This file was created by scanning the printed publication.
Errors identified by the software have been corrected;
however, some errors may remain.
American
Journal
of Botany80(3): 330-343. 1993.
GENETIC VARIATION IN THE PONDEROSAE
OF THE SOUTHWEST'
GERALD
E. REHFELDT
Intermountain
ResearchStation,U.S. Department
ofAgriculture,
ForestService,
1221S. MainStreet,Moscow,Idaho83843
Ninety-five
seedlingpopulations
of southwestern
ponderosapine(Pinusponderosavar.scopulorum)
alongwithsingle
ofPinusengelmannii
and Pinusarizonicawerecomparedin fourenvironmentally
commongardens.
populations
disparate
Differentiation
amongponderosapinepopulations
wasdetected
fora diverseassortment
ofvariablesthatincludedpatterns
of shootelongation,
measuresof growthpotential,
winterand springfreezing
damage,and leafcharacteristics.
Multiple
regression
modelsaccountedforas muchas 85% ofthevarianceamongpopulations
anddescribed
complexclinesthatwere
P. ponderosa,P. arizonica,and P. engelmannii
werereadily
dominatedby elevationaland latitudinal
effects.
Although
P. ponderosa
ofprogenies
fromone population
theperformance
differentiated,
suggested
introgression
primarily
involving
and P. arizonica but also implicatingP. engelmannii.
Microevolutionaryprocesses allow naturalsystemsof
genetic variabilityto be molded by environmentalheterogeneityto produce populationsgeneticallyattunedto
a local environment.As demonstratedrepeatedlyby the
experimentalapproachofClausen, Keck,and Hiesey(e.g.,
1940), culturingplantpopulationsin common gardensso
frequentlydemonstratesgeographicvariationthatadapof populationscommonlyis assumed
tive differentiation
(Mayr, 1970). Species, however,face environmentalheterogeneitywithdiverseassortmentsofgeneticvariability.
As a result,responsesto naturalselectionare varied. In
the Rocky Mountains, for example, clines in adaptive
amongpopulationsseparated
traitssuggestdifferentiation
in altitudeby 200 m in Pseudotsuga menziesii(Rehfeldt,
1989), 250 m in Pinus contorta(Rehfeldt,1988), 350 m
in Pinus ponderosa var. ponderosa (Rehfeldt,1991), and
450 m in Larix occidentalis(Rehfeldt,1982). But in Pinus
monticola,clines cannot be demonstratedforeithermorphological traits(Rehfeldt,Hoff,and Steinhoff,1984) or
allozymes (Steinhoff,Joyce,and Fins, 1983). Yet in the
comountainsofnorthernIdaho, thesespecies frequently
occur (Daubenmire and Daubenmire, 1968) across as
muchas 1,000 m ofelevation,an intervalassociated with
days (Baker, 1944).
a differenceof about 90 frost-free
Populations of these wind-pollinatedsympatricconifers
to selectionalongsimilar
have respondedmuchdifferently
environmentalgradients.An environmentperceived as
beingcoarse-grainedto Pseudotsugamenziesiiand P. conto P. monticola.
tortais apparentlyfine-grained
To the widespreadponderosa pine (P. ponderosa), the
grain of the spatially variable environmentis unquestionablycoarse. Geographic races are recognizedwithin
each of three varieties (Conkle and Critchfield,1988).
Genetic variation among populations within races is
growthand
abundant fora varietyof charactersreflecting
fieldtests(Conkle, 1973; Read, 1983;
survivalin long-term
Sheppard and McElderry,1986; Van Haverbeke, 1986),
growthand development in common gardens (Madsen
and Blake, 1977; Read, 1980; Rehfeldt,1990, 1991), toleranceto environmental
stress(Read and Sprackling,1981;
Rehfeldt,1986a, b), disease resistance(Hoff,1988b, 1990,
1991), and allozymes (Mitton et al., 1977; Mitton,Sturgeon, and Davis, 1980; Linhart et al., 1981; Hamrick,
Blanton,and Hamrick, 1989).
These disparateresultsdocumentextensivepopulation
differentiation,
much of which is interpretableas adaptation to heterogeneousenvironments.Nevertheless,to
P. p. var. ponderosaof the Inland Northwest,the grain
bywhichtheenvironmentis perceivedapparentlyis much
finerthan forpopulationsof P. p. var. scopulorum
on the
Colorado Plateau. Populationdifferentiation
is associated
with habitats that differby at least 35 frost-free
days in
the Northwest(Rehfeldt,1991) but by only 22 frost-free
days on the Colorado Plateau (Rehfeldt, 1990). In addition,geneticvariationwithinpopulationstendsto occur
in patches (Linhart, 1989) and is pronounced for numeroustraitsreflecting
growth(Conkle, 1973; Namkoong
and Conkle, 1976; Rehfeldt,1980), allozymes(Linhartet
al., 1981), and adaptation to the biotic (Hoff, 1988a, b,
1991) and abiotic environment(Rehfeldt,1992).
The Southwestis a regionin whichthegeneticstructure
of ponderosa pine (P. p. var. scopulorum)
populations is
poorly understood. While rangewide provenance tests
of southunanimously attest to genetic differentiation
westernpopulations fromthose to the north(Squillace
and Silen, 1962; Hanover, 1963; Wells, 1964; Read, 1980),
patternsofvariationamong southwestern
populationsare
obscure. This paper reportscommon gardenstudiesand
presents models that describe genetic variation. While
differentiation
can be eitherrandom or systematic,it is
the systematicpatternsthatinvariablycorrespondto environmentalgradientsand, therefore,most likelyresult
fromnatural selection. Because systematicpatternsare
predictable,functionalmodels can be applied to topics
to gene conservation.
rangingfromartificialreforestation
Two factorscomplicate studies of geneticvariation in
southwesternponderosa pine. First,Conkle and Critch' ReceivedforpublicationI June1992; revisionaccepted12 Nofield(1988) separate the Rocky Mountain race fromthe
vember1992.
in southern
race of P. p. var. scopulorum
southwestern
Department
TheauthorthanksA. K. ArbabandtheNavajo Forestry
Colorado.
The
of the
Utah
and
southeastern
abruptness
of theGila
and variouspersonnel
R. M. Jeffers
forclosecooperation;
transitionbetween races, however, is largelyunknown.
and D.
assistance;
S. P. Wellsfortechnical
NationalForestforsupport;
Secondly,in southeasternArizona and southwesternNew
criticism.
T. Lesterforstimulating
330
March 1993]
REHFELDT-GENETIC
UTAH
331
VARIATION IN PONDEROSAE
COLORADO
A/
370
5
tX
ARIZONA
f
i.
C-SangredeCristoMts
320
% X%
% K
)t
Fig. 1. Map of the regionof studyshowingthe distributionof ponderosa pine (shading,fromLittle, 1971) and populations sampled. Letters
kieygeographiclocalities referencedin the text.Circle ponderosa pine; triangle= Arizona pine; square = Apache pine.
and
sampledtheecologic,geographic,
Mexico,ponderosapineco-occurswithtwootherpines, ulationsotherwise
of the speciesin the Southwest
Arizonapine (P. arizonica)and Apachepine (P. engel- elevationaldistribution
groupsof
(Conkleand (Fig. 1). In thispaper,populationsreference
mannii),thethreeof whichare interfertile
individuals.The local1988)membersofthePonderosaesubsection adaptivelysimilar,interbreeding
Critchfield,
provincesthatmayconoftheDiploxylonsubgenusof Pinus(Littleand Critch- itiesofFig. 1 arephysiographic
in fact,Arizonapine was tainnumerouspopulations.
field,1969). Until recently,
The 97 populationsincludedsinglepopulationsofArconsideredto be a varietyof ponderosapine(cf.,Perry,
theexperimentation
and in izonaandApachepines(Fig.1).After
variationin terpenes
1991).Studiesofphenotypic
theconclusion wasunderway,itbecameobviousthata population(Barhaveprompted
coneandleafmorphologies
amongthesespeciesiscommon foot)in theChiricahuaMountains(localityJ,Fig. 1) was
thatnaturalhybridization
how- eitherintrogressed
or containeda mixtureofponderosa
(Peloquin,1971,1984).The extentofintrogression,
on geneticvari- and Arizonapines.As a result,theworkinghypothesis
ever,is unknown.Whileconcentrating
ofnoninterbreedwasa mixture
raceofP. p. var. scopulorum, wasadoptedthatBarfoot
ationin thesouthwestern
all seedeliminating
objectivesofcontributing ingspecies.Underthisassumption,
thisreportassumessecondary
ofArizona
oftheinterrelationships
amongthe lingsfromBarfootwiththeleafcharacteristics
to an understanding
fromthePinapine(fouror fivenarrowleaves/fascicle)
Ponderosae.
southwestern
lenoMountains(Fig. 1) allowedBarfoottobe considered
as a populationofponderosapine.
