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DISTRIBUTION AND ECOPHYSIOLOGY OF THE
PONDEROSAE IN THE SANTA CATALINAMOUNTAINS
OF SOUTHERN ARIZONA

presented by
JASON SCOTT KILGORE

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DISTRIBUTION AND ECOPHYSIOLOGY OF THE PONDEROSAE
IN THE SANTA CATALINA MOUNTAINS OF SOUTHERN ARIZONA

By

Jason Scott Kilgore

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Plant Biology
Ecology. Evolution, and Behavioral Biology Program

2007

ABSTRACT

DISTRIBUTION AND ECOPHYSIOLOGY OF THE PONDEROSAE
IN THE SANTA CATALINA MOUNTAINS OF SOUTHERN ARIZONA

By

Jason Scott Kilgore

I investigated the distribution and ecophysiological factors for
establishment and survival for Ponderosae in the Santa Catalina Mountains of
southern Arizona. The complex topography of this system produces variable
microhabitats that obscure a simple elevational pattern of Arizona pine (Pinus
arizonica Engelmann) lower than the Southwestern race of Rocky Mountain
ponderosa pine (P. ponderosa var. scopulorum Engelmann). In addition, an
intermediate needle morphotype, a putative hybrid between P. arizonica and P.
ponderosa var. scopulorum, is recognized from this and other mountain islands
in the American Southwest. Following extensive fires, Coronado National Forest
identified the need to assess their distributional patterns for reforestation.

Modeling of documented occurrences in relation to climatic variables
indicated clear distinction in distribution and ecological niche space between P.
arizonica and P. ponderosa var. scopulorum, while the putative hybrid displayed
intermediate distribution and occupation of niche space. High intratree variance
in needle number and the overlapping distribution and ecological niche space
support the hybridization hypothesis.

In controlled tests, germination success and time to initial germination

were similar, but P. arizonica seeds germinated over a longer period of time. In

the field, neither species germinated in the spring, while, following the monsoon,
P. an‘zonica (25%) had lower germination at low and high elevation than did P.
ponderosa var. scopulorum (52%). Controlled whole-plant freezing tests
revealed greater cold tolerance in needle, bud, and cambial tissue by P.
ponderosa var. scopulorum seedlings than P. arizonica seedlings over periods of
simulated fall, winter, and spring. Seedlings of P. ponderosa var. scopulorum
exposed to ambient light and freezing temperatures maintained higher
photosynthetic quantum yield than did seedlings of P. an‘zonica and the putative
hybrid. In the field, small P. ponderosa var. scopulorum trees maintained higher
net photosynthesis and midday quantum yield than did similarly sized P.
arizonica trees. When soil water availability was low and incident light levels
were high during the winter, small P. ponderosa var. scopulorum trees
maintained higher leaf water status yet similar net photosynthesis than P.
an‘zonica trees. During the arid foresummer, diurnal variation in gas exchange
was higher in P. ponderosa var. scopulorum than in P. arizonica, suggesting
greater stomatal response to water-limiting conditions. However, integrated
water-use efficiency and leaf nitrogen content did not differ between species.
During severe dehydration, P. ponderosa var. scopulorum maintained higher
photosynthetic quantum yield than did P. arizonica. However, xylem conductivity
and vulnerability to cavitation did not differ between species. P. ponderosa var.
scopulorum has greater cold tolerance, controls transpirational water loss, and

maintains photosynthetic function across the seasons better than P. arizonica.

Copyright by
JASON SCOTT KILGORE
2007

ACKNOWLEDGEMENTS

The research described in this dissertation could not have been
accomplished without the support of my family and many friends. I especially
appreciate the enduring support and love from my wife, Pam, and son, Oliver.
My extended family, including Lucy Dueck, Justin Kilgore, Bob and Kathy
Reynolds, Kim Reynolds, and Jason Tallant, provided unquestioning support,
trust, and curiosity in what I was doing in Arizona or at the lab through the night.

I thank my committee - Drs. Frank Telewski, Peter Murphy, Bryan
Epperson, and Brian Maurer - for serving my research interests. Frank was not
only my major advisor and mentor but also has become a good friend; I truly
appreciate his companionship and contributions to field and labwork. Pete has
been my prudent mentor and friend for over a decade. And I eagerly anticipate
exploring the genetic complexities of the Ponderosae with Bryan in the future.

I have been enormously fortunate to have many people volunteer their
time and resources to me through this dissertation research. Friends who
trudged the Santa Catalina Mountains with or for me have included Frank
Telewski, Kris Kern, Stephanie Blumer, Gretchen Friedlander, Samantha
Warwick, Clare and Bob Peterson, Jill Grimes, Tom and Annita Harlan, Holly
Bechetti, and Joe and Christina Hoscheidt. Folks from the Coronado National
Forest, especially Bill Hart but also Terri Austin, Jennifer Ruyle, and Josh Taiz,
have been incredibly supportive and interested in this research; these are the

managers of the forest who need this research. Barb Eisele (Trees for Mount

Lemmon) was instrumental in connecting this research with reforestation
interests for the Summerhaven community as well as in contributing seedlings to
our research program; she was always interested in trading home-cooked meals
for research updates. Jim Burns and Steve Leavitt at the Laboratory of Tree-
Ring Research provided shipping destinations and nitrogen cylinders, while Carol
Mack at Mount Lemmon General Store kindly arranged shipping departures.
Bob Peterson and the gang at the Steward Observatory always made sure they
had room for us at the top. Loma and Jim Griffith always had a place for us in
Tucson and provided transportation, including a 4WD vehicle, whenever needed.
Back in Michigan, I had ample assistance and support from friends. Folks
who contributed to lab and greenhouse adventures include Alex Lindsey,
Stephanie Blumer, Frank Telewski, Samantha Warwick, Jessica Reif, Tricia
Mitchell, Casey Bartrem, Calvin Glaspie, Jameel AI-Haddad, Justin Savu, Pam
Kilgore, Crystal Wallace, Will Paddock, Abbie Schrotenboer, Lynn Sage, Todd
Robinson, Andrea Corpolongo, Scott Smith, Hope Rankin, Christopher Start, and
Andrew Murphy. Anna Jacobsen is a valued friend, colleague, and contributor to
this research. Although not serving on my committee, Drs. Lissa Leege, Ken
Poff, Bert Cregg, Frank Ewers, Jim Flore, David Rothstein, Carolyn Malmstrom,
Merritt Turetsky, Andrew McAdam, and Andy Jarosz provided invaluable advice,
equipment, and/or office space. Jeff Wilson, Bob Goodwin, and Nate Siegert
assisted me with computer and GIS issues. Lynn Sage, Mark Hammond, and
Jim Klug arranged for cold conditions, while Dave Freville’s crew ensured warm

(greenhouse) conditions. Mike Grillo and Heather Hallen were invaluable friends

vi

and colleagues. No person acts without their departmental staff; I appreciate the
support and assistance by Tracey Barner, Kasey Baldwin, Jan McGowan, Jill
Richey, and Holly Nieusma.

Last, | wish to acknowledge those units that have financially contributed to
this research. The Department of Plant Biology supported a considerable
amount of travel, conference, and non-MSU course-related expenses through the
Paul Taylor Fund, as well as supporting me through the experience of teaching
assistantships. The College of Natural Science granted me the Dr. Marvin
Hensley Endowed Fellowship and the Dissertation Completion Fellowship, while
the Ecology, Evolution, and Behavioral Biology (EEBB) program granted me
Travel and Summer Fellowships. Campus Planning and Administration has
provided logistical support, while Frank has supplemented funding with his
research account. I appreciate all that these units have contributed to my

research.

vii

TABLE OF CONTENTS

CHAPTER1
AN INTRODUCTION TO SOUTHWESTERN PONDEROSA PINE,

ARIZONA PINE, AND THEIR ENVIRONMENT

CHAPTER 2
DISTRIBUTION AND ECOLOGICAL NICHE DIFFERENTIATION OF
SYMPATRIC PONDEROSAE TAXA IN THE SANTA CATALINA

MOUNTAINS OF ARIZONA

CHAPTER 3
INFLUENCE OF SEED GERMINATION ECOLOGY ON THE
DISTRIBUTION OF PONDEROSAE IN THE SANTA CATALINA

MOUNTAINS OF ARIZONA

CHAPTER 4
DIFFERENTIAL COLD TOLERANCE SEGREGATES DISTRIBUTION
OF PONDEROSAE IN THE SANTA CATALINA MOUNTAINS OF

CHAPTER 5
THE ROLE OF DROUGHT TOLERANCE IN STRUCTURING
PONDEROSAE DISTRIBUTION IN THE SANTA CATALINA

MOUNTAINS OF ARIZONA

CHAPTER 6
GENERAL CONCLUSIONS

viii

23

82

116

160

...220

...226

244

LIST OF TABLES

Table 2-1. Models and environmental variables used in the modeling
of species distributions, or suitable habitat (Phillips et al. 2006a), using
Maxent (Phillips et al. 2006b)

Table 2-2. Summary statistics for regression of mean number of needles
per fascicle per tree by morphotype and combined species against

elevation at which the tree was located

Table 2-3. Comparison of influence of spatial extent by shape of
calibration area on model output

Table 2-4. Performance characteristics and statistics for prediction

models generated by Maxent for the rectangular study region... ..

Table 2-5. Climatically suitable area estimates for the three morphotypes

based on cumulative probabilities from the Lit and Lit.Thresh models...

Table 2-6. Thresholds for the three morphotypes based on the Lit. Thresh

model...

Table 2-7. Ecological similarity matrix based on the ability of one taxon’s
model (columns) to predict the documented occurrences of each of the

taxa (rows)

Table 3-1. Seeds inventoried from cones collected from the Santa

Catalina Mountains in September-October 2005

Table 3-2. Type II tests of 2-factor ANOVA of percent germination
(pergerm), time to first germination (initiate), and time to last realized
germination (complete) for PIN_PON seed as a function of site (MTL

and PAL), stratification (strat; 0-, 15-, and 30—day), and their interaction ......

Table 3-3. Type II tests of 2-factor ANOVA of percent germination
(pergerm), time to first germination (initiate), and time to last realized
germination (complete) for P|N_ARI seed as a function of site (PAL and

RC/LIZ), stratification (strat; 0-, 15-, and 30-day), and their interaction... ..

Table 3-4. Type II tests of 2-factor ANOVA of percent germination
(pergerm), time to first germination (initiate), and time to last realized
germination (complete) for the PAL site as a function of morphotype

(PIN_ PON, Mixed, and PIN _ARI)W stratification ”(strat;. 0-, 15-, and

30-day), and their interaction.

