrid derivatives of P. 

engelmannii with P. glauca. A portion of these objectives 

were dealt with previously for populations of the Central 

Rocky Mountains (Rehfeldt 1994).

The analyses are based on genetic variation in quantitative traits expressed in common gardens. Assumptions 

fundamental to the analyses and interpretations are: 

• Climate is the primary factor controlling the distribution of organisms (Brown and Gibson 1983, 

Woodward 1987). 

Interspecific and Intraspecific Variation in 

Picea engelmannii and its Congeneric Cohorts: 

Biosystematics, Genecology, and Climate Change

Gerald E. Rehfeldt

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Figure 1—Distributions of Picea engelmannii, P. pungens, P. 

mexicana, and P. chihuahuana; the southwestern portion of the 

transcontinental range of P. glauca; the zone of hybridization 

and introgression between P. engelmannii and P. glauca; and the 

approximate boundary between P. engelmannii and southwest P. 

engelmannii. Numbered localities are referenced in the text and 

span the range of populations sampled.
2 USDA Forest Service RMRS-GTR-134. 2004 

• Niche space is a multidimensional hypervolume 

(Hutchinson 1958) whose limits are controlled by a 

balance between climatic tolerances and competition 

(Arthur 1987).

• Genotypes tend to occur in those environments where 

they can be competitively exclusive (Arthur 1987, 

Miller 1967).

• Climatic limits of distribution remain constant during 

periods of climate change (Box and others 1999). 

The same principles controlling the distribution of 

species also operate intraspecifically to control the 

distribution of genotypes within populations (Rehfeldt 

and others 1999).

Methods

Experimental Materials

Seeds that had received natural wind pollinations were 

obtained from 336 populations, 200 P. engelmannii, 32 

southwest P. engelmannii, 63 P. engelmannii – P. glauca 

hybrid derivatives, 23 P. glauca, 16 P. pungens, and one 

each of P. mexicana and P. chihuahuana. Identification 

of all taxa except southwest P. engelmannii was based 

on taxonomic characters of the parental populations. 

In the experience of the author, distinguishing mature 

P. pungens and P. glauca from P. engelmannii is unambiguous, but distinguishing hybrids from either P. glauca

or P. engelmannii is extremely difficult. No phenotypic 

keys exist for southwest P. engelmannii, the distribution of 

which is based on the research results of Rehfeldt (1994). 

The intent of this sampling was to thoroughly represent the ecologic and geographic distribution of the 

P. engelmannii complex from 32 oN to 54 oN latitude; 

provide a representative sample of the P. pungens 

distribution; sample enough P. glauca in Western North 

America to provide a bridge to P. engelmannii; and 

simply include P. mexicana and P. chihuahuana. Most of 

the seeds were obtained from squirrel caches in native 

populations, but those from Canada were from reforestation stores of either the British Columbia Ministry of 

Forests or Alberta Sustainable Resource Management. 

The procedures customarily used to procure these seeds 

assured that each population was represented by a large 

number of parental trees.

Experimental Procedures

Genetic variation among populations was compared 

in four independent studies:

• Study 1 consisted of 37 populations from Southwestern 

United States. 

• Study 2 consisted of the 103 Intermountain populations of Rehfeldt (1994).

• Study 3 consisted of 135 populations from the Inland 

Northwestern United States.

• Study 4 consisted of 73 populations from Western 

Canada.

This meant that study 1 consisted largely of southwest P. engelmannii; studies 2 and 3 largely of P. 

engelmannii; and study 4 largely of P. glauca and P. 

glauca-P. engelmannii hybrids. To provide a means 

of comparing results of independent studies initiated 

in different years, the studies contained either nine 

or 14 populations in common with one of the studies 

conducted previously. Each taxon was represented by 

one or more of the common populations.

Experimental procedures used for all studies followed 

those of Rehfeldt (1994) and involved assessing differences among populations for (1) patterns of shoot 

elongation during the second growing season in a 

shadehouse-greenhouse complex at Moscow, ID, and (2) 

growth, development, and cold hardiness of 4-year trees 

growing in field tests established at high (1,500 m) and low 

(970 m) elevations near Priest River, ID.

For assessing patterns of shoot elongation, nine 

seedlings representing each population were grown in 

740 cm3 containers arranged in a randomized complete 

block design for each of three blocks. Seeds were sown 

directly into the containers, which were kept in a greenhouse for the duration of the germination period. The 

containers were transferred to a shadehouse (50 percent 

shade) in June for the duration of the first growing 

season but were returned to the greenhouse for the duration of the winter. Winter temperatures were maintained 

above freezing. In March of the second growing season, 

greenhouse temperatures were set at 20 (±4) °C during 

the day, and then were allowed to cool to a minimum of 

10 °C at night with no supplemental lighting.

Periodic measurements were made of shoot elongation during the second growing season; a modified 

logistic model was fit to the elongation of each tree, and 

estimates were made of the initiation, cessation, rate, 

and amount of elongation from the logistic model (see 

Rehfeldt 1994). These variables plus 2-year height were 

then available for assessing genetic differences among 

populations. In assessing patterns of shoot elongation, 

measurements were begun on the day when the greenhouses were warmed, usually March 1.

Field tests were established in a randomized complete 

block design with 10 seedlings representing each population in each of three blocks. The tests were maintained 

for 4 years, during which time data were collected on 

growth, winter injuries, mortality, foliage color, and 

needle length. The tests provided as many as eight variables for assessing population differentiation.

Patterns of shoot elongation were assessed on 

all seedlings in each of the four studies. Field tests, 

however, were established at both elevations for studies 

1 and 2, but the high elevation site was abandoned for 

study 3 because the climate at this elevation was not 

severe enough to injure the northern populations being 

tested. In study 4, populations were compared for only 

the pattern of shoot elongation. These irregularities 

in the experimental approach meant that a different 

assortment of variables was available for assessing 

population differentiation in each of the studies; in fact, 

only the six variables describing shoot elongation were 

available for all populations (table 1). 

In the analyses described below, metric variables 

were transformed to logarithms because variances 
USDA Forest Service RMRS-GTR-134. 2004 3

were proportional to population means. Except where 

noted, all statistical analyses used the algorithms of SAS 

Institute (1985).

Scaling and Joining Multiple Data Sets

To assess genetic variation in response to climate 

across the entire geographic distribution of the P. 

engelmannii complex, population means were corrected 

for the effects of studies. Table 2 illustrates the mean 

values of three variables for a subsample of populations in common among studies. Differences between 

values for the same population represented in disparate 

studies illustrate the necessity of scaling the data before 

combining datasets. Table 2 shows that the effects of 

studies were most apparent for populations of southwest P. engelmannii, but that the ranking of populations 

remained constant between studies despite the differential effects of studies.

The scaling process followed that of Rehfeldt (1989, 

1999) and involved adjusting population means 

according the standardized normal deviate of the performance of populations in common between studies. In the 

present analyses, logarithmic transformations produced 

homogenous variances between studies, which meant 

that population means could be used directly for scaling 

as long as the adjustments were made within taxa. 

Only those six variables describing patterns of shoot 

elongation were measured in all studies (table 1) and, 

therefore, were suited for these procedures.

 In scaling population means between studies, those 

for study 4 were adjusted to those of study 3 according 

to the difference in performance of nine populations 

common to both studies; the resulting data set was 

then adjusted to those of study 2 according to the 

difference in performance of a different set of nine 

populations that were in common; and this data set in 

turn was adjusted to those of the study 1 according to 

the performance of 14 populations in common between 

the studies. Because adjustments were done separately 

for each taxon, P. mexicana and P. chihuahuana were 

Table 1—Number of populations tested (parentheses) and 

intraclass correlations for the main effects of populations 

expressed as a percentage of the sum of all variance 

components from four studies of the three taxa included 

in the Picea engelmannii complex. F-values for tests of 

significance for the effects of populations were statistically 

significant (p<0.01) for all intraclass correlations greater 

than 7 percent.