MATERIALS AND METHODS
was studiedby comparing
Populationdifferentiation
of seedlingsfrom97 popthegrowthand development
tests.Each population
ulationsin fieldand greenhouse
bya bulkedsampleofeightto tenwindwas represented
pollinatedconesfromeach oftentrees.To decreasethe
sampledtreeswereat least30
ofco-ancestry,
possibilities
m apartand wereseparatedbythecrownsofat leasttwo
weremadefrom
no collections
trees.Although
intervening
whereponthelowestlimitsofthespecies'distribution
individualsin woodlands
derosapineoccursas scattered
dominated by Pinus edulis or Juniperusspp., these pop-
weregrown
fromeachpopulation
Fieldtests-Seedlings
(65 cm3)ina shadehouseinMoscow,
inplasticcontainers
Idaho (latitude46.70 N, longitude1170 W), and first-year
seedlingswereplantedin commongardensnear Priest
River,Idaho; WindowRock,Arizona;and SilverCity,
New Mexico.PriestRiveris 190 km northof Moscow;
WindowRock and SilverCityare shownin Fig. 1. The
PriestRiverand WindowRocktestswereplantedin the
fall,whiletheSilverCitytestwas plantedthefollowing
spring.At all sites,ten seedlingsfromeach population
wereplantedin rowplotswithineachoffourrandomized
332
[Vol. 80
AMERICAN JOURNAL OF BOTANY
1. Physicalcharacteristics,
meanperformance,
andgeneralclimateat thefieldtestsites
TABLE
TABLE
2. Description
ofthevariables
analyzed
Variable,
Test site
Characteristic
Latitude (0N)
Elevation (m)
Frost-free
period (d)
Dry season
Survival (%)
4-yrheight(cm)
PriestRiver
Silver City
Window Rock
48.5
750
32.8
1,900
35.9
2,400
90
Summer
99.5
80.8
210
Winter-Spring
97.6
57.1
110
Winter-Spring
89.8
26.1
completeblocks.Rows wereseparatedby 0.6 m, while
0.3 m separatedseedlings
withinrows.Allsitesweretilled
andfencedbeforeplanting
andwereirrigated
andweeded
Testperiodically
forthreegrowing
seasonsafterplanting.
ingwas completedafterthetreesreachedage 4.
Because environmental
effects
at theseplantingsites
anddevelopment
oftrees
differentially
affected
thegrowth
(Table 1), a different
set and numberof variableswas
necessary
fordescribing
growth
and development
at each
site(Table 2). Fieldteststhuscontributed
a diversearray
of 17 variablesthatincludedmorphometric
traits,
spring
and winterfreezing
and surdamage,leafmorphology,
vival. Of thesevariables,notethatthedeviationsfrom
regression
accountfortheautocorrelation
oftheannual
shootgrowthof treesand are,thereby,
inderelatively
pendentof prioreffects.
These values thuscan reflect
in a shortperiod
environment
adaptationto a particular
oftime.The variablesPRDEV and WRDEV reflect
4-yr
heightas ifall individualshad been thesame heightin
yr2. Becauseof springfrostdamageat thebeginning
of
thethirdgrowing
seasonat SilverCity,SCDEV3 reflects
theeffects
ofspringfrostinjuryon thethird-year
growth
of treesfroma commonheightat yr 2; and SCDEV4
of
reflects
thegrowth
thatoccurredin yr4 independently
ofyr3.
thefrostdamagethatoccurredat thebeginning
tests-Seedlingsfromeachpopulation
Greenhouse
were
grownfor6 mo inplasticcontainers
(740 cm3)in a shadehouseat Moscow,Idaho. The experimental
designconsistedof nineseedlingsgrowingin rowplotsin each of
threeblocks.Traysof containers
threeplots
containing
weretransferred
intoan unheatedgreenhouse
forthewintermonthsand, in earlyMarchof the secondgrowing
ofabout
season,wereexposedto a daytimetemperature
25 C, whichwas allowedto cool to a minimumof 13 C
at night.All seedlingsweremeasuredthreetimeseach
oftheterminal
weekuntilelongation
shootwaswellunder
way.Thereafter,
each seedlingwas measuredtwiceeach
weekuntilelongationof thepreformed
shootwas complete.
Periodicmeasurements
ofinallowedshootelongation
dividualtreesto be modeledwitha logisticfunction
with
a hyperbolic
and Wykoff,
timeterm(Rehfeldt
1981):
Description
PRHT4
PRDIA
PRDEV
Age4 height
Age3 diameter
Deviationfromregression
of4-yrheighton 2-yr
height
PRCL
Scoresofthecolor(green= 0 or blue= 1) ofthesucculentshoot
PRRTO
Ratioofthe3-yrheightto the3-yrdiameter
PRLL
Leaflengthfroma fasciclenearthecenterofthe3-yr
terminal
shoot
PRLW
Leafwidthfroma fasciclenearthecenterofthe3-yr
terminal
shoot
PRLNM
Averagenumberofleavesin tenfascicles
distributed
the3-yrterminal
throughout
shoot
SCHT4
Age4 height
SCSF
Scores(I to 4) ofdamageto elongating
shootsfroma
springfrostin year3
SCDEV3
Deviationfromregression
of3-yrheighton 2-yr
height
SCDEV4
Deviationfromregression
of4-yrheighton 3-yr
height
WRHT4
Age4 height
WRDEV
Deviationfromregression
of4-yrheighton 2-yr
height
WRWI
Deathoffoliagefromwinter
desiccation
duringyears
2 and 3
WRLL
Leaflengthfroma fasciclenearthecenterofthe3-yr
shoot
WRDEAD
Scoreofmortality
at anyage
GHEL
Lengthoftheterminal
shootproducedin year2
GHS2
ofelongation:
Initiation
thedaybywhichthe2-yrterminalshoothad elongated
2 mm
GHS8
Startofelongation:
thedaybywhichthe2-yrterminal shoothad elongated8 mm
GHEN
Cessationofelongation:
thedaybywhichall but2
mmofelongation
had occurred
GHDR
Durationofelongation:
thenumberofdaysbetween
theinitiation
and cessation
GHRT
Elongation
perdayduringtheperiodforwhich20%
to 80% oftheshootelongated
GHHT2
Age2 height
GHDIA
at thesoilsurface
Age2 diameter
GHLL
Averagelengthofleavesfromtenfascicles
distributed
the2-yearterminal
shoot
through
GHLNM
Averagenumberofleavesin 10 fasciclesdistributed
the2-yrterminal
shoot
through
GHRTO
Ratio ofthe2-yrheightto the2-yrdiameter
a First
twoletters
ofvariablecodethetestsite:PR = PriestRiver,SC
-Silver City,WR = Window Rock, and GH = Greenhouse.
individualtrees(Table2). Additionalmeasurements
producedfivemorevariablesthatreflected
growthand developmentin thegreenhouse
environment.
Patternsof variation-Population differentiation
was
assessedfromanalysesofvariance(SAS Institute,
1985,
usingType III estimablefunctions),
whichwere performedaccordingto thefollowing
modelofrandomeffects:
Yijk =
+ Pi + Bj + Ei. + Wijk
Y = (1 + be{-rx + (c/X)})-l
whereYijk is an observation
on seedlingk in blockj from
whereY is theproportion
oftotalincrement
attainedby populationi; ,uis the mean;Pi and Bj are theeffects
of
and e is the populationsand blocks,respectively;
day X; b, r,and c are regression
coefficients;
Eii is the experibase ofnaturallogarithms.
statistics
Regression
ofblockswithpopulations;
produced mentalerror,theinteraction
allowedcalculationofsix variablesthat andWijkis thesampling
bythisfunction
error.Undertheassumption
that
wereused to describethepatternof shootelongationof blocksand populationsare randomvariates,theexperi-
March 1993]
REHFELDT-GENETIC
333
VARIATION IN PONDEROSAE
mentalerrorbecomes thevarianceappropriatefortesting
differencesamong populations. For these analyses, the
harmonicmean ofobservationsperplotwas 9.92 at Priest
River, 9.70 at Silver City, 8.62 at Window Rock, and
8.84 in the greenhouse.