...43

.51

...55

.58

.62

...64

.64

.88

..... 99

100

...102

Table 3-5. Type II tests of 2-factor ANOVA of natural-log-transformed

percent realized germination (pergerm), time to first germination

(initiate), and time to last germination (complete) for pooled sites as a

function of morphotype (PIN_ PON, Mixed, and PIN :AHRI), stratification
(strat;0-,15- and 30—day), and their interaction” ..............103

Table 3-6. Pairwise comparisons (Tukey’s HSD) of PIN_PON and

PIN_ARI seeds for natural-log-transformed percent realized germination
(pergerm), time to first germination (initiate), and time to last germination
(complete) as a function of stratification treatment for pooled sites... .103

Table 3-7. ANOVA for germination success of PIN _PON and
PIN _ARI seeds at the Mount Lemmon and Willow Canyon sites
(adjusted R2=0.1625)... 104

Table 3-8. Multiple comparisons between site (Mount Lemmon -
MTL and Willow "Canyon- WC), morphotype and interactions "by
TukeysHSD... .. .. ................105

Table 4-1. Seedlings and source trees from the Santa Catalina Mountains
used in the controlled whole-seedling experiment........................... .....125

Table 4-2. Conditions for hardening, acclimating, and deacclimating
PIN___PON and PIN_ARI seedlings for the whole-seedling freezing test... ....126

Table 4-3. Temperature profiles for each of the freezing tests outlined
in Table 4-2129

Table 4-4. ANOVA results for dark-acclimated chlorophyll fluorescence

(DACF), relative conductivity (RC), and cambial (Camb) and bud (Bud)

mortality for PIN _PON and PIN _ARI seedlings exposed to freezing

treatments (Tx) during the acclimation phase... .. .. .137

Table 4-5. Cold tolerance (LTso) 0f PIN_PON and PIN__ARI estimated

as a function of needle dark-acclimated chlorophyll fluorescence (ndl -

DACF), needle relative conductivity (ndl - RC), and cambium and bud

mortality across three stages of cold acclimation: Hardening, Winter,

and Deacclimation..............................................................................144

Table 4-6. Type II tests of ANCOVA for PSII quantum yield (LACF)
fitted to morphotype and the covariate air temperature (Temp) for 3-
year-old potted seedlings with frozen soil water........................................146

Table 4-7. ANCOVA results for predawn dark-acclimated chlorophyll
fluorescence (pDACF), midday light-acclimated chlorophyll fluorescence
(mLACF), predawn (ppsi) and midday (mpsi) xylem pressure potential,
and midday net photosynthesis (mphoto) for PIN _PON and PIN_ARI
trees at three sites (MTL, PAL, and LIZ) In the Santa Catalina
MountaIns In January 2006.. . .. . . ...

Table 4-8. Type II ANOVA results for predawn dark-acclimated
chlorophyll fluorescence (pDACF), midday light-acclimated chlorophyll
fluorescence (mLACF), predawn (ppsi) and midday (mpsi) xylem
pressure potential, and midday net photosynthesis (mphoto) for

PIN_ PON and PIN _ARI trees at three sites (MTL“. PAL, and Lil) In

the Santa Catalina Mountains In January 2006...

Table 4-9. Mean responses by PIN_PON and PIN_ARI trees by site
(MTL, PAL, and LIZ) and across sites (Combined) following removal

of three outlying data values

Table 4-10. Cold tolerance (LT50) of combined PIN_PON and PIN_ARI
tissues from this study and from Burr et al. (1990) across three stages

of cold acclimation: Hardening, Winter, and Deacclimation......

Table 5-1. Characteristics of 'small’ trees used for gas exchange and
xylem pressure potential measurements in 2005-06 at the Mount

Lemmon (MTL), Palisade Rock (PAL), and Lizard Rock (LIZ) sites...

Table 5-2. Comparison of seasonal xylem water potential (Wp) of
combined (both), PIN_PON, and PIN_ARI fascicles harvested at

predawn, midday, and evening across an elevational gradient...

Table 5-3. Nested ANOVA results xylem pressure potential (W,,) in
needles from PIN_PON and PIN_ARI trees across an elevation gradient,

3 seasons (“‘Arid" is arid foresummer), and 2-3 daytime periods...

Table 5-4 Univariate ANOVA results for carbon isotope (6130), nitrogen
isotope (615N), carbon (%C), nitrogen (%N), and carbon-to- n-itrogen

(C: N) content in needles for each year from PIN _PON and PIN _ARI

trees at PAL.
Table 5- 5. Nested ANOVA results for carbon isotope (613C), nitrogen

isotope (615N), carbon (%C), nitrogen (%N), and carbon-to- -nitrogen
(C: N) content' In needles from PIN_PON and PIN_ARI trees at PAL.

xi

...148

...150

152

153

166

190

.194

...195

...198

Table 5-6. ANCOVA results for fresh mass loss (pmass) and PSII

excitation capture efficiency (Fv’lFm’) of dehydrating needles for

PIN_PON and PIN_ARI stems from four sites in the Santa Catalina

Mountains: MTL, PAL, RCL, and LI2203

Table 5-7. ANOVA results for xylem pressure potential at 50% loss

of hydraulic conductivity (”’50) and xylem specific hydraulic

conductivity (Ks) for PIN_PON and PIN_ARI stems collected from

three sites in the Santa Catalina Mountains: MTL, PAL, and LlZ/RCL... ..207

Table A-1. Geographic coordinates and mean needle number for trees
from the distribution study ("SCAT")... ..............226

Table A-2. Correlation matrix for mean (1971-2000; PRISM 2006)

annual and monthly precipitation (Precip, top), minimum temperature

(MinTemp, bottom), and maximum temperature (MaxTemp, next page)

for the Santa Catalina Mountain study region...........................................241

xii

LIST OF FIGURES

Figure 1- 1. Vegetation of the Santa Catalina Mountains as drawn

by Whittaker and Niering .....(1965) from 400 vegetatIon samples In

a gradient analysis...

Figure 2—1. General distribution of ponderosa pine (top, Pinus
ponderosa, Thompson et al. 1999), and location of study region in
the Santa Catalina Mountains in southeastern Arizona (bottom,

Figure 2-2. Distribution of sampling points collected during the

distribution study (“SCAT”) and other projects ("Non- SCAT").” in the

study region of the Santa Catalina Mountains...

Figure 2-3. Frequency of trees (n=671) by mean needle number per

fascicle

Figure 2-4. Variability in needle number within a tree as a function of its

mean needle number

Figure 2- 5. Mean annual needle number (solid, :80) and sample size

(hollow) for the PIN _PON ”(A)”. Mixed ”(O)” and PIN _ARI ...(D). morphotypes

(Peloquin 1984)...

Figure 2-6. Distribution of occurrences for the three morphotypes in the

region using the distribution study (“SCAT”) data

Figure 2-7. Mean number of needles per fascicle as a function of

elevation foreach morphotype

Figure 2-8. Distribution of the three morphotypes in climatic space with

regard to mean monthly minimum and maximum January temperatures...

Figure 2-9. Distribution of the three morphotypes in climatic space with
regard to mean monthly maximum June temperature and monthly July

preCIpItatIon

Figure 2-10. Distribution of cumulative probability of suitable habitat
for PIN_PON relative to maximum June temperature and annual
precipitation in two spatial extents (“polygon” and “rectangle") for

Maxent model caIIbratIon

xiii

.....18

32

...35

47

.48

..48

.50

.51

.52

.54

...56

Figure 2-11. Distribution of cumulative probability of suitable habitat for

the three morphotypes in the region for the Lit model by Maxent...

Figure 2- 12. Distribution of cumulative probability of suitable habitat for

the three morphotypes In the region for the Lit. Threshold model by

Maxent...

Figure 2-13. Distribution of cumulative probability of suitable habitat for

the three morphotypes in the region for the NED.Asp model by Maxent...

Figure 3-1. Prepared seedbed within the predator-exclusion cage at the

Willow Canyon SIte

Figure 3-2. Predator-exclusion cage after placement over sown seeds at

the Willow Canyon site (January 2006)

Figure 3-3. Mean seed germination (top), time to first germination
(middle), and elapsed time to the last seed germination (bottom) for the
full data set . . .

Figure 3-4. Germination as a function of stratification treatment for seeds

from PIN_PON trees at MTL and PAL

Figure 3-5. Germination as a function of stratification treatment for seeds

from PIN_ARI trees at PAL and RC/LIZ

Figure 3-6. Germination as a function of stratification treatment for

combined morphotypes at the PAL Slte

Figure 3-7. Germination of PIN_PON and PIN_ARI seeds planted at

high (Mount Lemmon) and low (Willow Canyon) elevation...

Figure 3-8. A proposed path of selection for conditional (secondary)
dormancy of seeds produced by Arizona and Southwestern ponderosa
pine in the mountain islands of the Southwest experiencing bimodal
precipitation patterns . .. ..

Figure 4-1. Dark-acclimated chlorophyll fluorescence (left) and relative
conductivity (right) for PIN_PON and PIN_ARI after different freezing
treatments across three stages of cold acclimation: Hardening (top),

Acclimation (middle), and Deacclimation (bottom)....................................

Figure 4-2. Cambial and bud mortality for PIN_PON and PIN_ARI after
different freezing treatments across three stages of cold acclimation:

Hardening (top). Winter (middle), and Deacclimation (bottom). ..

xiv

.61

...63

...70

.94

.94

...97

.98

100

101

104

...113

.140

141

Figure 4-3. Linear regression of dark-acclimated chlorophyll
fluorescence, relative conductivity, and cambial and bud mortality
against treatment temperature for PIN_PON (solid line) and PIN_ARI
(dashed line) across three stages of cold acclimation: Hardening

(thin line), Winter (normal line), and Deacclimation (thick line). .. ... ... ... ... ... ....

Figure 4-4. Slope (thin line) and intercept (thick line) for regressions
of dark-acclimated chlorophyll fluorescence, relative conductivity, and
cambial and bud mortality against treatment temperature for PIN_PON

(solid line) and PIN_ARI (dashed line) across weeks of cold acclimation... ..

Figure 4-5. PSII quantum yield (LACF) as a function of air temperature

when measured from 3-year—old potted seedlings with frozen soil water:
PIN_PON (solid line), Mixed (dotted line), and PIN_ARI (dashed line). ..

Figure 4-7. Variable chlorophyll fluorescence (Fv/Fm, solid; Fv’lFm’,
empty) as a function of leaf temperature for PIN_PON and PIN_ARI

trees combined across sites in January 2006

Figure 4- 8. Xylem pressure potential (919) from excised needles of

PIN _PON and PIN _ARI trees at three sites (MTL” PAL, and LIZ) In

the Santa Catalina Mountains In January 2006...

Figure 4- 9. Midday net photosynthesis (A) results for PIN _PON and

PIN _ARI trees at three sites (MTL, PAL, and HLIZ)’ In the Santa Catalina

Mountains In January 2006...

Figure 5-1. Diurnal variation in net photosynthetic rate (A) and
transpiration (Tr) as a function of incident radiation (PAR) and

temperature (7.03;) for paired PIN_ PON and PIN _ARI trees at PAL

during winter (January 2006)...

Figure 5-2. Diurnal variation in net photosynthetic rate (A) and
transpiration (Tr) as a function of incident radiation (PAR) and
temperature (T leaf) for paired PIN_PON and PIN_ARI trees at PAL

during the arid foresummer (June 2005)

Figure 5-3. Diurnal variation in net photosynthetic rate (A) and
transpiration (Tr) as a function of incident radiation (PAR) and
temperature (That) for paired PIN_PON and PIN_ARI trees at PAL

during the monsoon season (August 2005)

Figure 5-4. Diurnal by seasonal needle xylem pressure potential (WP)

for PIN_ PON and PIN _ARI trees growing in the Santa Catalina

Mountains...

XV

142

144

146

.147

...151

...151

...181

183

.186

...190

Figure 5-5. Variation (standard deviation, SD) in diurnal needle xylem
pressure potential (Wp) by season for PIN_PON and PIN_ARI trees

growing in the Santa Catalina MountaIns

Figure 5-6. Needle xylem pressure potential (91p) by winter (top,left),
arid foresummer (bottom), and monsoon (top,n'ght) for PIN_PON and
PIN_ARI trees growing along an elevational gradient in the Santa

Catalina MountaIns

Figure 5- 7. Annual variation in mean carbon (513C, solid lines) and
nitrogen (615M, dashed lines) isotope composition for leaf tissue of
PIN_PON and PIN_ARI trees at PAL...

Figure 5-8. Annual variation in mean carbon (solid lines) and nitrogen

(dashed lines) content for leaf tissue of PIN_PON and PIN_ARI trees

atPAL

Figure 5- 9. Carbon isotopic composition (613C) relative to carbon
content in needles from PIN_ PON ”(solid line)“ and PIN _ARI m(dashed
Iine)trees atPAL...