Test site Study 1 Study 2 * Study 3 Study 4

 and variable (37) (103) (125) (59)

Greenhouse

total elongation 48 17 43 42

start of elongation 29 7 17 21

cessation of elongation 52 19 37 47

duration of elongation 54 17 43 51

rate of elongation 12 11 35 25

2-year height 43 23 48 44

Priest River, 970 m

4-year height 52 39 45 -

winter injuries 11 7 - -

blue foliage 0 17 - -

needle length - - 22 -

Priest River, 1500 m

4-year height 8 27 - -

winter injuries 10 0 - -

mortality 33 - - -

healthy 21 - - -

* From Rehfeldt (1994)

Table 2—Mean values of three variables for a sample of populations common to the independent studies. All populations are P.

engelmannii except where noted. 

Amount of elongation (mm) Duration of elongation (days) Cessation of elongation (day)

Population 

 number Study: 1 2 3 4 Study: 1 2 3 4 Study: 1 2 3 4

1 - 50 64 69 - 22 25 22 - 31 38 33

3 - 54 66 - - 20 23 - - 31 37 -

8 - 65 76 - - 24 27 - - 35 41 -

37 90 49 - - 27 20 - - 38 31 - -

45 72 43 - - 24 21 - - 35 31 - -

467 - - 104 92 - - 33 29 - - 44 39

512 - - 53 60 - - 21 22 - - 37 32

492†† 

62** 127 60 - - 48 31 - - 54 40 - -

108** 101 55 - - 31 22 - - 39 32 - -

60** 128 71 - - 62 51 - - 62 52 - -

61† 146 81 - - 67 50 - - 73 58 - -

67† 156 76 - - 76 47 - - 80 56 - -

447§ - - 61 64 - - 28 27 - - 41 37

** Southwest P. engelmannii

† P. pungens

†† P. glauca x P. engelmannii hybrid

§ P. glauca 
4 USDA Forest Service RMRS-GTR-134. 2004 

excluded from the adjusted data set. Note, however, 

that excluding the latter two taxa does not constrain 

interpretation of subsequent results because the available climate data from Mexico was not sufficient for 

estimating provenance climate. 

Developing Climate Surfaces

Meeting the objectives required estimates of climate 

variables describing the provenance of each population. 

To this end, monthly averages of daily temperatures 

(minimum, maximum, and mean) and sums of daily 

precipitation, normalized for the period 1961 through 

1990, were obtained for approximately 2,200 weather 

stations in Western United States (U.S. Department 

of Commerce 1994) and Southwestern Canada 

(Environment Canada 1994). However, most of the longterm stations were established for agronomic purposes 

and, therefore, poorly represented remote forested locations. To better represent the climate of provenances 

under test, daily temperature and precipitation records 

were obtained from Earthinfo (1994) for an additional 

805 stations that (a) had a record period of at least 5 

years for temperature and 7 years for precipitation and 

(b) had valid records (missing no more than 3 days) 

for all months. Data from these additional stations 

were adjusted to the normalized database by using the 

average of monthly deviations from the three closest 

stations within the database. The procedures gave 

approximately 3,000 observations for monthly means of 

the four variables for the area from 31o to 51o N latitude 

to 102o and 125o W longitude. Climate data sufficient 

for these purposes were not available for Northern 

Mexico. 

Thin plate splines (Hutchinson 2000) were used to 

develop climate surfaces that would provide estimates 

of the 48 monthly climate variables for a geographic 

location, specified by latitude, longitude, and elevation. 

The splines were first fit to the normalized database 

from which 10 percent of the observations had been 

withheld to provide independent data for verification. As 

indicated by (a) the relationship between the predicted 

and observed values, and (b) the deviation of the regression coefficient of the observed on predicted values from 

a value of 1.0, verification of the approach was superb, 

especially for temperature variables. In fact, the worst 

fitting regression of predicted values on the observed 

was for August precipitation for which R2 was 0.88, the 

regression coefficient was 0.9937 and the intercept was 

8.84 mm.

Following verification, the data used for verification 

were returned to the normalized database and thin plate 

splines were refit. Statistical output from the splines 

showed that 95 percent confidence interval for predicted 

point was (a) 1.5 to 2.0 °C for average and maximum 

temperature, (b) about 3 °C for minimum temperature, 

and (c) about 25 mm of winter precipitation or 10 mm of 

summer precipitation. 

Seventeen descriptive statistics of relevance in plant 

biology (see Tuhkanen 1980) were then calculated from 

the point estimates of monthly temperature and precipitation for the provenances represented in this study: 

• Mean annual temperature. 

• Mean temperatures in the coldest month.

• Minimum temperatures in the coldest month. 

• Mean temperatures in the warmest month. 

• Maximum temperatures in the warmest month.

• Annual precipitation. 

• Summer (April through September) precipitation.

• Summer to winter temperature differential. 

• Degree-days greater than 5 °C.

• Degree-days less than 0 °C. 

• Annual moisture index (ratio of degree-days greater 

than 5 °C to mean annual precipitation). 

• Summer moisture index (ratio of degree-days greater 

than 5 °C to mean summer precipitation). 

• Julian dates of the last freeze (50 percent probability) 

of spring. 

• Dates of the first freeze of autumn. 

• Length of the frost-free period.

• Sum of degree-days greater than 5 °C that accumulate 

during the frost-free period.

• Julian date on which the sum of degree-days greater 

than 5 °C reached 100. 

While the derivation of the first eight of these variables is straightforward from the monthly estimates, the 

algorithms and their verification for the latter nine are 

available from the author. 

Biosystematic Analyses

Because of the experimental design, two canonical 

discriminant analyses were needed to assess the relationships among taxa. The first analysis used data from 

study 1 and allowed discrimination among populations 

of P. engelmannii, P. pungens, southwest P. engelmannii, 

P. mexicana, and P. chihuahuana for 13 characters—six 

measured in the greenhouse and seven measured in 

the field (table 1). Although it is impossible to represent 

genetic variation with a sample of single population, P. 

mexicana and P. chihuahuana are carried through these 

analyses to provide a general assessment of their relationships to their congeneric cohorts. 

The second discriminant analysis dealt with P. 

engelmannii, P. glauca, and the hybrids. The analyses 

used data from studies 3 and 4 and used only the six 

variables describing patterns of shoot elongation within 

the corrected data set. Both discriminant analyses were 

performed on the standardized matrix. 

Analyses of Genetic Differentiation Within Taxa

Differentiation among the 295 populations representing the P. engelmannii complex was assessed 

with univariate analyses of variance of each variable 

measured in each study (table 1). Statistical tests used 

in these analyses are presented in detail in Rehfeldt 

(1994) where the degree of genetic variation was 

expressed as intraclass correlations, the ratio of the 
USDA Forest Service RMRS-GTR-134. 2004 5

variance component for the effects of populations to the 

sum of all variance components.

Variance components were also calculated for 

analyses of the 16 P. pungens populations and the 23 P. 

glauca populations.

Genecological Models

In assessing patterns of genetic variation among 

populations in relation to climate, preliminary examinations revealed several associations that were both 

strong and nonlinear. To facilitate nonlinear regression 

analyses, the 17 original climate variables were scaled to 

values between 0 and 1:

�� ���� ������������ � �����

��

� [1]

where X' is the transformed value of a climate variable 

for provenance i; and Xmax and Xmin are the largest and 

smallest values, respectively.

A simple quadratic model was used for an initial 

screening of the relationship between scaled climate 

variables and genetic variation of all variables of table 1. 