An attemptwas made to reducethe numberof dimensions about whichdifferentiation
was beingexpressedby
usingprincipalcomponentanalyses(SAS Institute,1985)
on the data fromeach testsite. However, differentiation
ofpopulationswas so pronouncedthattheprincipalcomponentanalysisallowed thenumberof variatesforwhich
populations differedsignificantly
to be reduced by only
8. Because ofthis,because theuse ofprincipalcomponents
resultsin a loss of information(Johnsonand Wichern,
1982), and because principalcomponents oftenare impracticalin the generalapplication of regressionmodels,
subsequentanalyses involved only the originalvariates.
Multiple regressiontechniqueswere used to develop a
generalmodel ofgeneticvariationaccordingto procedures
detailed earlier(Rehfeldt,1989):
1) Deriving independentvariables fromlatitude(LT),
longitude(LN), and elevation that could serve as surrogates forthe complex three-dimensionalenvironmental
gradientsthathave operatedin naturalselection.For the
presentanalyses,independentvariables includedthe first
and second powersof elevationand the first,second, and
thirdpowers of LT, LN, LT x LN and LT . LN. The
lattertwo of these variables produced a gridfromnorthwest to southeast and fromnortheastto southwest,respectively;
2) screeningthe independentvariables by stepwiseregression,the best model of which was judged relativeto
statisticalsignificance,
theMallows statistic,and patterns
displayed by the residuals (Draper and Smith, 1981);
3) refininga stepwise model with multipleregression
to develop the most parsimoniousmodel;
4) and finally,plottingelevationaland geographicpatternsof variationto assure thatthe models were sensible
biologically.
A fifthstep, verificationof the model, could not be
attemptedbecause independentdata were not available.
Models were developed forponderosa pine by excluding populationsof Apache and Arizona pine. This left95
populations forthe regressionanalyses. Excluding seedofArizona
lingsfromBarfootthathad leafcharacteristics
pine removedninetreesfromgreenhousetests,threefrom
PriestRiver tests,threefromSilver City,and none from
Window Rock where winterinjuries and mortalityhad
decimated the Barfootpopulation.
Rates of differentiation
along geographicor elevational
clines were interpretedrelative to the least significant
difference
(Steel and Torrie, 1960) among populationsat
the 20% significancelevel (lsd 0.2). Values of lsd were.
used because stepwisemodels developed fromnumerous
and overindependentvariables are subjectto overfitting
parameterizing(Draper and Smith, 1981). The use of lsd
guardedagainstacceptingfallaciousresults.The 20% significancelevel was used to guard against accepting no
differences
among populations when differences
actually
exist (typeII errors);such errorsprovide the greateptpowhenmodels are applied.
tentialforfaultyinterpretations
Values oflsd werecalculatedfromtheinteractionofblocks
and populations in the analysis of variance.
Using lsd to assess rates of differentiation
intuitively
suggeststhatinterpretations
are dependenton sample sizes
and experimentalerrors(uniformityof cultural conditions). However, because the cones fromten trees had
been bulked, variances within plots are composed not
onlyofenvironmentaleffectsat theplantingsite,but also
of genetic variances within populations. Consequently,
lsd would stillreflectthe geneticvariances withinpopulations even if samples were largeand the controlof microenvironmentaleffectswas complete.
Interrelationamong species-Canonical discriminant
analyses (SAS Institute,1985) were used to assess multivariaterelationshipsamongspecies.These analyseswere
performedseparatelyon data fromeach test site. They
used observationson individual seedlingsfromeach of
the populations of Apache and Arizona pines, fivepopulations of ponderosa pine, and the Barfootpopulation.
The fiveponderosa pine populationsweregeographically
proximal and elevationallysimilar to the Arizona pine,
Apache pine, and Barfootpopulation. Of the five,three
came fromthe Mogollon Rim and one each came from
theTularosa and Pinaleno Mountains(Fig. 1). These analyses also used all trees fromBarfoot,regardlessof leaf
morphology.Barfootwas of particularinterestbecause
the performanceof its progeniessuggestedthat the parentalpopulationwas eithera hybridswarmor a mixture
of Arizona and ponderosa pines.
RESULTS
The resultsconsiderfirst,variationamong populations
of ponderosa pine, and second, biosystematicimplications.
Population differentiation
-Differences among populations were detected(P < 0.05) forall but one variable,
withtheeffectsofpopulationsaccountingforat least 40%
of the total variance for ten of the variables (Table 3).
Because of the experimentaldesign thatwas used, weak
effectsforblocks (Table 3) meant thatthe intraclasscorrelation for the effectsof populations approximatesthe
ratio of the totalgeneticvariance to the phenotypicvariance. The size of these intraclasscorrelations,therefore,
atteststo pronounceddifferentiation
of populations.
The strongesteffectsof populations were associated
with the cessation of shoot elongation(GHEN), the duration of elongation(GHDR), and numberof leaves per
fascicle(PRLNM and GHLNM), variablesforwhichpopulation effectsaccounted foras much as 72% of the total
variance. For most of the variables, however, much of
the total variance was associated with sampling errors
that are composed of microenvironmentaleffectsat the
test site and geneticvariances withinpopulations. Since
geneticvariances formany of these traitstend to be pronounced (Rehfeldt,1992), largesamplingerrorsalso can
be expected.The deviation fromregressionof 4-yrheight
on 2-yrheightat Window Rock (WRDEV) was the only
variable for which no differenceswere detected among
populations,a resultsuggestingthatgrowthpotential(innate abilityto produce and assimilate photosynthatein
the absence of environmentaleffectsthat mask the ge-
334
[Vol. 80
AMERICAN JOURNAL OF BOTANY
Resultsofthe
ofanalysisofvariance.
and results
amongpopulations,
rangeofmeandiferences
pinepopulations,
TABLE3. Mean ofall ponderosa
to thesum ofall
theratioofthevariancecomponent
fortheindicatedeffects
as intraclass
correlations,
anlaysesof varianceare presented
components
Source of variance
Experimental
error
Populations
Range
Variable
PRHT4
PRDIA
PRDEV
PRCL
PRRTO
PRLL
PRLW
PRLNM
SCHT4
SCSF
SCDEV3
SCDEV4
WRHT4
WRDEV
WRWI
WRLL
WRDEAD
GHEL
GHS2
GHS8
GHEN
GHDR
GHRT
GHHT2
GHDIA
GHLL
GHLNM
GHRTO
Units
Mean
Maximum
Minimum
Blocks
cm
mm
cm
Score
cm/mm
cm
mm
Count
cm
Score
cm
cm
cm
cm
%
cm
%
mm
d
d
d
d
mm/d
mm
mm
cm
Count
mm/mm
81
17
0.0
0.8
2.9
17
1.7
3.1
57
2.2
0
0
26
0
27
7
11
134
4
8
38
34
6
240
6
14
3.1
38
97
21
11
1.0
3.4
19
1.8
3.2
85
2.8
7
10
33
5
92
8
35
175
6
11
50
46
8
319
9
167
3.4
44
58
14
-11
0.4
2.5
14
1.6
2.0
28
1.5
-4
-10
19
-4
0
5
0
71
3
7
29
25
4
130
4
106
3.0
30
0.07**
0.07**
0.01**
0.00
0.02**
0.03**
0.04**
0.00
0.03**
0.01**
0.01
0.07**
0.01 **
0.06**
0.05**
0.16**
0.04**
0.00
0.01*
0.00
0.01**
0.01 **
0.00
0.00
0.01 **
0.00
0.00
0.01*
Sampling error
0.51
0.47
0.80
0.89
0.67
0.67
0.71
0.26
0.46
0.82
0.76
0.60
0.61
0.66
0.70
0.58
0.71
0.55
0.82
0.74
0.48
0.51
0.73
0.54
0.53
0.76
0.37
0.65
0.02**
0.06**
0.06**
0.00
0.09**
0.04**
0.08**
0.02**
0.11**
0.04**
0.11**
0.19**
0.16**
0.25**
0.06**
0.23**
0.16**
0.05**
0.15**
0.19**
0.02
0.02
0.04*
0.07**
0.06**
0.04*
0.02*
0.19**
0.40**
0.41 **
0. 14**
0.10**
0.22**
0.26**
0.17**
0.72**
0.41**
0.13**
0.13**
0.15**
0.22**
0.03
0.19**
0.03*
0.09**
0.40**
0.03*
0.07*
0.50**
0.46**
0.23**
0.40**
0.41 **
0.20**
0.61**
0.15**
*Significance of F-value at 0.05 > PF> 0.01.