Figure 5- 10. Carbon isotopic composition (613C) relative to nitrogen
content In needles from PIN_ PON (solid line)”. and PIN _ARI “(dashed
Iine)trees atPAL...

Figure 5-11. Nitrogen isotopic composition (6‘5N) relative to nitrogen
content in needles from PIN_ PON (solid line)”. and PIN _ARI ”(dashed
Iine)trees atPAl.... .

Figure 5-12. Nitrogen isotopic composition (6‘5N) relative to carbon-
to-nitrogen content (ON) in needles from PIN_PON (solid line) and

PIN_ARI (dashed line) trees at PAL

Figure 5-13. Dehydration of fascicles for PIN_PON (solid lines) and
PIN_ARI (dashed lines) stems from four sites in the Santa Catalina

Mountains: MTL, PAL, RCL, and LIZ

Figure 5-14. Mean dehydration of fascicles for PIN_PON (solid lines)
and PIN_ARI (dashed lines) stems across four sites in the Santa

Catalina MountaIns

Figure 5-15. Photosynthetic efficiency as a function of needle
dehydration for PIN_PON (solid lines) and PIN_ARI (dashed lines)
stems from four sites in the Santa Catalina Mountains: MTL, PAL,
RCL, and LIZ.......

.192

193

...195

...197

...199

...199

...200

.200

.202

.203

.. .205

Figure 5- 16. Photosynthetic efficiency as a function of needle

dehydration for PIN _PON (solid lines) and PIN _ARI (dashed lines)

stems across four sites in the Santa Catalina Mountains...

Figure 5-17. Xylem vulnerability curves for PIN_PON (solid lines) and
PIN_ARI (dashed lines) stems collected from three sites in the Santa

Catalina Mountains: MTL, PAL, and LlZ/RCL

Figure 5-18. Xylem specific hydraulic conductivity (K...) for PIN_PON
and PIN_ARI stems collected from three sites in the Santa Catalina

Mountains: MTL, PAL, and LIZ/RCL
Figure 5- 19. Stomatal openings in PIN _PON “(left)”. and PIN _ARI "”(right)

bySEM

Figure 5-20. Chloroforrn-cleared stomatal openings in PIN _ARI of top

and transverse views by SEM...

...205

207

208

...211

...212

CHAPTER 1

AN INTRODUCTION TO SOUTHWESTERN PONDEROSA PINE,
ARIZONA PINE, AND THEIR ENVIRONMENT

From the time of Theophrastus (1916) some 2300 years ago, humans
have been fascinated with the distribution and variation of species. Vast human
and land resources have been invested in the quest to understand the
biogeography of life. This information is invaluable when considering future
distributions of species and their responses to global climate change patterns
and other anthropogenic influences shaping the biogeography of species. A
number of important questions can be posed in this regard. What restricts
geographic ranges, and why does a group of organisms vary within its range and
yet may be reproductively compatible across the range? How has the
heterogeneous environment selected for modified and plastic features? At what
point are organisms from two morphologically different populations considered
different species? What effect does interspecies gene flow have on the
systematics and evolution of the clade? Overall, how have the synergy of
geologic processes, climate, and time interacted with ecological and evolutionary
processes to produce the diversity of life around us? Finally, how have human
interactions affected, or will affect, biodiversity?

The objective of this dissertation is to combine information from
geographic range and ecophysiology to understand the distribution of two closely
related conifers - Rocky Mountain ponderosa pine (P. ponderosa var.

scopulorum Engelmann) and Arizona pine (P. arizonica Engelmann; formerly P.

ponderosa var. arizonica (Engelmann) Shaw) — in the Santa Catalina Mountains
of southern Arizona, USA. Within the isolated mountain islands of the American
Southwest, the systematics of the ponderosa pines remains unresolved (Rehfeldt
1993, 1999). Shared ancestry, dynamic climate and topography, strong selection
gradients, and hybridization have contributed to the difficulties in classifying
populations into species. Focusing on one geographic system - the Santa
Catalina Mountains — can elucidate processes occurring across other mountain
islands and species. Furthermore, following the devastating Bullock (2002) and
Aspen (2003) fires, the Coronado National Forest identified the need for
distribution information for improved management of the forest’s genetic
diversity.

This introduction serves to set the stage for the subsequent chapters on
the distribution, ecophysiology, and management implications of Arizona and
ponderosa pine in the Santa Catalina Mountains. Following a summary of the
putative origin and current status of the systematics of these species, I will
describe the geographic system in which my research was conducted. The
distribution and ecological modeling of the Ponderosae in the Santa Catalina
Mountains will be given in Chapter 2, while the ecophysiological studies that
shed light on the causes of their distributions will be addressed in Chapters 3-5.
Each of these chapters is intended as an individual paper without reference to
the other chapters or this introduction. Last, Chapter 6 will summarize the

findings and implications of this dissertation.

Origins of Ponderosa Pine

The model species for this research, Arizona and ponderosa pine, are
members of the Genus Pinus, Subgenus Pinus (Diploxylon or Hard Pines),
Section Pinus, and Subsection Ponderosae. Fossil evidence suggests that the
common ancestor for this genus emerged in the mid-latitudes of then-contiguous
northeastern United States and western Europe during the late Jurassic (Axelrod
1986, Millar 1993). At the time, the unified Laurasia was relatively homogeneous
in topography and climate. Tectonic activity was low, sea levels were high,
average temperatures were 10-20° C warmer than present in the middle and high
latitudes, and seasonality and latitudinal gradients in the Northern Hemisphere
were reduced compared to the present (Millar 1993). Subsequently, the newly
emerged Pinus began spreading east to west (Millar 1993) toward the Western
Interior seaway (Baldridge 2004).

By the end of the Cretaceous, Laurasia began to split, the Western Interior
seaway began to recede (Baldridge 2004), the Laramide orogeny (tectonism)
began creating new topography (e.g., elevation, dry slopes, rain shadows), and
the mid-latitudes began to dry leaving the low and high latitudes more humid
(Axelrod 1986, Millar 1993). This new geologic and climatic variability created
the opportunity for adaptation to new environments (Axelrod and Raven 1985),
and, facilitated by symbiotic associations with ectotrophic mycorrhizae, Pinus
began speciating into the major subsections of the genus by the late Cretaceous
(Axelrod 1983). Along with 5 other subsections in Pinus, Pondemsae likely

originated in the Cordilleran region of Colorado south into Mexico (Axelrod 1983).

Following on the heels of the western migration and subsectional
speciation of Pinus were the newly emerging boreotropical angiosperms in the
early Tertiary (Millar 1993). Increasingly warm and humid conditions in the mid-
latitudes during the Paleocene favored the more competitive and faster growing
angiosperms at the expense of pines (Millar 1993). Consequently, by the early
Eocene, widespread extirpation of pines in the lower elevations of the mid-
latitudes as well as the first major fragmentation of remaining pines into three
refugia are observed in the fossil record (Millar 1993). Some pines most closely
allied to Ponderosae retreated north to a circumpolar high—latitude zone (65-80°
N) where sufficient photosynthesis was still possible due to carbon dioxide levels
higher than at present (Millar 1993). Other Ponderosae lineages found refugium
at high elevation within the mid-latitudes (e.g., Colorado), while other lineages
sought the warmer and drier Eocene climate of low latitudes (e.g., Mexico and
Central America) (Millar 1993). The fragmentation of subsection Ponderosae
during the Eocene is thought to be the most significant event leading to the
speciation of Pinus ponderosa in the north (Axelrod and Raven 1985), with fossil
evidence near British Columbia (Axelrod 1986), and other distinct species
(possibly P. arizonica) to the south, as well as generating new centers of pine
diversity (Millar 1993).

At the close of the Eocene, the evolutionary environment changed
significantly. Average annual temperatures dropped 10-13° C with decreased
summer rainfall and increased seasonality, while mean annual range of

temperatures increased from 3-5° to 25° C (Millar 1993). Renewed tectonic

activity produced extensive volcanism and uplift producing the Rocky Mountains
and Mexican Sierra Madre Occidental and Oriental (Millar 1993, Baldridge 2004).
The complex climatic and geologic changes resulted in widespread extirpation of
the boreotropical angiosperms and concomitant expansion of pines back into the
mid-latitudes in some places in a few million years by the early Oligocene (Millar
1993). As the climate warmed and summer aridity increased through the
Oligocene and into the Miocene (Axelrod and Raven 1985), pines expanded their
range and abundance across latitudes, including mountainous low latitudes
(Millar 1993). The former refugia served as centers of pine diversity, radiating
outward to colonize the diverse environment. With at least a dozen closely
related extant species of Ponderosae endemic to the refugia in Mexico and
Central America, the events of the Eocene can still be seen through active
radiation and speciation within Ponderosae and other subsections of Pinus
(Millar 1993). The impact of the Eocene in forcing large latitudinal and
environmental shifts in distribution, and thereby dissecting the genus into
subsections, significantly contributed to the diversity in Pinus observed today
(Millar 1993).

Climatic fluctuations following the Oligocene to the present were
significantly less than those during the close of the Eocene. For example, the
amplitude of average temperatures during the 2.4-million-year Pleistocene (5-10°
C) was about the same as climatic cycles during the 20-million-year Eocene
(Millar 1993). Pleistocene glacial events caused significant shifts in pine

distributions generally from north to south or from high to low elevation. The

alternating distribution expansions and contractions (“pulsing”) allowed gene flow
between formerly disjunct taxa followed by isolation over dozens of warming and
cooling cycles. Through local adaptation, hybridization, and isolation, the genetic
structure of Southwestern flora, including Pinus, has changed, or pulsed, over
the last 20 million years through the present.

However, a discontinuity exists in the Quaternary fossil record for the
presence of P. ponderosa in the Southwestern isolated mountain systems, which
were generated long before by Eocene-Miocene crustal extension and tectonic
uplift (Baldridge 2004). Packrat midden data from van Devender et al. (1984)
extending back 40 ka did not show the presence of P. ponderosa in New
Mexico’s mountains until the last glacial maximum (18 ka), while no such
evidence appears in the Great Basin, Colorado Plateau, and current Southwest
desert regions for the same period (Thompson and Anderson 2000). Yet pollen
from the late Wisconsin (11.4 ka) has been detected in the Sonoran Desert
(Martin and Mehringer 1965), perhaps from distant forests (Betancourt et al.
1990). Furthermore, packrat midden data from montane forests in Arizona (van
Devender 1990) support the current belief that P. ponderosa was present at the
end of the Wisconsin (14 ka) but did not extend into the Sonoran Desert nor form
continuous cover between mountain systems or the high Colorado Plateau to the
north (Betancourt et al. 1990). During some pre-Wisconsin glacial, P. ponderosa
must have migrated south from the Colorado Plateau and Rocky Mountains.

Likewise, P. arizonica, with its purported more southern origin, presumably

migrated through the lowland conifers (e.g., subsection Cembroides) northward

into southern Arizona’s mountains during a cooler but more humid glacial.

Systematics of Ponderosa Pine

Due to the inherent complexity of the topography, historical biogeography
and evolution, as well as current evolutionary response to Quaternary climate
changes (Axelrod 1986), the systematics of subsection Ponderosae is
challenging and unresolved. In general, species closely related to ponderosa
pine have a wide distribution ranging from British Columbia to Montana and
south along the Cascade-Sierra Nevada, Rocky Mountains, isolated mountain
systems of the Great Basin, Colorado Plateau, and disjunct mountain islands
down into the Sierra Madre Occidental and Oriental of Mexico (Conkle and
Critchfield 1988). Ponderosa pine is one of the most widely distributed Pinus in
western North America (Oliver and Ryker 1990).