The primary intent of this screening was to examine the 

consistency of responses across studies. This screening 

showed that the same combination of dependent and 

independent variables produced the highest R2 in all 

studies, and of the independent variables, the cessation, 

duration, and amount of shoot elongation were the variables with the strongest relationships to climate. This 

meant that the analytical approach could be simplified 

without constraining interpretations by assessing clinal 

patterns of genetic variation with the corrected data set 

containing the six variables describing shoot elongation 

of all populations.

The preliminary screening also showed that of the 

independent variables, degree-days less than 0 °C and 

mean temperature in the coldest month consistently 

produced the strongest relationships with the six 

dependent variables. The relationships, however, were 

strongly nonlinear for all dependent variables except the 

rate of shoot elongation. To accommodate the nonlinear 

patterns, the following model was fit for the cessation 

and duration of shoot elongation:

�� � ���

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��

[2]

where Y is the cessation or duration of shoot elongation of population i; X is the transformed value of 

mean temperature in the coldest month of population 

i; e is the base of natural logarithms; and the b’s are 

regression coefficients. Values of X were transformed 

according to [1].

For 2-year height and amount of shoot elongation, 

the appropriate model was:

� � � �� �����

�� � � � [3]

where X is the transformed value of degree-days less 

than 0 oC with other symbols defined in [2].

And for the start of shoot elongation, the appropriate 

model was:

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�� [4]

with the symbols described in [3].

To find the best fitting combination of predictors for 

describing genetic variation in response to climate in 

general, values predicted by [2, 3, and 4] were treated 

as transformations of the independent variable in a 

stepwise linear regression model that screened, for each 

of the six dependent variables, a total of 35 independent 

variables—the first and second powers of the 17 original 

variables plus the variable transformed according to [2, 

3, or 4]. Rates of differentiation along a cline were interpreted relative to a confidence interval (CI) calculated 

from the standard error of the mean (s � ) at α = 0.2. The 

confidence interval indicates the amount by which two 

predicted values must differ before one is reasonably 

sure that the differences are real. A relatively high level 

of probability (0.2) was used to reduce the possibility 

of accepting no differences among populations when 

differences actually exist. Such errors (type II) have 

the greatest potentials for making poor decisions when 

models are applied to practical problems. The approach 

is directly suited for assorting populations into climatypes, the climatic ecotypes of Turesson (1925).

Effects of Climate Change

Assessing the effects of a change in climate on the 

distribution of taxa and adaptedness of populations 

required, first, an estimation of the climatic limits of 

each taxon. To this end, a canonical discriminant analysis was made using the 17 derived climate variables 

to segregate five taxa—the three of the P. engelmannii

complex, P. pungens, and P. glauca. Because the present 

studies sampled only a small portion of the transcontinental distribution of P. glauca, climate data from an 

additional 38 Alberta provenances were included in the 

discriminant analysis. These supplemental data were 

provided by N. K. Dhir, Alberta Sustainable Resources, 

Edmonton, Alberta. Climatic limits of taxa were inferred 

from those of their constituent populations. The canonical coefficients could then be used to determine site 

suitability: a locality was deemed suitable for a taxon if 

each of the four canonical variates calculated from the 

climate at that locality fell within the taxon’s limits (see 

Box and others 1999).

Next, a climate database was developed that was 

representative of the contemporary altitudinal distribution of the taxa. This database was produced by 

generating an altitudinal sequence of sites at 50 m 

intervals for each of the 36 localities numbered in figure 

1. The thin plate splines described above were then 

used to estimate for each site the monthly means for 

the minimum, maximum, and average temperature and 

precipitation. These variables were then converted to the 

17 derived variables being used to drive the models of 

genetic variation. 

To update this database for the effects of global 

warming, monthly output from General Circulation 

Models (GCM) produced by the Hadley Centre and the 

Canadian Centre for Climate Modelling and Analysis 

'
6 USDA Forest Service RMRS-GTR-134. 2004 

was summarized for the region of study. The scenarios 

chosen were HadCM3GGa1 of the Hadley Centre 

(Gordon and others 2000) and CGCM2_ghga of the 

Canadian Centre (Flato and Boer 2001), both of which 

address the IS92a scenario (1 percent per year increase 

after 1990) of the International Panel on Climate 

Change (IPCC 2001). For each, monthly means for the 

minimum, average, and maximum temperature and 

precipitation were obtained for the normalization period 

(1961 through 1990) and for three future decades, those 

beginning in 2030, 2060, and 2090.

GCM output was used to calculate the monthly 

change in climate between the normalization period and 

future decades for those grid cells that underlie figure 

1. The change in climate expected at each provenance 

represented in the present studies could then be calculated by a weighted average of change expected at each 

cell center lying within 500 km of the provenance. The 

inverse of the distance of the provenance to the cell 

center was used for weighting. Because the two GCMs 

use different grid sizes, weighted means were calculated 

for each GCM and were averaged to estimate the change 

in climate of each provenance. Estimated climates of the 

future were then converted to the 17 derived variables 

being used to drive the models of genetic variation.

In the third step, genecological models of genetic 

variation were used to predict for all locations in the 

database the six variables describing patterns of shoot 

elongation for the contemporary climate and for three 

decades of the future. This database could then be 

queried for the effects of climate on the adaptedness of a 

population at any specific targeted locality. To estimate 

the geographic distribution of populations expected to 

be genetically suited to the contemporary climate at 

a specific locality, one (1) selects a target locality, (2) 

determines which taxa are suitable for the target, (3) 

predicts genetic responses for the suitable taxa from 

the models of genetic variation, (4) attaches confidence 

limits to each prediction, and (5) searches the database 

of contemporary climates for all locations expected to 

be inhabited by genotypes whose responses fall within 

the confidence intervals calculated for the target. To 

estimate future effects, one merely uses, in step 5, the 

database of future climates instead of that of contemporary climates.

Two Euclidean measures of distance were calculated 

to provide an indication of the suitability of a selected 

population to the climate at a targeted locality. One was 

a genetic distance (GD):

GD d

CI n i

i

= 











 

 ∑  −

2

1

[5]

where d is the difference in genetic responses in trait i

between that predicted for the climate at the targeted 

locality and that of a population represented in the database; CI is the confidence interval (α = 0.2) surrounding 

population means for trait i; and n is the number of 

traits, equal here to 6.

The second was a climate distance (CD):

CD = ( ) ∑g n i

2 1− [6]

where g is the difference between climate variable i 

scaled according to [1] at the targeted location and that 

of a population represented in the database; and n is the 

number of climate variables. All derived variables were 

used for these calculations, and, therefore, n = 17.

Results

Biosystematics

The canonical discriminant analysis based on 13 

variables (table 1) measured on five taxa in study 1 

produced a primary vector that accounted for 46 percent 

of the variance among taxa; a second accounting for 42 

percent; a third for 10 percent; and a fourth for 3 percent. 

The position of the taxa with regard to the first three 

canonical vectors (fig. 2) shows that (1) P. chihuahuana is 

readily separated from all other taxa; (2) P. pungens can 

also be separated from all other taxa; and (3) P. engelmannii, southwest P. engelmannii, and P. mexicana tend 

to form a continuum anchored by P. engelmannii at one 

extreme and by southwest P. engelmannii at the other 

with P. mexicana peripheral and somewhat disjunct. The 

fourth vector was inconsequential.

The second discriminant analysis considered 290 

populations of P. engelmannii, P. glauca, and their 

hybrids; was based on the dataset that had been 

corrected for the effects of studies; and used, therefore, 

only the six variables describing shoot elongation. The 

first canonical vector accounted for 86 percent of the 

variance among groups and the second 14 percent. The 

scatter of data points according to these vectors (fig. 3) 

demonstrates that the three taxa can be separated on 

average, but not discretely. Not surprisingly, populations 

labeled as hybrid generally were positioned intermediate 

between parental taxa. 