* Significanceof F-value at P < 0.0 1.
notype) had been masked in the rigorousenvironment
(Table 1).
among populations is ilThe degreeof differentiation
lustratedreadilyby the variables describingshoot elongation.In the greenhouse,shoot elongationof individual
treeswas completedbetween 17 and 62 d afterthegreenhouse was warmed. This meant that seven to 19 observations were available for the logistic regressionsthat
describedshoot elongationof individual treesnearlyperfectly:values of R2 ranged from0.93 to essentially1.0,
averaging0.99.
Because shoot elongation is one of a sequence of developmental events that must be completed withinthe
patternsof shoot growthillusseason, different
frost-free
As shown
environments.
trateadaptationto heterogeneous
in the startof elongationwere small,
in Fig. 2, differences
in therate,duration,cessation,and amount
but differences
of elongation were pronounced. In this figure,the high
growthpotential and long duration of shoot growthof
treesfromtheBradshawMountains (localityE) describes
a populationfromthelowestelevation(1,700 m) sampled
in the study.The remaininggraphsare for populations
fromabout the same elevation (2,300-2,700 m) but differentgeographiclocalities. In contrastto southernpopulations,those fromthe north(e.g., San Juan Mountains
[localityB] or MarkaguntPlateau [localityA]) combined
an early cessation of elongationwith low growthpotentials.
Multiple regressionmodels accounted for statistically
significant(P < 0.01) proportionsof the variance among
populationsforall variables (Table 4). Values of R2 were
as high as 0.85, averaging 0.58. For only eight of the
variables did regressionmodels account forless than half
of the variance among populations. These resultsthus
demonstratethatmuchofthevariationfollowssystematic
200
E
~150150
----H
~~~~~~/
F
I
z
A
..D100
I-~~~~~~~~~~~~~/
z o
0
'/
1
/
-50
w
~~~~~~~~~~~/P
00
15
30
45
60
Fig. 2. Mean cumulative shoot elongationof seedlingsfromsix localities identifiedin Fig. 1.
March 1993]
REHFELDT-GENETIC
335
VARIATION IN PONDEROSAE
TABLE4. Resultsofmultiple
regression
analyses.Geographic
patterns
ofvariation
are keyedtoFig. 4. All regressions
werestatistically
significant
at probabilities
lessthan0.01
Patternof geneticvariation
Dependent variable
PRHT4
PRDIA
PRDEV
PRCL
PRRTO
PRLL
PRLW
PRLNM
SCHT4
SCSF
SCDEV3
SCDEV4
WRHT4
WRDEV
WRWI
WRLL
WRDEAD
GHEL
GHS2
GHS8
GHEN
GHDR
GHRT
GHHT2
GHDIA
GHLL
GHLNM
GHRTO
R2
Independent
variables
0.85
0.82
0.59
0.49
0.52
0.75
0.23
0.64
0.85
0.54
0.63
0.50
0.71
0.20
0.79
0.40
0.26
0.83
0.16
0.27
0.83
0.84
0.66
0.83
0.83
0.60
0.55
0.44
6
5
3
8
3
6
3
7
3
8
4
2
3
3
3
2
3
4
5
6
5
5
4
5
5
7
5
5
Elevation cline
Sign
Negative
a
Positive
Negative
Negative
Negative
Slope
Steep
Steep
Steep
Shallow
Steep
Steep
Geographiccline
Shape
Slope
Direction
Nonlinear
Nonlinear
Nonlinear
Linear
Linear
Nonlinear
Steep
Steep
Steep
Shallow
Shallow
Moderate
Shallow
Shallow
Moderate
Moderate
Moderate
Moderate
Moderate
Shallow
Steep
Shallow
Shallow
Steep
Shallow
Moderate
Steep
Steep
Moderate
Moderate
Steep
Moderate
Moderate
Shallow
South to North
South to North
South to North
Southwestto Northeast
Northto South
South to North
Northeastto Southwest
South to North
South to North
Northeastto Southwest
Southwestto Northeast
South to North
South to North
Southeastto Northwest
South to North
Southeastto Northwest
Southwestto Northeast
South to North
Southeastto Northwest
Southwestto Northeast
South to North
South to North
South to North
South to North
South to North
South to North
South to North
North to South
b
Negative
Negative
Positive
Negative
Shallow
Steep
Moderate
Steep
Nonlinear
Linear
Linear
Linear
b
Negative
Shallow
Linear
b
b
Negative
Positive
Negative
a
a
Negative
Negative
Shallow
Shallow
Moderate
Shallow
Shallow
Moderate
Moderate
Linear
Linear
Linear
Nonlinear
Nonlinear
Linear
Linear
b
Negative
Negative
Negative
a
Negative
Moderate
Moderate
Moderate
Shallow
Shallow
Nonlinear
Nonlinear
Linear
Nonlinear
Linear
No generalsign.
b
No elevational cline.
a
patternsthat regressionmodels are remarkablycapable
ofdescribing.Regressionequations are available fromthe
author.
The models described genetic variation as occurring
along both elevational and geographicclines (Table 4).
Elevational clines (Fig. 3) were detectedforall but five
of the variables and exhibiteda varietyof shapes, slopes,
and signs. Ten variables exhibitednonlinearelevational
clines, all of which were of shape similar to those of
PRHT4 and PRDEV in Fig. 3. While the sign of the
elevational cline is directlyinterpretableforlinearclines,
onlya generalcant describesthe slope ofnonlinearclines;
thus,the generalrelationshipforPRHT4 is negativeand
that for PRDEV is positive (Fig. 3). The strengthof a
relativeto lsd 0.2: forclines of steep
cline was interpreted
equal to lsd 0.2 are expected to occur
slope, differences
betweenpopulationswithinthe same geographiclocality
that are separated by less than 300 m of elevation; for
those of moderateslope, 300 to 500 m; and forthose of
shallow slope, more than 500 m.
Together,theclinesillustratedeclininggrowthpotential
as the elevation of the population increases. Thus, the
ratios,
durationofdevelopmentalevents,height-diameter
leaf lengths,and tree heightswere negativelyrelated to
elevation. In addition, as elevation increased,the color
of succulent shoots tended to change from blue-green
towardgreen,thenumberofleaves perfascicledecreased,
damage fromthespringfrostat SilverCityincreased,and
mortalityat Window Rock increased.
Nonlinear elevational clines typifiedthe general response of manyvariables measuredat PriestRiver,a site
to whichsome populationsweretransferred
northas much
as 170 of latitude.While linear clines commonlyrelated
growthpotential to elevation (see GHHT2 and GHLL,
Fig. 3), populationsfromthe mildestenvironments(lowestelevations)evidentlywereincapable offullyexpressing
theirgrowthpotentialat the northernsite. Yet, populations fromthe middle and high elevations seemed to be
unaffected.As a result,the populations fromhigh elevations had the most growthfroma common 2-yrheight
(PRDEV). The nonlinearcline for4-yrheight(PRHT4),
therefore,resultedfirstfroma differencein growthpotentialamong populations and second froma difference
in the degreethat the potentialwas masked.
Rates of differentiation
along linear clines are readily
interpretedin relationto lsd 0.2. For the 4-yrheightof
treesat SilverCity(SCHT4), thevariablewiththesteepest
linear cline, populations in the same locality that were
separated in elevation by about 300 m tended to be geneticallydifferent.
This variable, incidentally,integrated
genetic differencesin growthpotential with those controllingtoleranceto springfrosts.Frost injuryto populations fromhigh elevations accentuatedthe differences
in heightthataccrueaccordingto thenegativerelationship
betweengrowthpotentialand elevation.
Rates ofdifferentiation
alongthenonlinearclines,however,depend on the elevationsat whichcomparisonsare
made. The resultsfor4-yrheightat PriestRiver (PRHT4),
336
[Vol. 80
AMERICAN JOURNAL OF BOTANY
GHHT2
325-
E
~
~
o
<
~ ~
=
,
e
125
g-
H-~~~~~~~~~~~~~-
~
~
~
~
~
~ ~ ~ ~ ~
~~~~~~~~~~~~~~~~~~~~G
s
-
-
<
C])
r
160
0-
__
-T
2300
E LEVATI
100L
GHLL
170
ON
3000
1600
E LEVATI
15
PRHT4
30
2300
( M)
ON
(M )
PIRDEV
:H
-I
C
.
I,
25 -
ELEVATION
7.5-
75
-.
230
2300
1600
H--
.