Of the many classifications of Pinus, Shaw (1914, 1924) placed P.
ponderosa (Douglas ex Lawson) within the Group Australes, although currently
the more accepted placement is within the subsection Ponderosae in the
classification by Little and Critchfield (1969). This placement is based on
morphologic features and is supported by nuclear and chloroplast phylogenies
(Gernandt 2005). In 1880, Engelmann described a Rocky Mountain form (var.
scopulorum) of P. ponderosa as having smaller cones, shorter needles (leaves),
and 2-needled fascicles as compared to populations found in Oregon and

California (Farjon and Styles 1997). The relatively low (<3%) frequency of 2-

needled fascicles (Haller 1965), significant isozyme differentiation (Conkle and
Critchfield 1988), and differential growth response in range-wide provenance
tests (Conkle and Critchfield 1988) of the more western populations (var.
ponderosa) effectively segregate the Rocky Mountain populations. Later, two
distinct races of var. scopulorum were identified: Rocky Mountain and
Southwestern (Conkle and Critchfield 1988). Within var. scopulorum, the
Southwestern trees produce higher frequency (>85% vs. 15—40%; Haller 1965) of
3-needled fascicles (average 2.7-3.2 needles/fascicle; Peloquin 1984),
differential growth responses (eg, slower early-season growth and double
growth flushes; Conkle and Critchfield 1988), and more d-pinene (50% vs. 5%;
Smith 1977), a monoterpene found in the xylem resin, compared to the Rocky
Mountain race. While the Southwestern race is clearly recognized, the transition
to the Rocky Mountain race occurs gradually and broadly over the Great Basin
and Colorado Plateau (Haller1965).

Shaw’s ( 1914, 1924) classification of P. ponderosa included P. an'zonica,
P. engelmannii, and P. jeffreyi, while Little and Critchfield (1969) and The Flora of
North America (Kral 1993) considered P. arizonica to be a variety (var. arizonica
Engelmann) of P. ponderosa. However, the consensus in this field is that
Arizona pine is a distinct species (P. arizonica Engelmann) from Southwestern
ponderosa pine (Peloquin 1984, Conkle and Critchfield 1988, Price et al. 1998)
due to its more southern distribution in isolated mountains of Arizona, New

Mexico, and Texas south into Mexico (Farjon and Styles 1997), production of 5-

needled fascicles (4.6-5.4 needles/fascicle; Peloquin 1984), and generation of
close to 100% d-pinene with no A3-carene (Peloquin 1971).

Obfuscating contemporary American classification of the species, authors
more familiar with the Mexican Pinus redescribed P. arizonica with usually fewer
needles per fascicle and further classified the species into additional varieties. In
Perry (1991), P. an'zonica occurs in the isolated mountains of southeastern
Arizona through the Sierra Madre Occidental, produces 3 (occasionally 4-5)
needles per fascicle, and has 6-10 (vs. 2-6 in P. ponderosa var. scopulorum)
needle resin canals. He further describes a P. arizonica var. stonniae (Martinez)
in scattered populations in west Texas and northeast Mexico with similar needle
number, longer needles (20-30 cm vs. 12-22 cm), and fewer (3-8) resin canals
than P. arizonica. Farjon and Styles (1997) split P. arizonica into three separate
varieties: arizonica, stonniae, and cooperi. The first variety is distributed in
southern Arizona and New Mexico through the Sierra Madre Occidental and is
characterized by variable needle number (3-4, 5 closer to Sonora) and needle
length (10-20 cm) (Farjon and Styles 1997). As by Perry (1991), Farjon and
Styles (1997) characterized P. arizonica var. stormiae with a more eastern
distribution, longer (14-25 cm) needles, and longer cones. Finally, although
found in Durango and the central Sierra Madre Occidental, P. arizonica var.
coopen' (Blanco) is characterized by producing 5 (4-5) needles per fascicle and
shorter (6-10 cm) needles (Farjon and Styles 1997).

The great difficulty in segregating the various morphological variations

(morphotypes) within subsection Ponderosae is in part due to environmental

clines producing geographic variation within the taxa but also their interfertility.
Conkle and Critchfield (1988) summarized results of over 50 years of artificial
crossing of Ponderosae at the Institute of Forest Genetics in Placerville,
California. This work demonstrated moderate (5-40%) crossability of the Pacific
race of ponderosa pine (P. ponderosa var. ponderosa) with the Rocky Mountain
race, Arizona pine, Apache pine (P. engelmannii Carriere), and other Mexican
species in the Ponderosae; the Rocky Mountain race of ponderosa pine and
Arizona pine were not crossed. Within the Mexican species, crossability was
high (>40%) (Conkle and Critchfield 1988). Based on 14 species-specific
morphologic and monoterpenoid characteristics, Peloquin (1971, 1984)
concluded that Southwestern ponderosa pine, Arizona pine, and Apache pine
naturally hybridize in all combinations, including rare three-way hybridization.
Like Arizona pine, Apache pine lacks A3—carene and has 100% d-pinene
(Peloquin 1984) but produces 3 relatively long (25-35 cm; Farjon and Styles
1997) needles per fascicle.

Biogeographically, these species tend to be elevationally parapatric with
Apache pine replaced by Arizona pine which is then replaced by Southwestern
ponderosa pine with increasing elevation (Peloquin 1984). When grown in
common gardens, genetically and environmentally controlled characteristics of
the three species, as well as putative hybrids produced mostly from Arizona pine
and Southwestern ponderosa pine, demonstrate the influence of geographic
clines (Rehfeldt 1993). In the Southwestern mountain islands, Rehfeldt et al.

(1996) identified a “Taxon X” that combined the three-needled Southwestern

10

ponderosa pine with trees producing a mixed (3,4, and 5) number of needles
within a given year. This hypothetical Taxon X also demonstrates a strong
geographic cline (i.e., spatial structure) in needle morphology (Rehfeldt 1999,
Epperson et al. 2001) and growth (Rehfeldt 1999), with little spatial structure in
allozymes (Epperson et al. 2003). Despite significant spatial autocorrelation,
geographic clines do not necessarily signify hybridization but rather
environmental influence and/or genetic differentiation. For example, Epperson
et al. (unpublished data) have documented significant spatial autocorrelation in
chloroplast DNA single-sequence repeats (chNA SSRs) from sympatric
Ponderosae and have identified two unique populations based on chNA
corresponding to the Arizona pine and Taxon X morphotypes. However, chNA
is paternally inherited via pollen in Pinus (Wagner 1992) and since pollen flow in
ponderosa pine is considered unlimited (Latta et al. 1998), this genetic structure
could also be interpreted as unidirectional hybridization of Arizona pine by
Southwestern ponderosa pine pollen. In Japan, Watano et al. (1995) proposed
unidirectional hybridization based on chNA haplotype patterns within a putative
hybrid transition zone between Pinus pumila and P. parviflora var. pentaphylla.
Additionally, comparison of multiple gene markers (Watana et al. 1996, Latta and
Mitton 1997, Gugerli et al. 2001, Ribeiro et al. 2002, Burban and Petit 2003),
including maternally inherited mitochondrial DNA and biparentally inherited
nuclear DNA (Wagner 1992), is required to detect presence and direction of
hybridization. For example, in a separate paper, Watano et al. (1996) found

unidirectional introgression from mitochondrial DNA haplotype patterns but in an

11

equal and opposite direction as the chNA haplotypes in their earlier paper,
thereby concluding that gene exchange through both pollen and seed dispersal
contribute to the formation of the hybrid zone in P. pumila and P. parviflora var.
pentaphylla. Demonstrated strong interfertility and geographic variation within
subsection Ponderosae, like other subsections in Pinus (Price et al. 1998),
indicate that introgression across sympatric populations of Ponderosae is

possible and still requires more detailed investigation.

The Santa Catalina Mountains of Southern Arizona

The origin of the contemporary topography and geology of the Santa
Catalina Mountains began 35 Ma with the final subduction of the Farallon Plate
beneath the North American Plate (Baldridge 2004). For 140 Ma, the Farallon
Plate had been slipping beneath the North American Plate, thus fueling the
extensive Laramide orogeny across the West (Baldridge 2004). By 28 Ma, the
trailing end of the Farallon dipped beneath the vast continent thus fueling silicic
volcanism from central Mexico through southern Arizona, New Mexico, Texas,
and Colorado and forming the Sierra Madre Occidentals and many of the isolated
mountain islands of the Southwest (Baldridge 2004). Furthermore, changes in
the gravitational potential energy of the lithosphere of the Southwest, due in part
to the Pacific Plate grinding against the North American Plate and thinner, more
buoyant mantle in the Southwest, generated an extensive force that effectively

structured the contemporary Southwest landscape (Baldridge 2004).

12

This extensional orogeny and deformation process was important in
producing mountains and drop fault zones throughout the Southwest, thus
forming the Basin and Range province (Baldridge 2004). The Santa Catalina
Mountains were formed through one type of orogenic extension known as a
metamorphic core complex (Force 1997, Baldridge 2004). As upper-crust non-
metamorphic (sedimentary) rock became strained by extension, low-angle shear
zones formed above the core complex of middle-crust metamorphic (gneiss;
Lucchitta 2001) and plutonic rocks, which were then exposed and forced upward
in elevation (Force 1997, Baldridge 2004). As the sheared upper crust thickened
along the fault line, it became denser and sank (“gravitational collapse”) lower in
elevation than the newly exposed metamorphic rock (Baldridge 2004). The
Santa Catalina Mountains are particularly complicated compared to other
metamorphic core complexes clue to multidirectional extension and shearing
forces, intrusions, and uplifts (Force 1997). In general, since crustal tension
occurred in an east-west direction, linear mountain ranges like the Santa Catalina
Mountains formed perpendicular to the strain direction with broad sediment-filled
valleys, or basins (i.e., Basin and Range province), separating the ranges
(Baldridge 2004).

Roughly triangular in shape and extending over 510 kmz, the Santa
Catalina Mountains rise from a basal elevation of less than 760 m in the Tucson
Basin to 2,791 m (9,157 ft) on Mount Lemmon. The WNW — ESE ridgelines are
separated by steep escarpments into the smaller forerange on the southwest

side and the major Mount Bigelow (2608 m) and Mount Lemmon crest to the

13

northwest. The Samaniego, Red, and Oracle Ridges extend the crestline to the
north. Shallow lithosols characterize the soil in this precipitous environment, with
granitic-based soils in the south and a more complex interspersion of soils
derived from various granites, argillite, quartzite, schist, andesite, slate, shale,
and limestone in the northern 2/3 of the range (Whittaker and Niering 1965,

1968; Force 1997). Soil characteristics in the primarily forested zone (1830-2130
m elevation) show low fertility on diorite and limestone, respectively: bare rock 5-
20 and 20-60%; pH 5.5-7.0 and 8.0; organic matter content 4.4 and 5.1%;
nitrogen content 0.12 and 0.21%; carbon:nitrogen 20.0 and 14.2; and cation
exchange capacity 17.1 and 25.8 m-eq/100 g (Whittaker 1968; Whittaker and
Niering 1968). Minimum soil moisture is uniformly low (<5% at 15 cm) below
2000 m in elevation but increases to 28% on north slopes and 9% on south
slopes near the peak (Shreve 1915). Haase (1970) likewise found the south to
southwest aspects to be significantly more arid during all times of the year, while
soil with southern exposure and at lower elevations experience a greater diurnal
temperature range (Shreve 1924). Mature drainages with steep sides dissect the
slopes and carry water and eroded crustal material downhill to the extensive
bajadas of the Tucson and San Pedro Basins.