Population Differentiation

Analyses of variance detected abundant genetic 

variation among the 295 populations representing the P. 

engelmannii complex (table 1). Differentiation was most 

pronounced in study 1, which contained a mixture of P. 

engelmannii and southwest P. engelmannii genotypes; was 

nearly as pronounced in studies 3 and 4, composed of P. 

engelmannii and P. engelmannii – P. glauca hybrids; and 

was the least in study 2, composed primarily of the P. engelmannii populations analyzed previously (Rehfeldt 1994).

Table 1 shows clearly that population differentiation 

is most pronounced for either variables describing shoot 

elongation or seedling height in the low elevation field 

test. This means that the absence in studies 3 and 4 of 

data from either or both of the field test sites should not 

constrain interpretations.

In analyses not presented, population differences were 

also detected among the 15 P. pungens populations of study 

1. Particularly strong were population effects for 4-year 

height at the low elevation test site and for 2-year height 

measured in the greenhouse, but all variables describing 

the pattern and amount of shoot elongation were 
USDA Forest Service RMRS-GTR-134. 2004 7

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statistically significant (p < 0.0001). Differences among 

populations also were apparent among the 13 populations 

of P. glauca for all variables measured in study 4.

Climatic Clines Within Studies

Despite the disparate composition of the four studies, 

univariate relationships of climate variables to the 

performance of populations from the P. engelmannii

complex were surprisingly consistent across studies 

(table 3). The strongest relationships invariably involved 

the cessation, duration, and amount of elongation, all 

of which were measured in the greenhouse on 2-year 

trees. Relationships between genetic variation in these 

variables and climate tend to be strong, with climate 

accounting for as much as 76 percent of the variation 

among populations (table 3). Univariate relationships 

also suggest that in the south (studies 1 and 2), genetic 

differentiation is most closely related to climate variables 

reflecting winter temperature, but toward the north 

(studies 3 and 4), summer temperatures also become 

important descriptors. 

The consistency of these results implies that the 

same primary factors are molding genetic variation 

throughout the distribution of the three taxa that 

Figure 2—Dispersion of populations of 

five taxa along the first three vectors 

of a canonical discriminant analysis 

of 13 variables measured on populations in study 1 (table 1). 

Figure 3—Dispersion of three taxa 

along the first two vectors of a 

canonical discriminant analysis of 

six variables describing patterns 

of shoot elongation measured in 

populations of studies 3 and 4 

(table 1).
8 USDA Forest Service RMRS-GTR-134. 2004 

compose the P. engelmannii complex. Because (1) the 

variables for which genetic variation was the most 

pronounced (table 1), and (2) the variables for which 

relationships with climate are the strongest (table 3) 

are derived from measurements of shoot elongation, 

subsequent analyses could deal solely with the corrected 

data set. Nonetheless, before results using the corrected 

data set can be accepted unequivocally, some assurance is required that the methods employed in merging 

data sets did not inadvertently introduce bias.

Effects of Scaling and Joining Datasets

In scaling and joining data collected in disparate 

studies, correction factors were used that consisted of 

the difference in performance between the same populations represented in different studies. However, to 

develop correction factors for each taxon, all of the available data were required. This meant that no independent 

tests could be made for judging the effectiveness of the 

procedures (see Rehfeldt 1989). 

Three observations nonetheless are pertinent: First, 

of the 32 possible comparisons between adjusted and 

actual means, adjusted means on average were within 3 

percent of the actual mean for the start and end of shoot 

elongation; 4 percent for the duration of shoot elongation; 

6 percent for the amount of shoot elongation and 2-year 

height; and 7 percent for the rate of elongation. Second, 

the mean difference between the actual and adjusted 

values was smaller on average than the standard error 

of the mean. And third, in regressions of observed means 

on adjusted means for these 32 observations, values of R2

ranged from 0.88 (rate of elongation) to 0.99 (cessation of 

elongation); regression coefficients were essentially 1.0, 

ranging from 0.96 to 0.99; and intercepts were essentially zero (5 mm for 2-year height, 1.5 mm for amount of 

shoot elongation, 0.2 days for the duration of elongation, 

0.4 days for the cessation of elongation, 0.5 days for the 

start of elongation, and 0.14 mm/day for the rate of elongation). Although not conclusive, these comparisons are 

consistent with the tests of Rehfeldt (1989); both suggest 

that when careful experimentation is conducted under 

similar conditions, these independent data sets can be 

scaled according the performance of common populations without incorporating bias but yet producing errors 

of estimate that are quite small.

Climatic Clines of the P. engelmannii Complex

Regressions relating climate to genetic variation 

among the 295 populations representing the P. engelmannii complex used the corrected data and, therefore, 

deal with only those six variables describing patterns 

of shoot elongation. Figure 4 shows the relationship 

between the total amount of elongation and degreedays less than 0 °C modeled according to [3]. For this 

relationship, degree-days less than 0 °C accounts for 

62 percent of the variance in the amount of elongation. 

The relationship is decidedly nonlinear: as the winter 

of the provenance becomes more severe, the amount 

of shoot elongation exhibited in the mild climate of a 

common garden diminishes. But the cline is much less 

steep for severe climates than for mild climates. The 

clines relating winter temperatures to the cessation and 

duration of shoot elongation and to 2-year height are 

essentially the same as that shown in figure 4. As the 

winter climate of the provenance becomes more severe, 

growth potential is reduced in response to a shortened 

growth period, and these characteristics are expressed 

even when the populations are grown in the mild 

climates of common gardens.

Genetic variation in the start of shoot elongation also 

showed a primary relationship with degree-days less 

than 0 °C, with the dependent variable accounting for 

approximately 34 percent of the variance in the start 

of elongation. The relationship was driven largely by 

populations of southwest P. engelmannii, which initiated shoot elongation the earliest and originated from 

provenances with mild winters. Differences among 

the P. engelmannii and hybrid populations from colder 

climates to the north were not related to the coldness of 

winters of the provenance.

Table 3—Combination of dependent and independent variables with the strongest univariate relationships to provenance 

climate for populations in the P. engelmannii complex.

Study Dependent variables Independent variables Maximum R2

1 cessation, duration, and amount of elongation degree-days<0oC, mean temperature in coldest month, 0.76

 mean annual temperature

2* cessation and duration of elongation degree-days<0oC, mean temperature in coldest month, 0.70

 mean annual temperature, date when degree-days>5oC 

 summed to 100

3 duration of elongation, 4-yr height in low degree-days<0oC, mean minimum temperature in coldest 0.60

 elevation test site month, mean annual temperature, degree-days>5oC, date 

 when degree-days>5oC summed to 100, sum of 

 degree-days>5oC during frost-free period

4 cessation and duration of elongation degree-days>5oC, degree-days<0oC, mean annual 

 temperature, date when degree-days>5oC summed to 

 100, sum of degree-days>5oC during frost-free period 0.71

* From Rehfeldt (1994)

Note: all values of R2 statistically significant at p < .0001
USDA Forest Service RMRS-GTR-134. 2004 9

The primary cline relating the rate of shoot elongation 

to climate was essentially a linear response associated 

with either the Julian date of the last spring frost (R2

= 0.30) or degree-days more than 5  ̊C that accumulate during the frost-free period (R2 = 0.28). Slow rates 

of elongation were exhibited in common gardens by 

populations from climates where the last frost of spring 

customarily occurs early in the spring; and high rates 

typified populations from provenances where the degreedays accumulating within the frost-free season are high.

Rates of differentiation along univariate clines can 

be judged in relation to the standard error of the mean. 