3000
1600
ELEVATION
(M)
SCIOEV3
H-1
0-
-
3000
2300
ELEVATION
(M)
1600
(M)
SCHT4
,
1600
30 0
230
,
,
3000
2300
ELEVATION
(M)
Fig. 3. Mean performanceof ponderosa pine populations forsix variables plotted accordingto the elevation of theseed source. Variables are
referencedin Table 2. Each regressionline representsa geographiclocalityidentifiedin Fig. 1,>and the bracketnear theoriginquantifieslsd 0.2.
of populations from
for instance, imply differentiation
highelevations(>2,400 m) whenseparatedby about 220
among
m of elevation, but suggestlittle differentiation
populationsfromlowerelevations.Similarly,analysesof
growth from a common 2-yr height at Priest River
amongpopulationsfrom
(PRDEV) suggestdifferentiation
low elevation (<2,300 m) if separatedby at least 220 m
among the
of elevation, but imply littledifferentiation
populations at higherelevations.
The models thus suggestthatelevation and, therefore,
period are closelyrelatedto genetic
lengthofthefrost-free
This means that at localities such as the
differentiation.
Tularosa Mountains (locality G, Fig. 1) where the elevational distributionof ponderosa pine is broad (Fig. 3),
acrossthelandscape is pronounced.
geneticdifferentiation
A geographiccomponentto geneticvariationwas detectedin all of the variables (Table 3), and is illustrated
in Fig. 3 by regressionlines of different
intercept.This
component is shown in detail forsix variables in Fig. 4
wheregeneticvariationamong populationspredictedfor
an elevation of 2,400 m is representedby isopleths. In
this figure,the intervalbetween isopleths equals 1/2 lsd
0.2. This meansthatpopulationsseparatedbya geographic
distance equaling two intervalsare expected to differat
about the 20% probabilitylevel. Geographicpatternsfor
othervariablesare documentedin Table 4 wheretheslope
oftheclineis describedas steepwhenthedistancebetween
isopleths averages less than 50 km, moderate when avand shallow when averagingbetween 50 and 100 kkm,
eragingmore than 100 km. Here also, in describingthe
directionof a cline, the geographicdirectionlistedfirstis
that toward which highestvalues are predicted.
Althoughgeographicpatternsweredetectedforall variables, the model for the growthfrom a common 2-yr
March 1993]
REHFELDT-GENETIC
GHDR
337
VARIATION IN PONDEROSAE
WRWI
GHDIA
GHRT
GHS8
SCSF
Fig. 4. Geographic patternsof vaniationfor six variables predictedby regressionmodels for populations at a common elevation (2,400 in).
Isopleths connect populations of similarperformance,and the intervalbetweenisoplethsequals 1/2lsd 0.2. The mean isopleth(X) along with the
positive (+) and negative(-) deviations fromthe mean are marked.Variables are referencedin Table 2.
338
TABLE5. Simplecorrelations
ofselected
variables
withall variables.
withan absolutevaluegreater
than0.30 are
Onlythosecoefficients
arestatistically
presented.
Allcoefficients
atprobabilities
significant
lessthan0.001
PRHT4
PRDIA
PRDEV
PRCL
PRRTO
PRLL
PRLW
PRLNM
SCHT4
SCSF
SCDEV3
SCDEV4
WRHT4
WRDEV
WRWI
WRLL
WRDEAD
GHEL
GHS2
GHS8
GHEN
GHDR
GHRT
GHHT2
GHDIA
GHLL
GHLNM
GHRTO
[Vol. 80
AMERICAN JOURNAL OF BOTANY
GHDR
WRWI
SCDEV3
PRLL
PRCL
GHLNM
0.85
0.83
0.57
0.46
0.35
0.70
0.71
0.70
0.59
0.48
0.48
0.30
0.81
0.76
0.51
0.43
0.33
1.00
0.47
0.38
0.42
0.58
0.56
0.49
0.81
0.53
0.64
0.44
0.63
0.71
0.35
0.53
0.48
0.75
0.47
0.38
0.91
0.30
0.99
1.00
0.65
0.82
0.88
0.59
0.49
0.62
0.50
0.69
0.39
0.75
-0.58
1.00
0.32
0.75
1.00
0.46
0.42
0.56
-0.42
0.50
0.59
0.39
0.57
0.68
1.00
0.39
0.46
0.70
0.35
0.40
0.62
0.58
0.48
0.46
0.74
0.48
0.75
0.75
0.50
0.61
0.76
0.49
0.54
0.46
0.45
0.41
0.58
0.44
0.36
0.71
0.70
0.65
0.72
0.72
0.72
0.30
0.45
0.46
0.39
0.55
0.47
0.40
0.40
-0.31
0.30
200-
-
-
150
I
G
z
AZ
AP
100/
?
F
0.81
-
50-
0.49
0.53
0.30
0.40
0.49
0.49
0.53
1.00
-0.43
heightat Window Rock (WRDEV) failed to predictdifferencesthat exceeded lsd 0.2. This variable, moreover,
was the onlyvariable forwhichthe effectsof populations
lacked statisticalsignificance.Of the 27 remainingvariables, 18 exhibited geographic patternsthat were describedby latitudinalclines or variationsthereon(Table
3); fourof theselatitudinalclines are presentedin Fig. 4.
All but two of the latitudinalclines were inclinedtoward
thesouth;foronlytheratiosofheightto diameter(PRRTO
and GHRTO) were the largestvalues (least stockytrees)
found to the north.Together,these clines illustratethat
whenpopulationsfromthesame elevationare compared,
growthpotentialdecreases as latitudeincreases and the
lengthof the frost-free
period decreases.As a result,variables as different
as the duration of shoot elongationin
the greenhouse(GHDR) and winterinjuriesat Window
Rock (WRWI) can exhibitpatternsthatare nearlyidentical (Fig. 4).
While latitudinalclines were the strongestof the geographic patterns,two secondarypatternswere also evident. The strongestof these secondarypatterns(Fig. 4)
occurredacross an axis fromnortheastto southwestand
showed thatpopulationsfromtheRockyMountainswere
the earliestto begin shoot elongationin the greenhouse
(GHS8) and were the most susceptibleto damage from
the earlyspringfrostat Silver City(SCSF and SCDEV3).
Another weak pattern (not presented) occurred from
southeastto northwestand also tended to separate populations fromthe Rocky Mountains fromthose of the
Southwest.
Clinesinvolvingbothelevationand latitudewereprom-
0
15
30
45
60
D AY
Fig. 5. Mean cumulativeshootelongation
of seedlingsofArizona
pine(AZ),Apachepine(AP),andthreepopulations
ofponderosapine,
keyedto Fig. 1. Thepopulations
ofponderosapineweregeographically
proximaland elevationally
similarto thoseoftheotherspecies.
inent in regressionmodels for 16 of the variables. This
implies that many of the variables were stronglyintercorrelated(Table 5), a resultcommon in studies of populationdifferentiation
in ponderosapine (Rehfeldt,1986a,
b, 1990).
Biosystematicimplications-Some oftheeffects
ofpopulations detected by the analysis of variance (Table 3)
resultfromdifferences
betweenponderosa, Apache, and
Arizona pines.Differences
amongthesespeciesare readily
illustratedbypatternsofshootelongation(Fig. 5). Apache
pines tended to startelongationthe latest,elongated at
the slowest rate,and elongatedthe least, despite having
a longdurationofshootgrowth.SeedlingsofArizona pine
coupled a highrate of elongationwitha long durationto
achieve the highestgrowthpotential. Seedlings of ponderosa pine grew at a rapid rate but ceased elongating
early.
A comparisonof mean values of seedlingsrepresenting
the three species and the Barfoot population (Table 6)
thenumeroustraitsthatdifferentiate
shows,first,
Apache
and ponderosa pines. Apache pine's lowergrowthpotential, longerleaves, slower rates of shoot elongation,and
stockierformare prominent.Differences
betweenthepopulationofArizona pine and thoseofponderosapine center
on Arizona pine's higherleafcounts,narrowerleaves, and
slightlyhighergrowthpotential.It follows,therefore,
that
seedlingsof Arizona pine differedfromthose of Apache
pine in nearly all charactersmeasured. The trees from
Barfoot,however,minus those treeswith leaf morphologies of Arizona pine, differedfromponderosa pine for
about one-halfof the charactersthat separated Arizona
pine and ponderosa pine. These same trees, moreover,
differedfromponderosa pine forthreeof the characters
that separated Apache pine fromponderosa pine.
The canonical discriminantanalyses produced two eigenvalues thataccounted forat least 90% of the variance
among the fourgroups discussed above. In fact,for all
March 1993]
ofponderosa
populations
Mean valuesforseedlingsfromfive
pineas comparedto thoseoftheArizonapine,Apachepine,and
For populationsotherthanponderosapine,
Barfootpopulations.
pineby
ponderosa
thatdeviatefrom
onlythosemeansarepresented
an amountgreaterthanlsd 0.01. The populationsofponderosa
similartothose
proximalandelevationally
pineweregeographically
oftheothergroups
TABLE 6.