Because the topographically complex and arid Southwest is
geographically close to the Pacific Ocean, Gulf of California, and Gulf of Mexico,
as well as located between mid-latitude and subtropical atmospheric circulation
regimes, the region experiences a unique bimodal precipitation pattern (see

review by Sheppard et al. 2002). A subtropical high-pressure ridge (“Bermuda

14

High") advances northward, while the predominant westerlies retreat, and high
solar insolation over large upland areas bordered by lowlands generates
convection for large amounts of oceanic moisture to move into the Southwest,
especially Arizona. Over 60% of the total annual precipitation for the Santa
Catalina Mountains falls during this North American monsoon from July through
September. Much of this rain falls as intense convective storms with frequent
lightning over the mountains in the late afternoon, rushes downslope as
flashfloods, and evaporates from the soil surface (personal observation). Most of
the remaining moisture falls during the winter months as large cyclonic storms
come off the Pacific (Sheppard et al. 2002). These rain events are typically
widespread with soaking rains at low elevations and snowfall at high elevations
for consecutive days. Climate variability in the Southwest is strongly influenced
by the interactions and synergism of the El Niflo - Southern Oscillation (ENSO)
and Pacific Decadal Oscillation (PDO), which acts to strengthen ENSO effects in
positive years and dampen ENSO effects in negative years (Sheppard et al.
2002). For example, years of combined El Nino and positive PDO result in cool
and wet winters, while negative PDO enhances the warm and dry winter effects
of La Nifla years (Sheppard et al. 2002). Over the last century, the Southwest
has experienced low-frequency climate variability with periods of abundant
moisture (1905-1930), extensive drought (1942-1964), and warm, wet winters

. with irregular summers since 1976 (Sheppard et al. 2002).

The influence of changing environment with elevation on vegetation

patterns in the Santa Catalina Mountains has been reported in classic studies by

15

several well-known plant ecologists (Shreve 1915, 1922, 1924; Fuller 1916;
Whittaker and Niering 1965, 1968, 1975). In the broadest sense, Shreve (1915)
described four vegetation types from low to high elevation: desert, encinal (from
encina, Spanish for evergreen oak, by J.W. Harshberger; Shreve 1915), and
forest, which he further divided into pine and fir forests. In the archetypal
gradient analysis, Whittaker and Niering (1965) described 9 vegetation types by
elevation, aspect, aridity, and topography (i.e., canyons versus slopes)
summarized by their diagram in Figure 1-1. Further work by Whittaker and
Niering (1968) showed a similar transition from Desert Scrub through pine forest
on limestone and granite derived soils with an upward elevational shift in species
distributions on the limestone soils probably due to increased bedrock fissuring
that drains available moisture, as well as a Cercrocarpus Scrub zone below the
Pine-Oak Woodland.

Whittaker and Niering’s (1965) Pine Forest was dominated by ponderosa
pine with interspersed western white pine (Pinus strobiformis). At high elevation,
dense ponderosa pine stands develop and become successional to Douglas-fir
(Pseudotsuga menziesir) (Peet 1988). Other forest components above dominant
ponderosa pine include white fir (Abies concolor), subalpine fir (Abies
Iasiocarpa), corkbark fir (Abies Iasiocarpa var. arizonica), and quaking aspen
(Populus tremuloides) (Whittaker and Niering 1965, Peet 1988). Within mid-
elevations, ponderosa pine naturally forms a woodland mosaic of interspersed
grassy patches (Peet 1988). Lower in elevation, ponderosa pine is replaced by a

more open woodland of encinal (Quercus spp.) and pygmy conifer forest

16

containing Chihuahuan pine (Pinus chihuahuana), pinon pine (P. cembroides),
junipers (Juniperus spp.), and Arizona cypress (Cupressus arizonica) (Whittaker
and Niering 1965, Peet 1988). Stand density and structure of all these forest
types are strongly influenced by fire and interannual climate variability (Peet

1988).

17

 

Veqetanon of the Santa Catalma mountain:

(South slope. Data above 9000 feet from Pinaleno Mountains.)

 

T l l l l l l Tl I l
Picea engelmanii

  
 
 
  
   
   
  

Subalpine Forest

1000( —
3000 L
Abies Iasiocarpa

    
     
  
   
  
  

" Mixed Conifer Forest Pseudotsuga menziesii

Alnus tenuifolia
p _____

   
   
      
 
 

    

 

 
  
    

 

  
  
  
   
 
 

9000 —- —- -Montane Fir Forest —- —Pinus strobiformis — -
Pinus ponderosa
Abies ooncolor
Acer Iabmm
zsoor Acer gmndjdentum Pine Forest Quercus rugosa
8000 J Ace! grandidentum . -
Pinus ponderosa
Pine-Oak Forest
7000 Pinus ponderosa . _
Quercus gambe/ii Quercus hypo/eucordes {nus cembroides
2000 Alnus oblong/fo/ia Juniperus deDDeana
- . Quercus arizonica
PIne-Oak
W_oodland
6000 — (Upper Encinal) _
I
I

Pinus chihuahuana I
Pinus ponderosa

Quercus hypoleucoides

  
 

‘— Juglans major
Cupressus arizonica
Canyon
1500» 5000 — WOOdIand
Plateaus wn’ghtii

Juniper-us deppeana
Quercus arizonica
Quercus emoryr

Open Oak Woodland Vauquelinia califomica
(Lower Encinal)

   
   
    
  
  
  
 

Fraxinus . .
velurina Quercus oblongrfolla -

Desert Grassland

 
   
    
 

    

 

 

 

 

 

4000 — Fouquien‘a splendens
Pmsopis juliflora

P l s

”22%;,” Sonoran Desert Scrub -
1000 ._ .- Cercidium microphyllum
Elev. Elev. 99”" Camegiea giganrea

m. rt. rel/cuffs . P l_~ Fouquien'a splendens A I I
Ravines
Draws
NNE NE ENE E ESE SE SSE S SSW
N NNW NW WNW W WSW SW
Mesic Xeric

 

Figure 1-1. Vegetation of the Santa Catalina Mountains as drawn by Whittaker and
Niering (1965) from 400 vegetation samples in a gradient analysis. Note that Pinus

 

arizonica is not recognized as a species separate from P. ponderosa but replaces the
latter species between the Pine and Pine-Oak Forests (personal observation). Also note

that Picea engelmannii does not occur in the Santa Catalina Mountains.

18

The 19‘"-20th century changes in fire regime and widespread introduction
of grazing by domestic livestock have had a profound effect on the forest
structure and composition in the Santa Catalina Mountains. Due in part to a lack
of knowledge of pre-European lightning frequency as well as anti-Native
American bias, pre-European humans were considered to be the primary driver,
while climate and lightning were secondary drivers to the past fire regime in the
mountainous regions of the Southwest (Swetnam and Baisan 2003). Based on
fire scar records, large fires occurred synchronously about every 7.5 years from
1700-1900 in Southwestern ponderosa pine forests (Swetnam and Baisan 2003).
While prehistoric fire frequencies were generally highest during times of greatest
human conflict, the understory fuel amount, which takes a few wet years of good
growth to accumulate between fires, is strongly associated with large fires in the
xeric ponderosa pine forests (Swetnam and Baisan 2003). Domestic livestock
(“hoofed locusts;” Muir 1911) introduced in the late 19th century reduced
interannual fuel accumulation by grasses and forbs. As a consequence, fire
frequency significantly decreased; the last widespread fire of the 20th century
occurred in 1900 (Swetnam and Baisan 2003). Due to reduced fire frequency
from livestock grazing and organized fire suppression, begun in the Santa
Catalina Mountains by the Forest Service during the 1910 Alder Canyon fire
(Alexander 1991), stand density of Southwestern ponderosa pine in Arizona
increased by several orders of magnitude through the 20th century (Moore et al.

1999). Finally, after close to a century of fire suppression, the Bullock (2002) and

19

Aspen (2003) Fires burned close to 470 kmz, or 92% of the Santa Catalina
Mountain area (Coronado National Forest 2002, 2003).

Human settlement and use of the Santa Catalina Mountains has certainly
had a profound effect on the system. Evidence of the Hohokam living throughout
the Santa Catalina Mountains exists from 100 BC. to 1400 AD, after which they
disappeared (Alexander 1991 ). Shortly afterward, Apache people occupied the
northern foothills and lower slopes during the Summer (Alexander 1991). The
earliest Spanish explorer was Francisco Vasquez de Coronado in the 1540s
(Alexander 1991), and the 17'“ century arrival of Spanish settlers in the Santa
Cruz Valley distressed the Apaches, whereby they raided settlers and clashed
with government armies until the late 1880s (Alexander 1991). There are two
stories about the European naming of the mountains: in 1697, Father Eusebio
Kino called them the Santa Catarino Mountains; and, in the 16908, a Jesuit
mission in the northern foothills was called Santa Catalina de Cuitabagu, or “well
where people gather mesquite beans" (Alexander 1991). The Europeans,
though, recognized the potential for mineral wealth in the mountains and began
exploring for gold as early as 1871 (Alexander 1991). Homesteading associated
with mine claims began in 1880, and businesses to support the mining trade
soon sprung up in the mountains (Alexander 1991). The highest point, Mount
Lemmon, was named for Sara Plummer Lemmon, who together with botanist
John Gill Lemmon and Emerson Oliver Stratton, were the first Europeans to be

recognized as reaching the peak (Alexander 1991). During this trip, J.G.

20

Lemmon first noted the presence of Arizona pine, which he recognized as a
variety of ponderosa pine (Alexander 1991).

In 1902, President Theodore Roosevelt established a 100,000-acre (404-
kmz) forest reserve in the Santa Catalina Mountains and, in 1908, created the
Coronado National Forest, which then included the Dragoon, Rincon, Santa Rita,
and Whetstone Mountains of southern Arizona (Alexander 1991). Trails to the
summit begun in 1897 were improved, fire towers at Lemmon Rock Lookout
(1913) and Mount Bigelow (1916) as well as numerous look-out trees aided fire
detection efforts, and eventually a road was constructed to the summit
(Alexander 1991). The village of Summerhaven, Boy and Girl Scout camps, and
Forest Service campgrounds and picnic areas were developed from early
camping sites throughout the early 20th century (Alexander 1991). In 1953,
several ski runs and a lodge were constructed near the summit, bringing even
more tourists to the mountain. The US. Air Defense Command established and
operated a radar station (Semi-Automatic Ground Environment, 684“1 Radar
Squadron) at the summit from 1959 through 1969 (Radomes, Inc. 2005). In 1962
and 1970, the University of Arizona constructed astrophysical observatories on
Mount Bigelow and at the newly abandoned radar station on Mount Lemmon,
respectively (Alexander 1991). Following the recent reconstruction of the 28-
mile-Iong paved Catalina Highway, originally completed in 1949 (Alexander
1991), 22,000 vehicles per year of recreationists, homeowners, land managers,
contractors, and scientists travel into the Santa Catalina Mountains (PCDOT

2007).

21

The nature of the Santa Catalina Mountains has changed dramatically
over time, none more so than since Europeans began exploring its economic and
recreational potential. While the ebb and flow of glacial climates allowed slow
intermixing of Rocky Mountain and Madrean species, we are now challenged
with understanding the resultant genetic diversity in the face of altered fire
regimes, human uses of the mountain resources, and relatively rapid climate
change. I embarked on this quest to examine ecophysiological influences on the
distribution for one closely related group of species in the Ponderosae with the
aim of contributing to a better understanding of their distribution, ecology, and

genetic diversity, and with important implications for their management.

22

CHAPTER 2
DISTRIBUTION AND ECOLOGICAL NICHE DIFFERENTIATION

OF SYMPATRIC PONDEROSAE TAXA
IN THE SANTA CATALINA MOUNTAINS OF ARIZONA

Introduction

Phylogeographers are challenged not only with the genetic vagaries of
species delimitation, especially with closely related and sympatric taxa, but also
the difficulties in delineating the distribution of species. The geographic range of
a species is commonly considered to be a spatial manifestation of its ecological
niche (Brown and Lomolino 1998). However, one must consider the various
interpretations of the niche when discussing the potential distribution of species.
Early uses of the term “niche” implied a place in the environment that could
support a species, thus the environment held a certain number of niches (Elton
1927). Conversely, Hutchinson (1957) proposed the n—dimensional hypervolume
in which a species can persist indefinitely within the boundaries of two measured
independent environmental variables and cannot exist outside of those
boundaries. This fundamental, or Grinnellian, niche will be considerably larger
than its realized niche, or the region between those environmental variables
where the species actually exists, due to biotic interactions (Hutchinson 1957).
Dispersal, whether limited or extended beyond suitable environmental conditions,
can obfuscate the relationship between the geographic range and realized niche

(Pulliam 2000). However, beyond the local scale, the geographic distribution of

23

species is largely influenced by the environment, especially climate (Woodward
1987).