For figure 4, for example, the confidence interval (α = 

0.2) about a population mean for amount of elongation 

is ±0.0802 mm in units of logarithms. When converted 

to the original units of measure, a standard error of 

this size would mean that for two populations to differ 

at a reasonable level of probability (α = 0.2), the mean 

difference in elongation must be at least 12 mm for populations with low innate growth potential (for example, 75 

mm total elongation) or by 24 mm for populations with 

high growth potential (for example, 150 mm elongation). 

It follows, therefore, that two genetically different populations tend to originate in provenances differing by 

about 110 degree-days less than 0  ̊C if growth potential is low or about 90 degree-days if growth potential is 

high (fig. 4). The clines relating 2-year height to degreedays less than 0 produced a similar conclusion, but the 

cline for the start of elongation produced much broader 

intervals particularly for populations from cold climates 

where differentiation was difficult to detect.

The same reasoning applied to the univariate 

clines—that is, relating rates of change in the cessation 

and duration of elongation to mean temperature in the 

coldest month—also produced estimates of 0.6  ̊C and 

2.2  ̊C by which populations from either warm or cold 

provenances, respectively, must be separated before one 

can be reasonably certain (α = 0.2) of genetic differentiation. The cline for the rate of elongation suggested that 

genetically different populations originate from provenances differing by at least 20 days in the date of the 

last spring frost and about 600 degree-days more than 

5  ̊C that accumulate across the frost-free period.

Genetic differentiation in response to multiple 

climatic effects was assessed for populations of the P. 

engelmannii complex with stepwise regression, the best 

fitting models of which produced values of R2 between 

0.54 and 0.83 (table 4). The dependent variables for 

which climatic clines were the steepest were the cessation and duration of shoot elongation. Although the 

regression models include as many as five independent 

variables, most described the severity of the winter. This 

result also corroborates those obtained from univariate 

regressions done within studies (table 3) that show 

Figure 4—Population means for the amount of shoot elongation of 

taxa of the P. engelmannii complex plotted according to degreedays less than 0  ̊C of the provenance. The univariate regression 

model (line) accounts for 62 percent of the variance in the dependent variable. 

Table 4—Independent variables for the best fitting multiple regression models of genetic variation in variables describing 

patterns of shoot elongation in populations of the P. engelmannii complex on provenance climate. 

Dependent variable R2 Independent variables

start of elongation 0.54 degree-days<0oC, mean minimum temperature in coldest month, April – September precipitation 

rate of elongation 0.62 date of last spring frost, date of first fall frost, mean minimum temperature in coldest month, mean 

 maximum temperature in warmest month

cessation of elongation 0.81 degree-days<0oC, mean minimum temperature in coldest month, mean temperature in the coldest 

 month, mean maximum temperature in warmest month 

duration of elongation 0.83 degree-days<0oC, mean minimum temperature in coldest month, mean temperature in the coldest 

 month, mean maximum temperature in warmest month, summer – winter temperature differential

2-year elongation 0.78 degree-days<0oC, mean maximum temperature in warmest month, length of the frost-free period, 

 date when degree-days>5oC summed to 100

2-year height 0.72 degree-days<0oC, mean temperature in coldest month, mean maximum temperature in warmest 

 month

Note: all values of R2 statistically significant at p < .0001
10 USDA Forest Service RMRS-GTR-134. 2004 

relationships of genetic variation to summer temperatures to be secondary. Relationships between predicted 

and observed values are shown in figure 5 for four of the 

variables.

Climatic Clines in P. pungens and P. glauca

Even though few populations of P. pungens and P. 

glauca were tested, their performance shows without 

doubt that climatic clines within these species are much 

different from those of the P. engelmannii complex (fig. 

6). Climate variables most strongly related to genetic 

variation in P. pungens included degree-days less than 

0  ̊C, mean temperature in the coldest month, the 

summer-winter temperature differential, and summer 

precipitation. Although populations of both the P. engelmannii complex and P. pungens are arranged along clines 

driven by winter temperatures, the cline for the P. engelmannii complex is of much steeper slope than that for P. 

pungens (fig. 6).

For P. glauca, the summer-winter temperature differential, the Julian data at which the annual degree-day 

greater than 5  ̊C sum reached 100, length of the frostfree period, Julian date of the last spring frost, and 

mean temperature in the coldest month were the climate 

variables most closely associated with variation among 

populations. These clines, therefore, are driven by much 

Figure 5—Plots of population means for the 

P. engelmannii complex against values 

predicted by multivariate regression 

models of the duration (upper left), start 

(upper right), and rate (lower right) of 

shoot elongation and 2-year height (lower 

left) on climate variables. The regressions 

account for 83, 54, 62, and 72 percent of 

the variance in the dependent variable, 

respectively. Symbols correspond to 

figure 4.

Figure 6—Population means for cessation of shoot elongation of 

the P. engelmannii complex, P. pungens, and P. glauca plotted 

according to the degree-days less than 0 °C of the provenance.
USDA Forest Service RMRS-GTR-134. 2004 11

different climate variables than those of P. pungens and 

the P. engelmannii complex. Figure 6, in fact, shows that 

variation in cessation of shoot elongation for populations of P. glauca, unlike that of its congeneric cohorts, 

is not arranged along clines paralleling winter temperatures. While winter temperatures were most closely 

related to the start of shoot elongation in P. glauca, on 

the whole, genetic differentiation within the species was 

most closely related to the summer-winter temperature 

differential of the provenance.

Interpreting Genetic Variation Along Multiple 

Climatic Clines

Because the distribution of genotypes along multiple 

climate gradients deals as much with the distribution 

of taxa as with populations, a discriminant analysis for 

separating the five taxa, the three of the P. engelmannii

complex, P. pungens, and P. glauca, was made using 

the 17 climate variables as discriminators. The analysis 

produced a first canonical vector accounting for 60 

percent of the variance among the five taxa; a second 

for 27 percent; a third for 7 percent; and a fourth for 5 

percent. The results are illustrated in figure 7 (left and 

right). These illustrations show that while most taxa 

are separated along the first and second vectors, P. 

pungens tends to be separated from both southwest P. 

engelmannii and P. engelamnnii along the third vector. As 

expected for taxa that can cooccur, the segregation of the 

taxa is not complete. Nonetheless, populations labeled as 

hybrids between P. engelmannii and P. glauca segregated 

into positions intermediate between populations of the 

two parental species. 

The discriminant functions when used in conjunction with the climatic limits of the taxa can be used to 

construct a climatic envelope (see Box and others 1999) 

within which genecological models can be interpreted. 

Modeled population responses for the duration of shoot 

elongation (table 4), for instance, are mapped in figure 

8 within the climatic envelope of the P. engelmannii

complex on a 1 km (0.0083 degrees) grid (Globe 1999). 

Figure 7—Dispersion of populations of five taxa along 

the first two vectors of a 

canonical discriminant analysis (left) and the first and 

third vectors (right) using 

17 variables describing 

provenance climate.

Figure 8—Mapped patterns 

of genetic variation in the 

duration of shoot elongation 

predicted for populations of 

the P. engelmannii complex. 

Left: continuous variation 

from 21 days (dark blue) to 

79 days (light yellow). Right: 

nine classes of breadth equal 

to the confidence interval 

(α = 0.2) surrounding mean.
12 USDA Forest Service RMRS-GTR-134. 2004 

In figure 8, a map of continuous variation described 

by the model can be compared with that for nine 

geographic groups composed of genetically similar 

populations.