Variable
Units
Apache pine
PRHT4
PRDIA
PRDEV
PRCL
PRRTO
PRLL
PRLW
PRLNM
SCHT4
SCSF
SCDEV3
SCDEV4
WRHT4
WRDEV
WRWI
WRLL
WR
DEAD
GHEL
GHS2
GHS8
GHEN
GHDR
GHRT
GHHT2
GHDIA
GHLL
GHLNM
GHRTO
cm
mm
cm
Score
cm/mm
cm
mm
Count
cm
Score
cm
cm
cm
cm
%
cm
68.1
25.0
90.8
18.9
0.9
0.89
3.0
18.5
1.7
3.04
68.7
2.1
1.7
-0.9
28.8
-1.1
56.4
7.0
105.5
7.9
1.00
0.66
1.7
21.3
1.1
4.57
56.2
1.3
0.7
12.0
17.1
13.5
95.0
%
mm
d
d
d
d
mm/d
mm
mm
cm
Count
mm/mm
Barfoot
Arizona pine Ponderosa pine
95.2
52.3
47.8
4.86
3.29
20.7
9.8
1.00
2.6
1.5
3.30
92.5
21.0
153.6
4.0
8.6
42.9
38.9
7.0
276.8
7.5
15.7
3.09
37.2
92.5
111.5
6.9
13.6
51.4
44.5
4.2
190.7
9.4
ponderosa pine,althoughtherangeand densityofplotted
values are accuratelydepicted. Discriminant scores for
data fromSilverCityand Window Rock are notpresented
because theresultswereoflesserresolution.While it may
seem anomalous forthe two least naturalof the testsites
to provide the results of greatestresolution,one must
recallthat 1) Arizona and Apache pines performedpoorly
in the harsh environmentat Window Rock, and 2) the
leafcharactersand ratiosofheightto diameterthatprominentlydistinguishedthe species (Table 6) were not measured at Silver City.
For both the PriestRiver and greenhousedata sets,the
discriminantfunctionnicelyseparated the threespecies.
On the one hand, the greenhousedata suggestedthatthe
Barfootpopulationis a mixtureofponderosaand Arizona
pines, the two of which could be separatedby leaf morphology. Thus, the threeindividuals fromBarfootthat
had leaf morphologies of Arizona pine fell within the
clusterof Arizona pines (Fig. 6). But on the otherhand,
the distributionof Barfoot trees for Priest River data
implied introgressionthat may even implicate Apache
pine. Noteworthyis one seedling that displayed a leaf
morphologyof Arizona pine but otherwise resembled
ponderosa pine.
DISCUSSION
48.8
44.8
9.3
3.67
29.8
but the Window Rock data set, the firsttwo eigenvalues
accountedforat least 96% ofthevariancebetweengroups.
In Fig. 6, scores foreach seedlingare plottedforthe first
two canonical variables forPriestRiver and greenhouse
data. Only about one-halfof the plots are presentedfor
When grown in environmentallydisparate common
gardens,seedlingpopulations representingthe Ponderofor27
sae of the Southwestexhibitedgeneticdifferences
of the 28 morphologicaland developmental traitsanalyzed.While some ofthisvariationwas due to-interspecific
most reflectedgeneticdifferentiation
differences,
among
the 95 populations of ponderosa pine thatwere studied.
-Mathematical models were
Populationdifferentiation
remarkablysuccessfulin describingpatternsof genetic
variationacross the landscape. Because intercorrelations
among traitswere strong,similar patternsof variation
6-E
10E
EE
E
x
E
E
E
E
E E
E
p
*
*
r)ig.P
~~p
P p
z
Ap
pine;
P
P
-
FIRST
Fig. 6.
=
A
-1
A
A
N
P
*i*
A
*
1
I
A
35
CANONICAL
A
A=A P
mAAAA
A
AXIS
z
LU
o P CD
EEE
A
<AE
ppp PpPR*SA
-3
-
A
A
EA
tpFp
~
pine;E
E
E
E
E
E
o
Z
C28
E
3
<
E
~~~~~~~~~E
E E EE
E E
E
<
339
VARIATION IN PONDEROSAE
REHFELDT-GENETIC
AP
A
-10-26
CA
A
A
A
A AA
A
p
AA
-2
FI R ST
-
izo
'p
0
2
C A NONI C AL AXI S
studies(left) and PriestRivertests(right). P = ponderosapine;A = Arizona
Resultsofcanonicaldiscriminant
analysesforgreenhouse
= Barfoot
trees.
treeswith leafmorphologies
of?P.arizonica;circle= otherBarfoot
Apache pine;asterisk
340
AMERICAN JOURNAL OF BOTANY
were evident. The most prominentpatternsimplicated
elevation and latitude,two variables whose relation to
environmentalfactorsis well known.
Correlatedsets of traitsapparentlyhave resultedfrom
parallel selectionto produce coherent(sensu Clausen and
Hiesey, 1960) geneticsystemsinvolvingthe components
of an annual sequence of developmentalevents.This sequence begins with dehardeningin the spring;includes
shoot elongation,leaf expansion, bud development,diand concludeswithcold
ametergrowth,and lignification;
acclimation. As describedby Dietrichson(1964), the sequence has been molded to fitwithina growingseason
offinitelength,and, therefore,
thedurationofeventstends
to be intercorrelated.
As a result,a diverseassortmentof
variablesbecomes correlatedeven thoughthetraitsmight
be measuredin verydifferent
environments.In thiscase,
the duration of shoot growth(GHDR), the number of
leaves per fascicle(GHLNM and PRLNM), winterinjury
at WindowRock (WRWI), leaflengths(PRLL and GHLL),
stem color (PRCL), springfrostdamage at Silver City
(SCDEV3 and SCSF), and various expressionsof growth
intercorrelated.
Because shootgrowth
potentialarestrongly
in ponderosa pine is predetermined(Sacher, 1954), the
patternof shoot elongation can act as a surrogatefor
understandingthe adaptation of the entiresequence to a
heterogeneousenvironment.
distributed
Geneticvariationthatis systematically
along
environmentalgradientsundoubtedlyarises fromnatural
selection. Selection apparentlyhas molded a systemof
loosely intercorrelatedtraitsthat jointly adapt populations to the Southwest'sspatiallyheterogeneousenvironments,a conclusion compatiblewithresultsof studiesof
the same species fromthe Colorado Plateau (Rehfeldt,
1990), the Inland Northwest(Madsen and Blake, 1977;
Rehfeldt,1991), the Sierra Nevada (Callaham and Liddicoet, 1961; Conkle, 1973), and theeasternslopes of the
Rocky Mountains (Read, 1980, 1983).
Elevational clines have direct microevolutionaryinterpretations.As elevation increases, temperaturesdecrease, with a reductionin mean annual temperatureof
1 C leaving 12 fewerfrost-free
days (Baker, 1944). Consequently,seedlingsfrompopulations distributedalong
an elevational gradientdisplay adaptations to growing
seasons ofdifferent
length.When comparedin a common
environment,populations fromlow elevations expressa
high growthpotential,grow fora relativelylong period,
and become large; populations adapted to shortgrowing
seasons cease developmentearlyand tendto be small. In
the Southwest,an elevational intervalof 1,000 m tends
to be associated witha changeof90 frost-free
days (Baker,
1944). As shown in this study,populations separatedin
elevation by about 220 m tendto be different
genetically.
This suggeststhat populations occupyingenvironments
thatdifferby 20 frost-free
days tend to differgenetically.
This conclusionis remarkablysimilarto thatreachedfor
the same varietyon the Colorado Plateau (22 d) but is
much less than that obtained forP. p. var. ponderosa in
the Inland Northwest(35 d).
The latitudinalclines also seem to reflectadaptationto
frost-free
periods of variable length.Baker (1944) shows
thatfora constantelevation,the frost-free
period differs
by about 70 d betweenthe San Juan Mountains and the
Tularosa Mountains (Fig. 1). In Fig. 4, each isoplethrep-
[Vol. 80
resents1/2 lsd 0.2, a value thatequals the amount of differentiationexpected between populations separated by
110 m (10 frost-free
d) alongtheelevationalcline.If,then,
associated with
thelatitudinalclinesreflectdifferentiation
period,thereshouldbe about sevenisopleths
thefrost-free
separatingthe San Juanand Tularosa Mountains. Of the
latitudinalclines in Fig. 4, nine isopleths separate these
localitiesforGHDR, seven forWRWI, eightforGHDIA,
and six forGHRT. Clearly,much of the variation associated withbothlatitudeand elevationreflectsadaptation
to the lengthof the frost-free
period.