The relationship between the geographic distribution and spatially
structured environmental characteristics can be used to investigate the potential
distribution and fundamental ecological requirements for a species. Because the
influence of biotic interactions varies across a species’ geographic distribution,
models representing the potential distribution, or suitable habitat (Guisan and
Thullier 2005), for a species are arguably more analogous to its fundamental
rather than realized niche (Peterson et al. 2002). The amount of research using
predictive species distribution models (SDMs) to understand the ecology and
evolution of species has exploded over the last 10 years due to advances in
computing technology. For example, even with few records of occurrence, the
distribution of rare Madagascaran geckos were successfully modeled with
advanced forms of species distribution models (Pearson et al. 2007), which can
then be used to focus search and conservation efforts. From comparisons of
occurrences to mean climate values through simple linear and additive models,
Vetaas (2002) inferred the relative role of cold versus hot temperatures on the
distribution of Himalayan Rhododendron species from herbarium specimens and
field data. The potential role of competition between closely related South
American pocket mice was identified through overlapped predicted distributions
as a function of relevant environmental variables (Anderson et al. 2002).
Peterson et al. (1999) used reciprocal geographic distributions to demonstrate

the role of geographic rather than ecologic isolation during speciation between

24

sister taxa of butterflies, birds, and mammals in Mexico. Environmental
divergence during speciation of Andean frogs was demonstrated with principal
components analysis (PCA) of environmental variables determined to be suitable
habitat for different species through predictive models (Graham et al. 2004).
Using an ecological niche modeling method, Costa et al. (2002) successfully
identified four ecologically distinct populations of a major Chagas’ disease vector
in Brazil resulting in a change in monitoring and control measures. Application of
a variety of ecological niche and species distribution models has resulted in
advancement in the understanding of species' biogeography, ecology, evolution,
and conservation.

An assortment of SDM methods have been developed that can produce
successful models using only documented presence localities (“presence-only”).
Sources of occurrence data, such as herbaria, museums, and field surveys, do
not usually incorporate documentation of absences, especially spatially
redundant for rare or cryptic species. Spatial interpolation using explanatory
variables of geographic coordinates and neighborhood similarity as measured by
abundance is capable of producing comparable models to those using
environmental variables in a regression tree algorithm (Bahn and McGill 2007),
which demonstrates the predictive value of spatially structured variables.
However, most presence-only modeling methods use information about where a
species is documented to occur and its associated environmental characteristics
to generate a habitat suitability map (Guisan and Thuiller 2005). In these

models, pseudo-absences are randomly drawn from the background to evaluate

25

the model’s ability to correctly predict the recorded occurrences for the species.
Of course, the modeled species is assumed to be in pseudo-equilibrium with its
environment, thus overfitted models may conservatively underestimate the
spatial extent of the potential distribution for the species (Guisan and Thuiller
2005). Another major assumption of any SDM is that an appropriate scale, both
resolution and extent, is used in the modeling exercise (Maurer 2002, Guisan
and Thuiller 2005; but see Guisan et al. 2007). Although each environmental
variable will interact with a population at a different scale (Thomas et al. 2002),
modeling algorithms usually require constant scale across explanatory variables.
Despite these and other assumptions and limitations to SDM (Guisan and
Thuiller 2005), the continuing advances in statistical techniques have resulted in
great strides in understanding the biogeography of organisms.

The comparison of modeling algorithms, each with means to reduce
autocorrelation effects and estimate goodness-of-fit, in a variety of species
modeling challenges has been extensive in the literature (e.g, Segurado and
AraL'Ijo 2004, Guisan and Thuiller 2005, Elith et al. 2006, Tsoar et al. 2007).
Some of the more common presence-only algorithms include regression
(generalized linear, additive, and multivariate models), bioclimatic envelope,
boosted decision trees, artificial neural networks, classification trees, decisions
made with genetic rule sets, and maximum entropy (Elith et al. 2006). The latter
two methods are drawn from the machine-learning community and are
considered to be the most advanced (Elith et al. 2006) at this time. As

implemented in GARP (Genetic Algorithm for Rule-Set Prediction,

26

httpzllnhm.ku.edu/desktopgarp/index.html), the genetic algorithm uses species
localities and environmental data to produce a niche-based model using
conditional decision rules from a combination of approaches, including atomic,
logistic regression, and bioclimatic envelope (Anderson et al. 2002). The
maximum entropy algorithm, as employed by Maxent (Maximum Entropy Species
Distribution Modeling, Phillips et al. 2006b), calculates a probability distribution of
maximum entropy (i.e., closest to uniform) when constrained by incomplete
information about the species distribution, which presents itself as species
occurrences within pixels across the study area and the associated
environmental variables (Phillips et al. 2006). Under the maximum entropy
distribution, the expected value (probability) of each pixel is equal to its empirical
average from the occurrence data (Phillips et al. 2006). Consequently, Maxent
may be conservative by constraining probabilities in the environmental niche
space where the species is not known to occur (Pearson et al. 2007).

The Ponderosae of the mountain islands in the American Southwest
presents an opportunity to employ SDMs to segregate the realized niche of taxa
with overlapping distributions by their association with the heterogeneous
environment. These wind-pollinated conifers are extensively distributed from
central Mexico through British Columbia and throughout the major mountain
ranges and foothills (Little and Critchfield 1969). Despite significant outcrossing
(>90%; Mitton et al. 1977), the Rocky Mountain ponderosa pine (Pinus
ponderosa var. scopulorum Engelmann) experiences significant intrapopulation

allozyme variation associated with geographic clines, such as elevation (Hamrick

27

et al. 1989, Rehfeldt 1990) and slope aspect (Mitton et al. 1977, 1980).
lntrapopulation genetic variation of morphological characteristics, such as
needles per fascicle, needle length, shoot length, and heightzdiameter ratio
(Rehfeldt 1999), are also associated with geographic clines. Factors varying with
geographic clines such as elevation and slope aspect and that seem to influence
genetic variation center on length of the frost—free season and patterns of
precipitation (Rehfeldt 1990). For example, years with prolonged drought are
linked to decreased number of needles per fascicle in the following year (Haller
1965). Ecological response surface models produced by Humphries and
Bourgeron (2003) identified maximum January temperature, annual solar
radiation, and maximum July temperature as important predictors for presence of
mature ponderosa pine in granite-based soils. Limited pollen dispersal (Latta et
al. 2001) and limited seed-bearing trees (Linhart and Mitton 1985), in
combination with strong environmental selection gradients in mountainous areas
(Rehfeldt 1986, 1990), generates considerable genetic and morphologic spatial
structure at local scales in ponderosa pine (Epperson et al. 2001). Although less
work has been done on the closely related Arizona pine (P. an'zonica
Engelmann; formerly P. ponderosa var. arizonica Engelmann) (except see
Barton et al. 2001), its mating system and interactions with the environment are
expected to produce similar genetic structures.

Due to extensive geographic variation, the systematics of the ponderosa
pine and its closely related species in the Southwest’s mountain islands has not

been resolved (Kral 1993). The Southwestern race (Conkle and Critchfield 1988)

28

of the Rocky Mountain ponderosa pine reaches its southernmost distribution in
these disjunct habitats, while the Sierra Madrean Arizona pine extends northward
into southern Arizona, New Mexico, and Texas (Farjon and Styles 1997).
Putative hybrids with varying needle number are reported by Peloquin (1971,
1984) to occur where the species distributions come into contact; artificial
hybridization between closely related Ponderosae has been demonstrated by
Conkle and Critchfield (1988). Alternatively, based on allometric, needle, and
phenologic properties expressed in a common garden, Rehfeldt (1996) suggests
that a nonhybrid “Taxon X,” identifiable in the field by producing variable needle
number (range 3-5, mean 3.3, standard deviation 0.4; Rehfeldt et al. 1996) in
comparison to the mean 3- and 5-needled fascicles of Southwestern ponderosa
and Arizona pine, respectively (Peloquin 1984), replaces the commonly identified
ponderosa pine of the Southwest. In the Santa Catalina Mountains of southern
Arizona, Epperson et al. (2001, 2003) demonstrated a very strong elevational
cline in number of needles per fascicle (0.55-0.64% heritable; Rehfeldt 1993)
with little differentiation in allozymes across a contact zone between Arizona and
Southwestern ponderosa pine. Unpublished work on the same trees by
Epperson et al. shows strong spatial structure in chloroplast DNA single-
sequence repeats (chNA SSRs) with two unique populations with regard to
chNA heritage: trees corresponding to Arizona pine and the Taxon X
morphotype.

The objectives for this paper are to document the geographic distribution

and determine if there is any detectable ecological niche differentiation between

29

the closely related Ponderosae taxa in the Santa Catalina Mountains. These
taxa form a relatively continuous cover interrupted only by uninhabitable rock
from about 1760 m to the summit of Mount Lemmon at 2791 m, depending on
aspect and associated changes in forest cover; on the north slope of the peak,
ponderosa pine becomes a minor component of a mixed subalpine forest
community. Populations producing a mean of 3 needles per fascicle at the
summit and on the north slope of Mount Lemmon, 5 needles per fascicle below
2438 m, and 3-5 needles per fascicle with high intratree variance (“Taxon X” of
Rehfeldt et al. (1996), or “mixed”) between these two elevations have been
reported by Dodge (1963) and observed by others (F. Telewski and T. Harlan,
personal communication). Following the Bullock (2002) and Aspen (2003) fires,
the Coronado National Forest (W. Hart, personal communication) identified a
need to recognize the genetic diversity of the Ponderosae in the Santa Catalina
Mountains and to delineate their distributions for reforestation and conservation
efforts.

Given the close genetic relatedness within Ponderosae, I expect
distributions for all of the taxa to be associated with similar climatic factors,
including minimum winter and maximum arid foresummer temperatures, and
monsoonal precipitation. However, I expect that the 3- and 5-needled trees
should have little overlap in ecological niche space. Under the hybridization
hypothesis, the mixed-needled trees should have an intermediate ecological
distribution, or niche overlap, and be less sensitive or intermediate in response to

relevant climatic factors than the other taxa (Anderson and Stebbins 1954).

30

Assuming Taxon X (Rehfeldt et al. 1996), the 3- and mixed-needled trees should

have the same distribution, occupied niche space, and climatic sensitivity.

Methods
Study area

The Santa Catalina Mountains are located within the Basin and Range
Province of the American Southwest (Figure 2-1). These isolated mountains
were formed from a metamorphic core complex uplifted as strong crustal
extension forces sheared the overlying sedimentary rock (Force 1997, Baldridge
2004). Multidirectional extension, shearing, intrusions, and uplifts have produced
a ruggedly complex landscape in this system (Force 1997). The resulting
mountainous area is roughly triangular in shape, extends over 510 kmz, and rises
from a basal elevation of less than 760 m in the Tucson Basin to 2,791 m (9,157
ft) on Mount Lemmon. Soils are shallow lithosols derived largely from acidic
granites and local outcrops of sedimentary rock (Whittaker and Niering 1965,
1968; Force 1997). Mature drainages with steep sides dissect the slopes and
carry water and eroded crustal material downhill.