Figure 8 nonetheless illustrates univariate cline 

described by multiple climatic effects. To assess genetic 

variation across the landscape according to all six 

response variables, the regression models of table 4 

were used to make queries into a climate database to 

locate extant populations expected to have patterns of 

shoot elongation that are genetically compatible with the 

climate at a targeted location. As an example, consider 

a target at an altitude of 3,000 m in southwestern New 

Mexico (location 3, fig. 1) that is currently inhabited by 

southwest P. engelmannii. This population is relatively 

isolated (fig. 1) in a region where populations have a 

high growth potential and, therefore, clines are steep 

(fig. 4). As a result, genotypes compatible (for example, 

within ±CI at α = 0.2 for all six predicted responses) with 

the climate at this location are expected to be found 

at a few isolated locations peripheral to location 3: in 

east-central Arizona (location 4) at an altitude of 2,900 

m, in central New Mexico (location 8) at 2,900 to 3,000 

m elevation, and in northern New Mexico (location 9) at 

2,800 m (table 5). 

In the severe climates in the north, clines tend to be 

less steep than in the south (fig. 4), and as a result, 

populations expected to be genetically compatible with 

the climate at a targeted site (table 5) are more widespread than in the south. At location 20, for instance, 

the climate at an altitude of 2,250 m should be suitable 

for genotypes that are distributed across a region so vast 

as to include populations occurring as distant as 2,500 

km (table 5, fig. 1). Broad adaptability of northern populations of the P. engelmannii complex is also evident for 

targeted sites at localities 24 and 33. 

 Because clines are steeper for mild climates than 

for severe, populations best suited genetically to the 

climate at high elevations tend to be more widespread 

than those compatible with low elevation climates (for 

example, 1,350 m altitude at location 24 versus 1950 m 

at the same location). Also noteworthy in table 5 is that 

(1) either P. pungens or southwest P. engelmannii should 

be suited for the selected elevations at locations 10 or 14 

even though the former is best suited for southwest P. 

engelmannii and the latter for P. engelmannii, (2) either 

hybrid or P. engelmannii genotypes should be suited 

to localities 20 and 33, (3) P. engelmannii, hybrid, or 

P. glauca populations are suited for the target site at 

location 32, (4) only P. glauca is suited for the Cypress 

Hills (location 31) of Southwestern Alberta and the 

Black Hills (location 19) of South Dakota, and (5) the 

same elevations at localities 32 and 33, which are on 

the eastern and western slopes of the Rocky Mountains, 

have climates so diverse that the best suited genotypes 

are from entirely different complements. For future 

reference, note that in table 5, populations capable of 

similar response to the climate at a targeted locality are 

within 0.75 units of genetic distance, and these similar 

populations should recur in climates within 0.25 units 

of climate distance.

The relationships of table 5 can be mapped for the 

P. engelmannii complex by classifying the continuous 

patterns of genetic variation for each of the six response 

variables (fig. 8) and mapping unique combinations of 

the classes as climatypes (fig. 9 and 10). Three of the 

largest climatypes are mapped in figure 9. Of these, the 

largest, colored yellow, occurs at altitudes of about 900 

to 1,300 m in Alberta, 1,800 to 2,400 m in Idaho, 2,000 

to 2,500 m in Utah, and 2,300 to 3,100 in Colorado; 

the climatype of the Northwest (pink) occurs at altitudes 

from 900 to 1,500 m on both sides of the Columbia 

Table 5—Locations (number codes in bold face, keyed to figure 1) and their altitudes (m) containing genotypes expected to be 

genetically compatible with the contemporary climate of the targeted locality. Compatible genotypes are P. engelmannii except as 

noted.

Location of compatible genotypes

Targeted

 locality 1 2 3 4 5 6 7

3: 3000 3: 3050-3150 * 8: 2900-3000* 9: 2800-2850 * 4: 2900 *

10: 3000 10: 2950-3100 * 10: 2950-3000 † 5: 2950 * 7: 3200 * 6: 2700*

14: 2800 14: 2750-2850 14: 2750-2850 † 15: 2750-2850 15: 2750†

12: 3200 12: 3100-3300 14: 3250-3300 15: 3200-3250 17: 2900-2950 20: 2500

17: 2900 17: 2800-2950 16: 2850-2900 23: 2100-2250 15: 3200 12: 3200 20: 2350

20: 2250 20: 2150-2300 †† 24: 2000-2200 16: 2700-2750 17: 2650-2750 21: 2200-2250 32: 1750-1800 12: 2900-2950

24: 1350 24: 1300-1600 25: 1250-1500 27: 1100-1150 28: 1100-1150

24: 1950 24: 1800-2050 25: 1700-1800 26: 1400-1550 27: 1500 28: 1550 32: 1650-1700

31: 1450 31: 1150-1500§

32: 1250 32: 1300-1400 †† 32: 1200-1250§ 30: 1650-1700§ 21: 1850§ 28: 1150

33: 1250 33: 1100-1350†† 34: 700-1050†† 28: 1400-1500 27: 1450 24: 1750

Note: also compatible for targeted location 20 are: 21: 2150 ††, 15: 3000, 14: 3150, and 12: 2900†

* Southwest P. engelmannii

† P. pungens

†† P. glauca x P. engelmannii hybrid

§ P. glauca
USDA Forest Service RMRS-GTR-134. 2004 13

Basin; while the third (green) is common from 2,700 

to 3,600 m in the Southwest. Grid cells within each of 

these climatypes are expected to be of similar climate 

and therefore should support similar genotypes. 

In figure 10, the assortment of climatypes is mapped 

for Idaho and western Montana (left) and Colorado 

(right). Although these maps delimit a relatively large 

number of climatypes (103 for Idaho and Montana; 87 

for Colorado), both regions are dominated by only a few. 

For Idaho and Montana, for instance, 20 climatypes 

account for approximately 75 percent of the distribution 

of the P. engelmannii complex, while two of these account 

for about 20 percent. For Colorado, the 20 largest climatypes account for about 80 percent of the taxon’s total 

distribution while one of these alone accounts for 12 

percent. 

Climate Change

The estimates of global warming used for these 

analyses were derived from the average of predictions by 

the Hadley and Canadian Models for the IS92a scenario. 

At a longitude of 114 °W, the change in mean annual 

temperature projected by the average of the two models 

for the decade beginning in 2030 is +1.9 °C for a latitude 

of 35 °N and +2.5 °C for a latitude of 50 °N; +3.5 °C and 

+3.7 °C, respectively, for the decade beginning in 2060; 

and +4.4 °C and +5.0 °C, respectively, for the decade 

beginning in 2090. Although calculated in percentages, 

changes in precipitation equate roughly to +17 mm, +10 

mm, and +65 mm for the decades of 2030, 2060, and 

2090, respectively, at 35 °N; and +55 mm, +96 mm, and 

+105 mm in the three decades at 50 °N.

After changing the climate at a target location 

according to averages of GCM output, one then can 

identify the contemporary populations that should be 

the best suited for novel climates. Table 6 provides 

examples for the same target locations used for table 

5. At location 3, for instance, the combined increase 

in temperature and precipitation should produce a 

climate beyond current climatic limits of distribution for 

southwest P. engelmannii and P. pungens. The analyses 

imply further that the climate should not be suitable for 

Picea at locations 1 and 2 by 2030 and at location 4 by 

2060. By 2090, the southern limits of both southwest 

P.engelmannii and P. pungens are expected to be at locations 7 and 8 (fig. 1). One must note, however, that the 

suitability of P. chihuahuana and P. mexicana for the 

Figure 9—Three climatypes of the P. engelmannii complex superimposed on the taxon’s climatic distribution in Western United States 

and Southwestern Canada.

Figure 10—Distribution of climatypes of the P. engelmannii complex in Idaho and western Montana (left) and Colorado (right).
14 USDA Forest Service RMRS-GTR-134. 2004 

novel climates at these southern locations could not be 

assessed.