Secondary geographicclines were much weaker than
the latitudinalclines and tended to separatepopulations
fromthe Rocky Mountains fromthose of the Southwest
(see GHS8 and WRWI, Fig. 4). This separationwas also
apparentforpopulationsfromtheColorado Plateau (Rehfeldt,1990) and seemed to reflectthe transitionfroma
climate dominated by winter-springdroughts in the
Southwestto that characterizedby summerdroughtsin
the Rocky Mountains. Populations fromthe continental
climateof the Rocky Mountains initiateshoot elongation
rapidlywhile those fromthe Southwestinitiateshoot activitymore slowly. Because of this, southernColorado
populations were the most susceptibleto damage froma
late springfrostat Silver City (SCSF, Fig. 4).
Together,theelevationaland geographicclinesdescribe
complex patternsacross the landscape. Populations inmountainranges
habitingthe same elevation in different
tend to be different
genetically(Fig. 3). This also means
thatpopulationscapable ofsimilarresponsesare expected
elevationsin different
mountainrangto recurat different
es. For instance,populations capable of developing relatively long leaves (e.g., GHLL = 150 mm, Fig. 2) are
expectedat 1,750 m in theBradshawMountains (locality
E), 2,000 m in the Sacramento Mountains (locality H),
2,300 m in thePinaleno Mountains (localityI), and 2,800
m in the Tularosa Mountains (localityG).
Examining the frequencyby which populations that
exhibit similar responses recur with respect to several
variablesis facilitatedbyusingtheregressionmodel. Each
regressionequation can be used to generatea data base
containingpredictedvalues forthe entiregeographicand
elevational distributionof ponderosa pine withinthe region of study.Then, by surroundingeach observationin
the data base with a confidenceintervalof ? 1/2 lsd 0.2,
populations readilycan be grouped accordingto similar
responses.
The expectedrecurrenceofpopulationscapable of similar responsesis illustratedin Fig. 7 forsix targetedpopulations(pyramids).For populationsin thecenteroflarge
continuous distributions(Tularosa Mountains [locality
G] and Sangre de Cristo Mountains [localityC]) recurrence is widespread. But recurrenceis considerablyrestricted for populations in isolated mountain ranges
whetherthelocalityis on theperiphery(BradshawMountains [localityE] and SacramentoMountains [localityH])
or in the center (Defiance Plateau [locality D]) of the
species distribution.For the isolated ranges in southeastern Arizona (Pinaleno Mountains [localityl]),however, recurrenceis extremelylimited(Fig. 7).
Practical uses of models of genetic variation are numerous and diverse (see Rehfeldt,1991). For artificial
one can assume thatthe targetedlocations
reforestation,
March 1993]
REHFELDT-GENETIC
ELEVATION
)
341
VARIATION IN PONDEROSAE
G
C
ELEVAON
2900 -
2900 -
2500-
2500-
2100-2100-
114
38.5
LONGfWDEic8
ZU
114
38.o
108
LATMDE
LATITUDE
LA
105 .0
105
ELEVATION
(M)
E
D
I~~~~~~~~~~~~~~~~~~~~~~~ATITU
H
ELEVATON
O)
1700
29000
1200
2900
114
38.5
LONGITUDEioe
114
38.
LOITUDE
LATITUDE
108
105t.0
LATfflJDE
105
ELEVATION
(M)
ELEVATION
(M)
2900
2900-
2500 -200
2100
2100
-4 P--
1700
LONGITUDE 108
.
~~~~~~~~~~~~~~~~~~1700
LATiTUDE
t
Fig. 7. Using models of geneticvariationto locate populations (balloons) expectedto exhibitresponsesin common gardensthatare similarto
those of a targetedpopulation (pyramid).Letterskey the generalgeographiclocality(Fig. 1) of the targetpopulation.
342
[Vol. 80
AMERICAN JOURNAL OF BOTANY
representeitherplantingsites or seed productionareas.
The model can thenbe used to 1) locate sources of seeds
that should be geneticallycompatible with the environment at the target,or 2) select plantingsites forwhich
seeds gatheredfromthe targetedpopulation should be
adapted. The model mightalso be used to locate disjunct
populations so unique geneticallythatgene conservation
programsmay be desirableas, forexample,in thePinaleno Mountains (localityI). Additional uses mightinclude
assessingtheimpactofclimatechangeon theadaptedness
of populations,understandingphenotypicvariation,and
delimitingseed orchardsand breedingzones.
The usefulnessof a model, however, depends on its
and a firststepin acquiringcredibilityinvolves
credibility,
verification.Unfortunately,independentdata currently
the presentmodel. Thereare not available forverifying
fore,the model should be used withdiscretion.In addition, genetic variation mightbe occurringalong clines
independentof those alreadydetected.This would mean
thattherecurrenceof similarpopulationsacross thelandscape is more restrictedthan the resultsimply. For this
reason, functionalmodels require periodic updating as
additional mathematical descriptorsbecome available.
Finally, credibilityrequires that the appropriatespecies
are ecologicallysuited to the elevations and localities for
which predictionsare made. This means thatdata bases
must be firmlycoordinatedwith the ecological distribution of the species; untilphysiographicpredictorsare replaced by environmentalvariables,models should not be
used forextrapolation.
Nevertheless,the resultsattestto pronouncedlevels of
pongeneticvariationamongpopulationsofsouthwestern
derosa pine. It seems reasonable to conclude that much
of the variation has been molded by heterogeneousenvironments.Microevolutionaryprocesses undoubtedly
have been furtheredby ponderosa pine's discontinuous
distributionin the Southwest. Such distributionslimit
gene flowand therebypromote selectivedifferentiation.
Biosystematic implications- Conkle and Critchfield
(1988) separate the southwesternrace of P. p. var. scopulorumfromtheRockyMountain race in southernUtah
and southwesternColorado. Yet the resultsof this study,
like those fromthe Colorado Plateau (Rehfeldt,1990),
demonstratecontinuousgeneticvariationalonggeographic and elevationalgradients.To be sure,populationsfrom
southernArizona and New Mexico differtremendously
fromthosein Utah and Colorado. While thesedifferences
to justifythe racial classifications,the
may be sufficient
transitionbetweenraces is unquestionablybroad.
The resultsalso supportPeloquin's (1971) contention
that introgressionamong species of Ponderosae is common in southeasternArizona. While Peloquin reached
his conclusions fromphenotypicobservationsin natural
from
populations,thepresentresultssuggestintrogression
the performanceof progeniesfromBarfoot.One might,
moreover,interpretseveral of the patternsof geographic
variation in ponderosa pine according to introgressive
hybridization.As illustratedbythedurationofshootelongation in the greenhouse(GHDR) and winterinjuriesat
Window Rock (WRWI) in Fig. 4, the slope ofgeographic
cline for nine variables became' the steepest in southeasternArizona. Of thesenine,thepatternsforthreevari-
ables could have been interpretedas introgressionwith
Apache pine,whilethose fortheothersix variables could
have been interpretedin termsof introgressionwithArizona pine. Althoughlogical, such interpretationsmust
be tempered by the fact that only two ponderosa pine
populations were sampled from the isolated ranges in
southernArizona, and the southernmostof these was
itis likely
Barfoot.Since Barfootseems to be introgressed,
thatthe changein the slope of geographicclinesin southof the regressionto the
ernArizona was due to the fitting
performanceof Barfoot progeniesratherthan to widespread introgression.
Nevertheless,theseresultsalong withthoseofPeloquin
primarilyinvolvingponderosaand
supportintrogression,
Arizona pines but also implicatingApache pine. Yet the
degreeofhybridization,theamountand directionofgene
flow,and the ecological geneticsof species of the Ponderosaein theSouthwestdeservea thoroughexamination.
LITERATURE CITED
BAKER,F. S. 1944. Mountains climatesof the westernUnited States.
EcologicalMonograph14: 223-254.
CALLAHAM,
R. Z., AND A. R. LIDDICOET. 1961. Altitudinalvariation
at 20 yearsin ponderosa and Jeffrey
pines. Journal
ofForestry
59:
814-820.
CLAUSEN,
J.,AND W. M. HIESEY. 1960. The balance betweencoherence
and variation in evolution. Proceedings
oftheNationalAcademy
of Sciences, USA 46: 494-506.
, D. D. KECK, AND W. M. HIESEY. 1940. Experimentalstudies
on the natureof species. I. The effectsof varied environmentson
westernAmerican plants. Carnegie Instituteof Washington,Publication 520. Stanford,CA.