The semiarid climate is characterized by the Southwestern bimodal
precipitation pattern with an arid foresummer (May-June) followed by summer
monsoon (Sheppard et al. 2002). Annual precipitation is about 820 mm at
Palisade Ranger Station (2426 m elevation) near the observed transition zone
between the 3— and 5-needled trees (Western Regional Climate Center 2007),

with approximately half falling during the summer (Whittaker and Niering 1965).

31

 

Figure 2-1. General distribution of ponderosa pine (top. Pinus ponderosa.
Thompson et al. 1999), and location of study region in the Santa Catalina
Mountains in southeastern Arizona (bottom. USGS 2004).

32

The minimum January, maximum June, and annual temperatures at this location
are -4, 23, and 15° C, respectively (Western Regional Climate Center 2007).

The vegetation is strongly influenced by elevation and associated changes
in climate (Shreve 1915, Whittaker and Niering 1965). Based on Whittaker and
Niering’s classic study using gradient analysis in the Santa Catalina Mountains,
the first “ponderosa pine” trees at low elevation occur around 1500 m elevation
mixed with pinon pine, (Pinus cembroides), Chihuahuan pine (P. chihuahuana),
and oak scrub (Quercus spp.). From about 1900 to 2500 m elevation, depending
on aspect and topography, ponderosa pine forests are dominant. Forests near
the Mount Lemmon peak become dominated by Douglas-fir (Pseudotsuga
menziesil), white fir (Abies concolor), and Arizona corkbark fir (Abies Iasiocarpa

var. arizonica), with interspersed ponderosa pine

Field data collection

Georeferenced species occurrence (presence-only) and needle samples
were derived from several sources. Most of the data (n=671 samples, “SCAT;”
data in Appendix, Table A-1) were collected with the intent to examine the spatial
distribution of the three morphotypes in the Santa Catalina Mountains. Sampling
locations were determined in an arbitrary manner to efficiently capture the spatial
variation of these morphotypes. Based on species cover maps (Terri Austin,
CNF, personal communication), institutional knowledge (William Hart, CNF,
personal communication), and past observations (Frank Telewski, and Thomas

Harlan, University of Arizona, personal communication), sampling locations were

33

located prior to entering the field so as to confirm centers of and transitions in
distribution between the three morphotypes. Accessibility was limited to
proximity of road and trail due to the rugged terrain, thus some geographical bias
was introduced by the sampling methods. At each predetermined location, over
300 geographic positioning system (GPS; GPSMAP 6008, Garmin International,
Inc., Olathe, Kansas, USA) readings were averaged to increase georeferencing
accuracy. The point-centered quarter method was used to select the four closest
Ponderosae trees. This method was used to increase the efficiency of species
data per effort in a less subjective manner (Cottam and Curtis 1956) and was
employed by Dodge (1963) when examining biogeographic patterns of
Ponderosae in Arizona. From each tree, diameter at breast height (1.37 m) and
distance to the GPS unit were recorded, and 1-4 needle-bearing branches from
the most southern accessible side were cut with 23-ft pole pruners and stored in
labeled paper bags; the more xeric southern exposure was expected to force
variable production of needle number per fascicle, but a subsequent examination
by aspect from trees across a transition zone (Epperson et al. 2001) yielded no
difference (two-sample t-test, p>0.12, n=62 trees (19713 fascicles». In some
cases, the closest tree had been recently killed by fire (CNF 2002, 2003) or
branches were completely inaccessible, so the next closest tree in the quadrant
was sampled. Predominant cover type and other notes were also recorded.
From August 2004 to October 2006, 671 georeferenced samples were collected

from 176 locations (Figure 2-2).

34

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35

Samples were also collected in association with other components of this
research program (“Non-SCAT,” Figure 2-2). Needles from at least the south
side of each tree were similarly collected, and georeferenced locations were
recorded by GPS at Kimball Peak Saddle (n=10 in 2001; GPS 12, Garmin
lntemational, Inc., Olathe, Kansas, USA), Bear Canyon (n=15 in 2002; GPS 12),
Wilderness of Rocks (n=10 in 2002; GPS 12), and Mount Lemmon (n=183 in
2004; G820 Personal Data Mapper, Leica Geosystems AG, St. Gallen,
Switzerland). I identified the coordinates for additional trees (n=23) sampled and
intensively studied at Palisade Rock with the aid of field notes and an online
mapping program (Google Earth, URL: http:l/earth.google.com, accessed May
2007)

Needles from each sample were examined by year to determine the
average needle number for the tree (Epperson et al. 2001). Needle number was
calculated both by year and across sampled years. Species, or morphotype,
determination was by Peloquin’s (1984) classification: trees with mean needle
number less than 3.2 are Southwestern ponderosa pine, greater than 4.6
needles per fascicle are Arizona pine, and between 3.2 and 4.6 needles per
fascicle are Mixed (hybrids (Peloquin 1984) or Taxon X (Rehfeldt et al. 1996)). In
sum, 75,083 fascicles from 671 trees in the distribution study (“SCAT”) were
individually examined. The species occurrence data files (.csv) used for SDM
contained a combination of geographic coordinates (WGS 1984) with either

mean needle number or morphotype, depending on the model.

36

Environmental data

The explanatory variables used in the species distribution modeling were
derived from online sources. Digital elevation data were downloaded from the
National Elevation Dataset (“NED,” 1/3 arc-second, or ~10-m resolution; USGS
2004). Annual and monthly average minimum temperature, maximum
temperature, and precipitation data (30 arc-second, or 800-m resolution) for
1971-2000 were downloaded from PRISM (2006). The data were projected to a
geographic coordinate system using the WGS 1984 datum in ArcCatalog (ESRI
2006). I imported the raster coverages into ArcView (ESRI 2006) as grids,
clipped the grids to a rectangular polygon surrounding the Santa Catalina
Mountains (32.6181481469°N, -110.984166666°W to -110.564629628°N,
32.2984259247°W) using Hawth’s Analysis Tools (Beyer 2006), and then
converted the clipped grids into ascii format in ArcCatalog (ESRI 2006). Both
NED (USGS 2004) and PRISM (2006) data are globally available and commonly

used in the SDM literature.

Modeling algorithm

The SDM software called Maxent (Version 2.3, Phillips et al. 2006b) was
the only method selected to focus less on modeling techniques and more on the
ecophysiological relevance of the output. Maxent is proven useful in
conservation planning, easy to use, freely downloadable from the internet, and
successful at predicting species distributions in comparative studies (eg. Elith et

al. 2006), even with small (<25) sample sizes (Hernandez et al. 2006). In

37

addition, Maxent does not explicitly require absence data, incorporates
interactions between variables, minimizes model overfitting through
regularization, and produces spatially continuous output for comparative
interpretation (Phillips et al. 2006a). Furthermore, model complexity can be
controlled by selecting from a combination of linear, quadratic, product, binary,
and hinge mathematical functions (“features”). Recommended user-specified
parameters included: 30% of the occurrence data set aside for model validation
(“testing”); maximum iterations = 500; convergence threshold = 105;
regularization multiplier = 1.0; automatically select mathematical features
depending on sample size; and remove duplicate occurrences within an
environmental pixel, except for comparisons (see Results). The major
drawbacks are its use of an exponential distribution, which is inherently
unbounded above, and its recent development in terms of fully understanding the
implications of regularization, sample size, and spatial extent as well as its
performance when extrapolating to environmental features outside the model’s

calibration (Phillips et al. 2006a).

Model selection

The number of occurrences for a given species affects the predictive
potential for any statistical procedure, including SDM. The asymptote of
maximum predictive power depends not only on the sample size, or number of
occupied pixels across the study region, but also the quality of and heterogeneity

within the explanatory variables, the strength and shape of the species response,

38

and the spatial resolution of the analysis (Hernandez et al. 2006). However, due
to the sampling structure, multiple trees for a given species will share a
geographic coordinate, thus spatially pseudoreplicating that species’ occurrence
at that location. In addition, the useful sample size can be reduced due to the
resolution of the explanatory variables. Any duplicate occurrences for a species
within the 064-ka PRISM pixel would also constitute spatial pseudoreplication
in the modeling algorithm. Consequently, in the SCAT dataset, the number of
occurrences for the 3-needled morphotype decreased from 146 to 31, with
similar reductions for the other morphotypes. This sample size should be
adequate given machine-learning methods, such as Maxent, correctly predict
occurrences around 90% of the asymptote with 10 sample points and near
maximum with 50 data points (Stockwell and Peterson 2002).

Most algorithms allow inclusion of multiple environmental variables to use
as explanatory variables for species distribution modeling. With extensive data
freely available on-line, the modeler is tempted to include as many variables as
the algorithm can tolerate to increase the precision and accuracy of predicting
the spatial extent and probability of species occurrence within the region of study.
However, ecological theory must be part of the model building exercise or else
the output will have little relevance or application (Austin 2002). For example, in
most models and ecology textbooks, species response to environmental
gradients is assumed to be unimodal and symmetric. Unlike earlier algorithms

like GLM, Maxent incorporates a number of mathematical functions (e.g., linear,

39

quadratic, threshold, and hinge) that can be combined into a response to each
variable.

The selection of explanatory variables depends in part on the ultimate
application or hypotheses to be tested by the model. Conservation biologists
interested in parameterizing occupied habitat for projection to potentially suitable
habitat elsewhere will likely utilize a larger suite of environmental variables to
capture and better transfer the multidimensional niche space for the species with
more concern on successful prediction and less concern with the direct
physiological implications of the variables. When the objective is to understand
more about the environmental constraints on the distribution of a species, fewer
but ecophysiologically important variables should be used to reduce interaction
effects. In addition, decreasing the number of spatially and temporally
autocorrelated variables, such as precipitation in July and August (r=0.92 for the
Santa Catalina Mountains; Appendix, Table A-2), to those representing
seasonally important features of the habitat further reduces potentially complex
interaction effects.

In this research, physiologically relevant variables representing the
amount and pattern of precipitation and temperature (PRISM 2006) were used to
parameterize the SDMs because of their demonstrated relation to the distribution
of ponderosa pine across the Rocky Mountains of the American West.
Daubenmire (1943) showed experimentally that soil drought during the summer
is the primary determinant of lower elevation in seedlings, and thus adults, of

Rocky Mountain conifers, including ponderosa pine. Although lower elevationally

40

distributed conifers had a higher tolerance for high summer temperatures, the
ability to access moist soil (i.e., rate of root growth) during summer drought was
most important (Daubenmire 1943). In California, Haller (1959) showed that
lower elevational distribution in ponderosa pine was determined by drought, while
the upper limit was determined by cold temperatures. He also noted that
occupied elevational bands decrease in elevational range with decreasing
latitude due to more rapid altitudinal climb in isohyets than isotherms further
south (Haller 1959), thus the effects of summer drought and cold winter
temperatures should be less spatially distal in the Santa Catalina Mountains.
Independently, Yeaton et al. (1980) showed that summer drought stress limits
the ability of ponderosa pine seedlings to grow below their lower elevation
distributional limit in the Sierra Nevada; he also demonstrated unimodal mortality
along an elevational gradient. Tree-ring data from P. ponderosa var. scopulorum
suggest that summer drought becomes more important with decreasing elevation
(Adams and Kolb 2005). At larger spatial scales, Oliver and Ryker (1990)
surmised that precipitation in May and June were most associated with
ponderosa pine distributions, and Thompson et al. (1999) showed that a
combination of annual precipitation, January and July precipitation, and January
and July temperature could effectively contain the realized niche for ponderosa
pine across the American West. Similarly, Norris et al. (2006) demonstrated the
importance of temperature in January and July, as well as growing season
precipitation, in characterizing the realized niche space for P. ponderosa var.

scopulorum. Further support for the importance of maximum January and July

41

temperature, in addition to the overall solar radiation, in predicting presence of
ponderosa pine growing on granite in the Southwest is provided by Humphries
and Bourgeron (2003).