In another example, at 2,800 m at location 14, the 

climate should become more suitable to P. pungens

than to P. engelmannii by 2030 and should remain so 

throughout the remainder of the century. Yet, as the 

climate warms, the best suited populations of P. pungens

are shown to currently reside farther and farther to the 

south. By 2090, the best suited genotypes for this site 

currently inhabit 2,600 m elevation at location 3, about 

600 km to the southeast. By 2060, P. pungens should be 

the dominant member of the genus on the plateaus of 

central and southern Utah (locations 13, 14, 15, and 17). 

By contrast, at 3,000 m at location 10, a climate that 

today is suitable for southwest P. engelmannii should 

remain suitable throughout the century. Nonethless, 

the contemporary location of the best suited genothypes 

shifts toward the south as the climate changes. At the end 

of the century, in fact, the best suited genotypes for the 

novel climate at this location reside today at the southern 

limits of the taxon’s distribution, nearly 700 km away. 

Elsewhere, at 3,200 m at location 12, the climate of 

2,030 should become best suited to populations of P. 

engelmannii from lower elevations and milder climates. 

After 2060, however, the climate at this site should 

become more suitable for P. pungens, the best suited 

genotypes of which currently reside at lower elevations 

400 km to the south.

Toward the north at location 20, the novel climate of 

2030 should be best suited to hybrid genotypes from 

slightly lower elevations, but beyond 2060, the climate 

should be more suitable for populations of P. glauca. 

For location 32, a transition from P. engelmannii to P. 

glauca also is expected by 2060, but by 2090 the climate 

should no longer be suited for the genera. Quite similarly, the populations of P. glauca that currently inhabit 

mid elevations in the Cypress Hills (location 31, fig. 1) 

and the Black Hills (location 19) should remain suitable 

for the novel climate of 2030, but by 2090, the climate 

at all elevations should be unsuitable for the genera.

In the northwest part of the region, the projected 

changes in climate should produce responses that are 

now expected: (1) a zone of extirpation moving from low 

to high elevations as the climate warms and (2) a change 

in the genotypes best suited to the novel climate to those 

that today occupy milder climates at lower elevations. 

Thus, at 1,350 m at locality 24, the best suited genotypes 

for 2030 currently reside at 1,000 m elevation slightly 

to the north where the climate is somewhat milder. By 

2060, the site should no longer be suitable for Picea. But 

at an elevation of 1,950 m at the same general locality, P. 

engelmannii should be suited throughout the century. Yet 

the contemporary location of the genotypes best suited to 

the new climates shifts from the contemporary site to the 

low elevations at the northern tip of the Cascade Range 

(location 26) by the end of the century.

The effects of a changing climate at location 24 

(table 6) tend to be repeated at locations 26, 27, 33, 

and 34. However, by the end of the century at site 

35, the northernmost site used for these examples, 

P. glauca should be the best suited taxon up to an 

elevation of about 1,500 m, beyond which hybrid 

populations currently inhabiting milder climates to 

the south become best suited. This suggests that by 

the end of the century in the northwest portion of the 

region of study, taxa of the P. engelmannii complex 

should become a rarity, occupying only the highest 

elevations and coldest sites.

Effects of global warming on the P. engelmannii

complex are illustrated in figure 11 where the current 

and future distributions of a single climatypes are superimposed on the climatic distribution of the complex. As 

shown in this figure, the distribution toward the end of 

the current century is expected to be reduced by about 

89 percent as climates suited for this complex are pushed 

upward and northward. The altitudinal displacement is 

Table 6—Contemporary location (codes in bold face, keyed to figure 1) and their altitudes (m) containing genotypes expected to be 

the best suited for future climates predicted according to the IS2a scenario of two GCMs. All locations are assumed to be inhabited 

by P. engelmannii except where noted.

2020-2029 2060-2069 2090-2099

Target locality Population location Genetic distance Population location Genetic distance Population location Genetic distance

3: 3000 --- --- --- --- --- ---

10: 3000 7: 2850 * 0.30 8: 2800 * 0.60 2: 2950 * 0.99

14: 2800 9: 2700 † 0.86 6: 2500 † 0.42 3: 2600 † 0.88

12: 3200 14: 3050 0.61 9: 2750 † 0.90 10: 2550 † 0.35

17: 2900 16: 2500 0.71 11: 2600 † 0.81 10: 2500 † 0.53

20: 2250 21: 1850 †† 0.76 30: 1600 § 0.92 19: 1300 § 0.90

24: 1350 25: 1000 0.89 --- --- --- ---

24: 1950 27: 1500 0.70 27: 1100 0.92 26: 600 0.85

31: 1450 19: 2000 § 0.36 --- --- --- ---

32: 1250 25: 1000 1.08 19: 1200 § 1.23 --- ---

33: 1250 28: 1100 0.34 24: 1300 0.72 --- ---

* Southwest P. engelmannii

† P. pungens

†† P. glauca x P. engelmannii hybrid

§ P. glauca 
USDA Forest Service RMRS-GTR-134. 2004 15

Figure 11—Distribution of a 

single climatypes of the 

P. engelmannii complex 

superimposed on the 

climatic distribution of the 

species for the contemporary climate (upper left), 

and the distribution of 

future climates suitable for 

the same climatype in the 

decades beginning in 2030 

(upper right), 2060 (lower 

left), and 2090 (lower 

right).

expected to approach 1,000 m. As a result, only about 

23 percent of the 2090 distribution will be inhabited 

continuously during the century, but approximately 

80 percent of the 2090 distribution should be suitable 

climatically for climatypes that exist today.

Discussion

Biosystematics

Biosystematic relationships among the Picea taxa 

of Western North America are complex and confusing 

(see Ledig and others 2004). The present results join 

with those of numerous studies dealing with a broad 

spectrum of characters to readily distinguish P. pungens

and P. chihuahuana from each other, from the taxa 

composing the P. engelmannii complex, and from P. 

glauca. The present results are also consistent with the 

prevailing view of widespread introgressive hybridization on the northern front between P. engelmannii and P. 

glauca and show that the introgression tends to bridge 

the climates inhabited by the parental taxa. 

Unanimity among researchers, however, does not exist 

concerning the southern front, where the taxonomic 

status of P. mexicana has been repeatedly questioned 

since its naming in 1961 (Martinez 1961). To be sure, all 

pertinent research has demonstrated a close relationship 

between P. mexicana and P. engelmannii in morphologic 

(Taylor and Patterson 1980, Taylor and others 1994), 

chemical (Taylor and Patterson 1980, Taylor and others

1994), and molecular (Sigurgeirsson and Szmidt 1993) 

characters. While Taylor and his coworkers (for example, 

Taylor and Patterson 1980) believed that P. mexicana

should be recognized as a variety of P. engelmannii, 

Ledig and others (2004) believe that allozyme differences 

between the taxa are large enough to warrant the designation of separate species. Illustrative of the problem but 

providing no solution is figure 2, in which the P. mexicana population was aligned closely with, but peripheral 

to, populations of southwest P. engelmannii. 

Also unclear is the taxonomic status of southwest 

P. engelmannii. While mean differences between this 
16 USDA Forest Service RMRS-GTR-134. 2004 

taxon and P. engelmannii are real (Ledig and others 

2004, Rehfeldt 1994, fig. 2), variances obviously overlap 

(fig. 4). On the one hand, the present results (for example 

fig. 2) could be used to support the view that southwest 

P. engelmannii merely represents the southern portion of 

the P. engelmannii cline. Yet, on the other hand, populations of the two taxa are arranged along clines of much 

different slope that presumably have been molded by 

different climatic effects (fig. 4). It seems reasonable, 

therefore, to view southwest P. engelmannii as a variety 

of P. engelmannii. A logical extension of this view would 

be to designate P. mexicana as the taxon at the southern 

tip of this cline.