CONKLE,M. T. 1973. Growthdata from29 yearsfromthe California
elevational studyof ponderosa pine. ForestScience19: 31-39.
, AND W. B. CRITCHFIELD.
1988. Genetic variationand hybridization of ponderosa pine. In D. M. Baumgartnerand J. E. Lotan
[eds.], Ponderosa pine: the species and its management,27-43.
WashingtonState University,Pullman, WA.
DAUBENMIRE,
R., AND J.B. DAUBENMIRE.1968. Forest vegetationof
easternWashingtonand northernIdaho. WashingtonAgricultural
ExperimentStation Technical Bulletin60. WashingtonState University,Pullman, WA.
DIETRICHSON,J. 1964. The selection problem and growthrhythm.
SilvaeGenetica13: 178-184.
DRAPER, N. R., AND H. SMITH. 1981. Applied regressionanalysis.
Wiley,New York, NY.
HAMRICK, J. L., H. M. BLANTON, AND K. J. HAMRICK. 1989. Genetic
ofgeographicallymarginalpopulationsofponderosapine.
structure
American
JournalofBotany76: 1559-1568.
HANOVER, J. R. 1963. Geographicvariationin ponderosa pine leader
growth.ForestScience9: 86-95.
HoFF, R. J. 1988a. Resistance of ponderosa pine to the goutypitch
midge (Cecodpmyia
Research Paper INT-387. U.S.
piniinopsis).
DepartmentofAgriculture,
ForestService,IntermountainResearch
Station. Ogden, UT.
1988b. Susceptibilityof ponderosa pine to the needle cast
ResearchPaper INT-386. U.S.
fungusLophodermium
baculiferum.
DepartmentofAgriculture,
ForestService,IntermountainResearch
Station. Ogden, UT.
1990. Susceptibilityof ponderosa pine to westerngall rust
withinthe middle Columbia River System.Research Paper INT416. U.S. DepartmentofAgriculture,
ForestService,Intermountain
Research Station. Ogden, UT.
1991. Resistance to westerngall rustin artificially
inoculated
ponderosa pine. CanadianJournalofForestResearch21: 13161320.
JOHNSON, R. A., AND D. W. WICHERN. 1982. Applied multivariate
statistics.Prentice-Hall,Englewood Cliffs,NJ.
March 1993]
REHFELDT-GENETIC
VARIATION IN PONDEROSAE
Y. B. 1989. Ecologicalandevolutionarystudiesofponderosa
LINHART,
pine in the Rocky Mountains. In D. M. Baumgartnerand J. E.
Lotan [eds.], Ponderosa pine: the species and its management,7789. WashingtonState University,Pullman, WA.
ANDM. L. DAvis. 1981. Genetic
,J.B. MITON, K. B. STURGEON,
variation in space and time in a population of ponderosa pine.
46: 407-426.
Heredity
LITTLE, E. J. 1971. Atlas of United States trees,vol. 1, Conifersand
importanthardwoods.MiscellaneousPublication,U.S. Department
of AgricultureNo. 1146. Washington,DC.
1969. SubdivisionsofthegenusPinus
,IAND W. B. CRITCHFIELD.
(pines).Miscellaneous Publication,U.S. DepartmentofAgriculture,
Forest Service,No. 1144. Washington,DC.
1977. Ecological geneticsof ponMADSEN, J. L., AND G. M. BLAKE.
26:
derosa pine in the northernRocky Mountains. SilvaeGenetica
1-8.
MAYR, E. 1970. Populations, species and evolution. Harvard UniversityPress, Cambridge,MA.
MITTON,J. B., Y. B. LINHART,J. L. HAMRICK,AND J. BECKMAN.1977.
and matingsystemsin ponderosa
Observationson geneticstructure
Genetics
andApplied
pine oftheColorado FrontRange. Theoretical
51: 5-13.
1980. Genetic differen, K. B. STURGEON, AND M. L. DAVIS.
tiationin ponderosa pine along a steep elevational transect.Silvae
Genetica29: 100-103.
G., AND M. T. CONKLE. 1976. Trend analysis in genetic
NAMKOONG,
controlof heightgrowthin ponderosa pine. ForestScience22: 212.
PELOQUIN,R. L. 1971. Variation and hybridizationpatternsin Pinus
Ph.D. dissertation,University
ponderosaand Pinusengelmannii.
of California.Santa Barbara, CA.
1984. The identificationof three-specieshybridsin the ponderosa pine complex. Southwestern
Naturalist
29: 115-122.
PERRY, J. P., JR. 1991. The pines of Central America and Mexico.
Timber Press, Portland,OR.
READ, R. A. 1980. Geneticvariationin seedlingprogenyof ponderosa
23: 1-59.
pine provenances.ForestScienceMonograph
1983. Ten-yearperformanceof ponderosa pine provenances
in the Great Plains of North America. Research Paper RM-250.
U.S. Departmentof Agriculture,Forest Service, Rocky Mountain
Forest and Range ExperimentStation. Fort Collins, CO.
1981. Hail damage variationby seed
, AND J. A. SPRACKLING.
source in a ponderosa pine plantation. Research Note RM-4 10.
U.S. Departmentof Agriculture,Forest Service, Rocky Mountain
Forest and Range ExperimentStation. Fort Collins, CO.
REHFELDT, G. E. 1980. Genetic gains fromtreeimprovementof ponderosa pine in southernIdaho. Research Paper INT-263. U.S. DeForest Service,IntermountainForestand
partmentof Agriculture,
Range ExperimentStation. Ogden, UT.
of Larix occidentalis
populations from
1982. Differentiation
the northernRocky Mountains. SilvaeGenetica31: 13-19.
343
. 1986a. Adaptive variation in Pinus ponderosa from Intermountainregions.I. Snake and Salmon Riverbasins. ForestScience
32: 79-92.
. 1986b. Adaptive variation in Pinus ponderosa from Intermountain regions. II. Middle Columbia River System. Research
Paper INT-373. U.S. Departmentof Agriculture,Forest Service,
IntermountainResearch Station. Ogden, UT.
1988. Ecological geneticsof Pinus contortafromthe Rocky
Mountains (USA): a synthesis.Silvae Genetica 37: 131-135.
* 1989. Ecologicaladaptationsin Douglas-fir(Pseudotsugamen-
ziesiivar.glauca): a synthesis.
ForestEcologyand Management
28: 203-215.
1990. Geneticdifferentiation
amongpopulationsofPinusponderosa fromthe upper Colorado River Basin. Botanical Gazette
151: 125-137.
1991. Models of geneticvariationforPinus ponderosa in the
Inland Northwest(U.S.A.). Canadian Journalof Forest Research
21: 1491-1500.
* 1992. Early selection in Pinus ponderosa: compromisesbetweengrowthpotentialand growthrhythmin developingbreeding
strategies.Forest Science 38: 661-677.
, R. J. HoFF, AND R. J. STEINHOFF. 1984. Geographicpatterns
ofgeneticvariationin Pinus monticola.Botanical Gazette 145: 229239.
, AND W. R. WYKoFF. 1981. Periodicityof shoot elongation
among populations of Pinus contortafrom the northernRocky
Mountains. Annals ofBotany 48: 371-377.
SACHER, J.A. 1954. Structureand seasonal activityofthe shootapices
of Pinuslambertiana
and Pinusponderosa.AmericanJournalof
Botany42: 82-91.
SAS INSTITUTE. 1985. SAS user's guide: statistics,version 5, ed., vol.
2. SAS Institute,Inc., Cary,NC.
SHEPPARD, W. D., ANDS. E. McELDERRY. 1986. Ten-yearresultsof a
ponderosa pine progenytestin the Black Hills. WesternJournalof
AppliedForestry
1: 79-83.
A. E., ANDR. R. SILEN. 1962. Racial variationin ponderosa
SQUILLACE,
pine.ForestScienceMonograph
2: 1-26.
STEEL,R. G. D., AND J. H. TORRIE. 1960. Principlesand procedures
of statistics.McGraw-Hill, New York, NY.
R. J.,D. G. JOYCE,AND L. FINs. 1983. Isozyme variation
STEINHOFF,
inPinusmonticola.
CanadianJournal
113:1122ofForestResearch
1131.
1986. Genetic variationin ponderosa pine: a
15-yeartest of provenances in the Great Plains. Research Paper
RM-265. U.S. DepartmentofAgriculture,
ForestService.FortCollins, CO.
WELLS, 0. 0. 1964. Geographic variation in ponderosa pine. I. The
ecotypesand theirdistribution.Silvae Genetica 13: 89-103.
VAN HAVERBEKE, D. F.