Clines of the P. engelmannii Complex

Widely distributed species invariably face a broad 

range of climate variability. Indeed, for most of the 

Picea taxa considered herein, genetic differences among 

geographically disparate populations have been detected 

in common garden studies: P. pungens, for example, 

by Bongarten and Hanover (1986), P. engelmannii by 

Shepperd and others (1981) and Rehfeldt (1994), and 

P. glauca by Morgenstern (1996). In the present paper, 

however, clines in P. engelmannii were considered in 

relation to climatic rather than geographic gradients.

The results showed that the primary cline along which 

genotypes of the P. engelmannii complex are distributed 

is in relation to the winter temperature regime of the 

provenance, particularly for southern populations. A 

secondary cline was in relation to summer temperatures 

and seemed most pronounced for northern populations. These clines show that populations from the least 

severe climates, whether measured by either summer 

or winter temperatures, were the largest after 2 years, 

ceased elongation the latest, had the longest duration of 

elongation, elongated at the fastest rate, began elongation the earliest, and elongated the most. Other climate 

variables that appeared influential included: maximum 

temperature in the warmest month, length of the frostfree season, Julian date on which the degree-day greater 

than 5 ∞C sum reached 100, summer precipitation, and 

dates of first spring and last fall frosts.

Global Warming

Climate is the primary factor controlling the distribution of organisms (Brown and Gibson 1983, Woodward 

1987). Organisms, therefore, will respond to a change in 

climate. In plants, the response can be of four different 

types (see Rehfeldt and others 2001): coping—the 

exploitation of physiological plasticity; extirpation—the 

mortality that accrues when the amount of change 

exceeds the capacity of the plastic physiological system 

to adjust; evolution—the changes resulting from the 

novel selection pressures; and immigration. Table 6 

suggests that the IS92a climate change scenario will 

stimulate one or more of these responses in each and 

every population within the distribution of the taxa 

under study. Global warming, therefore, will have effects 

that transcend the margins of distribution where extirpation and immigration occur to include all populations 

as genetic systems adjust to change (Davis and Shaw 

2001).

According to the IS92a scenario, effects of global 

warming during the present century on P. engelmannii and its congeneric cohorts should be especially 

pronounced on four fronts (table 6). One front involves 

the northwest portion of the study area where extirpation is predicted for all taxa of the genera across 

hundreds (600 to 900) of meters of elevation. This level 

of extirpation essentially would remove Picea from the 

local flora except perhaps, for the coldest sites and 

those at the highest elevations (greater than 1,900 m). 

Thompson and others (2002) reached the same conclusions using much different procedures. A second front 

would involve extirpation in the mountain islands of 

the Southwest: by the end of the century, the southern 

limit of southwest P. engelmannii may, in fact, may have 

shifted 400 km to the north. As suggested by table 6, P. 

pungens is expected to take advantage of this widespread 

extirpation of the P. engelmannii complex, although the 

possibilities that P. mexicana and P. chihuahuana could 

fill the void remain unexplored. A third front involves 

the geographic isolates of P. glauca in the Black Hills of 

South Dakota and the Cypress Hills of Alberta where a 

changing climate should rapidly produce climates not 

suitable for Picea. And last, throughout much of the 

remaining areas, widespread extirpation is expected at 

low elevations as a warming climate forces upward the 

lower altitudinal limits.

To be sure, immigration of Picea taxa will be occurring concomitantly with the extirpation, but it seems 

doubtful that the immigration can be sufficient to offset 

the extirpation. On the one hand, an orderly migration 

of P. glauca toward the north and P. pungens toward 

higher elevations is readily envisioned. But on the other 

hand, the immigration opportunities for taxa of the P. 

engelmannii complex are limited largely because taxa of 

the complex currently inhabit the high elevations. In the 

north, moreover, the distribution of the complex abuts 

and intermixes with that of P. glauca. While the P. engelmannii complex is montane, P. glauca is a component 

of the boreal forests distributed across the continental 

climates of the northern latitudes. These continental 

climates are warmer in the summer than the montane 

climates inhabited by taxa of the P. engelmannii complex.

Climate data, for instance, show that habitats occupied 

by P. glauca range up to 2,100 degree-days greater than 

5 °C and 21 °C for mean temperature in the warmest 

month, while maximums for the same variables in the 

P. engelmannii complex are 1,700 degree-days and 

18 °C, respectively. A warming climate in the north, 

therefore, should further increase the inhospitality of 

the contemporary boreal forests to taxa of the P. engelmannii complex.

One is led, therefore, to an independent corroboration of the conclusions of Thompson and others (2002): 

global warming should result in a large reduction in the 

distribution of the P. engelmannii complex. While the 

distribution of P. glauca may expand to the north, and 

P. pungens may become the dominant species of the genus 

in the Southwest and Great Basin, the opportunities 

for immigration of taxa of the P. engelmannii complex are 

greatly limited. 
USDA Forest Service RMRS-GTR-134. 2004 17

Extirpation and immigration notwithstanding, a 

portion of the region currently inhabited by taxa of the 

P. engelmannii complex should remain suitable for taxa 

of the complex throughout the century. Table 6 shows 

clearly, however, that those genotypes expected to be the 

best suited for the novel climates may today exist at large 

distances from the site of their future climatic optima. 

This means that accommodation of a warming climate by 

taxa of the P. engelmannii complex will not only involve 

extirpation and immigration, but also should result in 

a complete redistribution of genotypes across the landscape. This conclusion is precisely the same as those 

reached for Pinus contorta of Western North America 

(Rehfeldt and others 1999, 2001) and for Pinus sylvestris

of Eurasia (Rehfeldt and others 2002). As argued for 

these pines, the process by which redistribution occurs 

most likely will involve selection and recombination in 

extant populations rather the migration. Calculations 

for the pines also suggest that the amount of change 

required to accommodate global warming is more than 

genetic systems are capable of achieving in a single 

generation of selection. Adjustment, therefore, may 

require as many as 10 generations and may take more 

than 1,000 years. Although data are not available for 

making similar calculations for Picea, there is no reason 

to expect the natural processes to differ greatly. 

Practical Applications

An understanding of the adaptation of populations 

to heterogeneous environments has direct application 

in reforestation, the goal of which invariably includes 

producing for the future populations that are physiologically attuned to their environment. In a stable climate, 

this goal can be achieved by establishing geographic 

limits beyond which seeds from existing populations 

are not transferred. Although fundamentally sound, 

such geographically based guidelines are not suitable 

for extrapolation and, therefore, are not suitable (1) in 

regions where genetic variation is unexplored and (2) 

during periods of climate change. As demonstrated by 

the present results, climate-based seed transfer guidelines overcome such restrictions. 

Table 5 can be considered as output from a prototype of an expert system for guiding seed transfer 

(see Monserud and Rehfeldt 1990). The expert system 

requires a database containing an extensive inventory 

of locations and their climates within the distribution 

of a species. From this database, queries can be made 

for the location of genotypes expected to be compatible 

with the climate at a specific planting site. According to 

table 5, for example, in the contemporary climate, seeds 

suitable for a planting site at low elevation at location 24 

could come from an interval of 300 m surrounding the 

planting site, from slightly lower elevations in peripheral 

localities where the climate is somewhat more severe, or 

from higher elevations where the climate is more mild. 

Seeds suitable for a high elevation planting site at the 

same locality, however, could come from a broad range 

of sites, generally at high elevations where the climate 

is similar. When the climate shifts, however, a different 

assortment of seeds becomes best suited to the same 

planting site. The seed lots best suited to these new 

climates readily can be selected by the expert system 

(see table 6). 

Climate-based seed transfer guidelines, therefore, 

represent a flexible means for managers to approach the 

goal of establishing populations physiologically attuned 

to the future even during periods of climate change. As 

shown herein, converting geographically-based transfer 

guidelines to those that are climate-based merely 

requires recasting clines using climatic predictors.

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