Consequently, the variables selected for this research in the Santa
Catalina Mountains characterize winter cold and summer drought (Table 2-1).
Models using variables suggested by the literature (“Lit”) focus on the influence
of cold temperatures in restricting upper elevational distributions via the mean
minimum and maximum January temperature variables (PRISM 2006). The
influence of growing season drought, combining warm temperatures and soil
moisture, was encapsulated by mean maximum June temperature (“arid
foresummer”) and mean July precipitation (“monsoon”) (PRISM 2006). Models
using the full set and various combinations of climatic (PRISM 2006) or
topographic (USGS 2004) variables were generated to compare predictive
versus physiological tolerance types of output. Topographic variables were not
combined with climatic variables due to expected correlation between the
variables.

As mentioned earlier, spatial considerations influence the model outcome.
For example, the spatial extent of the species’ range with regard to the
environment can influence the success of the predictive model. Wide-ranging
species will be less associated with certain environmental characteristics and
thus could result in reduced predictive potential (Stockwell and Peterson 2002).
On the other hand, McPherson et al. (2004) conclude that effects of range size

may be statistically artifactual. Although Pinus ponderosa has a large

42

geographic range (Little 1971), its horizontal range within the craggy Santa
Catalina Mountains relative to the scale of the explanatory variables is

considerably smaller.

Table 2-1. Models and environmental variables used in the modeling of species
distributions, or suitable habitat (Phillips et al. 2006a), using Maxent (Phillips et al.
2006b). Climate data are either mean annual or monthly normals for 1971-2000 at 800-
m resolution (PRISM 2006). Topographic data are 10-m resolution (USGS 2004).

 

Model Variables
Full Annual temperature
Minimum January-December temperature
Maximum January-December temperature
Annual precipitation
January- December precipitation
Win.Sum Minimum December, January, February temperature
Maximum June temperature
January, February, July, August precipitation
Lit Minimum January temperature
Maximum January, June temperature
July precipitation
Lit.Thresh Minimum January temperature
Maximum January, June temperature
July precipitation

 

Lit. Red1 Maximum January, July temperature
Lit.Red2 Maximum January, June temperature
NED.Asp Elevation

Aspect

 

The extent of the explanatory variables used in training (calibrating) the
model has received much less attention in the SDM literature. Restricting the
spatial extent of the environmental data to a species’ likely habitat reduces the
number of “pseudo-absent” background points and thus affects the tails of the
response curves (Thuiller et al. 2004). However, higher probabilities are given
for the restricted versus complete environmental data range, thus the complete
range is more conservative. R. Pearson (personal communication) has stated

that the influence of the spatial extent of the explanatory variables on model

43

outcome for different species is currently attracting much attention by SDM

modelers.

Model evaluation

Successful models depend on their objective and two types of statistics -
threshold-dependent and -independent - against which competing models can
be compared. Similar to the Type II" error matrix from hypothesis testing,
predictive models can be evaluated based on their ability to correctly predict
occurrences and absences from real data, whether apportioned from the existing
data set (“test data”) or validated against independent data. Sensitivity and
specificity are successful positive and negative (absent, or “pseudo-absent”)
predictions, respectively (Phillips et al. 2006a). The analogous Type II error is
the omission rate which is the fraction of actual observations (by pixel) predicted
absent, while the commission index measures the fraction of predicted
occurrences where no observation was made (Anderson et al. 2003). A
threshold-dependent evaluation includes comparison of the omission rate and
proportional predicted suitable area at a particular threshold across the models
with a binomial probability calculated to determine if each model is better than
random (Phillips et al. 2006a). At an arbitrarily determined cumulative threshold,
t, t% of resampled pixels will have cumulative probability of tor less, or t%
omission of test localities, and some minimum (correctly) predicted background
area (Engler et al. 2004, Phillips et al. 2006a). Put another way, to measure one

model output’s ability to discriminate between occupied and unoccupied sites

against another model’s output, a threshold of predicted probability of occurrence
must be selected at which sensitivity and specificity will be calculated (Pearce
and Ferrier 2000). A higher decision threshold will have lower sensitivity
(proportion of correct positive predictions) but higher specificity (proportion of
correct negative or absent predictions).

A variety of thresholds can be selected depending on known rarity of the
species and the model application (Pearce and Ferrier 2000, Anderson et al.
2003, Liu et al. 2005). To focus on identifying differences in suitable habitat
between the taxa in the Santa Catalina Mountains, I will use the “minimum
training presence” (Phillips et al. 2006b), or “lowest presence threshold” (Pearson
et al. 2007). This conservative approach will identify pixels where the
environment (based on the explanatory variables) is at least as suitable as where
the species was recorded with zero omission rate (Pearson et al. 2007), and thus
will constrain the potentially suitable niche space for each species.

The threshold-independent approach measures model performance by
plotting the sensitivity against 1-specificity for all possible thresholds and then
calculating the area under the curve (AUC). An AUC value of 1.0 indicates the
model (or Maxent distribution) fits randomly selected recorded presences and
absences perfectly, while an AUC equal to 0.5 indicates that a random model fits
just as well (Phillips et al. 2006a, b). Due to using presence-only data, and thus
Maxent generating pseudo-absences, the AUC must remain less than 1.0
(Phillips et al. 2004). The use of AUC on such plots, called receiver operating

characteristic (ROC) curves, has a long history in medical research, and its

45

significance is approximated by the nonparametric Wilcoxon statistic (Hanley and

McNeil 1982).

Ecological niche space

Ecological niche differentiation was measured by the ability of one
species’ model to predict the occurrence of another species. Species
occurrences were overlain onto the Maxent output (.asc) for each of the species
in ArcView (ESRI 2006), and then Hawth’s Analysis Tools (Beyer 2006) was
used to identify the output pixel’s value (probability) for each occurrence locality.
Summary statistics for each combination were calculated in R (R Development
Core Team 2007). Visualization of occupied ecological niche space was
accomplished by plotting species occurrence points as a function of two-
dimensional climate space (Thompson et al. 1999, Norris et al. 2006). The pixel
value for the environmental variable on which an occurrence was located was

determined as in the previous analysis.

Results
Spatial distribution of morphotypes

The distribution of mean needle number per fascicle across all trees from
the distribution study was bimodal (Figure 2-3). Of these 671 trees, 145 (22%)
were classified as Southwestern ponderosa pine (PIN_PON), 136 (20%) were
classified as mixed-needle (Mixed), 281 (42%) fall into Taxon X (Rehfeldt et al.

1996), and 390 (58%) were classified as Arizona pine (PIN_ARI). Within tree

46

variability in needle production peaked at a mean needle number around 4.0
(Figure 2-4), but 41 PIN_PON and 35 PIN_ARI trees had zero intratree variance
in needle number. lntrannual and interannual variability in needle number was
minimal for PIN_PON and PIN_ARI, while Mixed trees had 1.8-5.4 times the
lntrannual variation as the other morphotypes (Figure 2-5). More needles per
fascicle were produced by Mixed trees in 2003 than either in 1999-2002 or 2004-
2006 (two-sample t—test, p<0.01), and more needles per fascicle were produced

in 2003-2006 than in 1999-2002 (two—sample t-test, p<0.01).

180 a
160 c
140 a
120 <
100 -

80*

Frequency

603

20‘
|
.. Jinn . I
ws»
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.0) 9’. 95109901599999?
4 o: oaxroocoo—smutsurm

 

 

l-

.“ PJ‘PS’ISPP'SHPI
N «cacao-awe);

63

.w
0
Mean needle number per fascicle

Figure 2-3. Frequency of trees (n=671) by mean needle number per fascicle. Data are
from the distribution study (“SCAT”).

47

   

 

 

0

g o °° .

:E: °° co co 9 8

‘ 08« 8 eo°o Q. “0%;

2 ' “game. 0 6%

g : 8 00 0 ;

2 35 ° ‘

s

8 1

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0

0

r:

E; APIN_PON
oMixed
oPlN_ARl

0 + . i i e i
2.5 3.0 3.5 4.0 4.5 5.0 5.5

Mean number of needles per fasclcle

Figure 2-4. Variability in needle number within a tree as a function of its mean needle
number. Hashed lines indicate breaks between one morphotype and another (Peloquin
1984). Forty-one PIN_PON and 35 PIN_ARl have no intratree variance. Data are from
the distribution study (“SCAT")

6 - r 400
" 350
‘t 300

<r 250

N
8
Number of trees

 

Mean needles per fascicle
h

 

 

 

 

1999 2000 2001 2002 2003 2004 2005 2006
Figure 2—5. Mean annual needle number (solid, :80) and sample size (hollow) for the

PIN_PON (A), Mixed (O), and PIN_ARI (El) morphotypes (Peloquin 1984). Data are from
the distribution study ("SCAT”)

48

The Ponderosae morphotypes as described by Peloquin (1984) have
visible differences in their distribution (Figure 2-6). The occurrences of PIN_PON
hug the high-elevation ridgelines, with three visible exceptions - Romero Pass in
the southwest (n=3 trees, average needle number equal to 3.05, mean elevation
2041 m), Catalina Camp in the north (n=4 trees, average needle number equal to
3.00, elevation 1745 m), and near Lizard Rock in the southeast (n=1 tree, needle
number equal to 3.10, 2125 m). These anomalous trees are located either below
or at the lowest extent of the local distribution of PIN_ARI, but they are retained
in the modeling and analysis, unless noted. In general, PIN_ARI were found
along slopes and lower elevation ridges. The spatial distribution of Mixed trees
overlapped both of the other taxa. Factors linked with elevation were positively
and significantly associated with production of fewer needles per fascicle at high
elevation within Mixed, Taxon X, and PIN_ARI, as well as when all occurrences
are combined (Figure 2-7, Table 2-2); however, the correlation for PIN_ARI was

weak (R2=0.04).

49

 

Elevation
2790 m

...... H

      

701 m

Figure 2-6. Distribution of occurrences for the three morphotypes in the region
using the distribution study ("SCAT") data.

50

 

 

9'
or

 

 

 

 

 

 

w 5 a 3 as o

T) o B

'3 a

g. 4.5 7 O

i 4 . 0 Q) °

§ .

C o 9

2 3.5 ‘ 8 o
oo 8 0

E . . °, .

A
c 31AP|N_PON A A A 85 99 “a
E o Illixed
a PIN_ARI
2.5 l l 1 r fir
1 500 1 750 2000 2250 2500 2750 3000

Elevatlon (m)

Figure 2-7. Mean number of needles per fascicle as a function of elevation for each
morphotype. Data are from the distribution study (“SCAT”).

Table 2-2. Summary statistics for regression of mean number of needles per fascicle per
tree by morphotype and combined species against elevation at which the tree was
located. Data are from the distribution study (“SCAT”). All trees within each sampling
plot are included in the regression.

 

Mean Stdev

 

Morphotype N Elevation (m) Elevation (m) R2 P-value
PIN_PON 146 2405.2 214.7 0.02 0.08
Mixed 137 2469.1 207.4 0.26 2.28E-10
Taxon X 283 2438.2 213.0 0.1 1 1.09E-08
PIN_ARI 388 2286.0 143.7 0.04 1.80E-04
All 671 2350.2 191.5 0.19 7.29E-33

 

Ecological distribution of morphotypes

The distributions of morphotypes in climatic space show not only the clear
overlap among the taxa but also the limiting climatic influences. The three
morphotypes occupy the coldest portions of the study region with little apparent

differentiation between distributions (Figure 2-8). The warmest monthly minimum

51

 

 

 

 

 

 

  

 

SE
5 e
5 g .
E a '
>~
fig i
o
E E j
I: , .
a ‘ j
. ‘ ‘
‘ eStudy area 1
2 IP|N_PON
I
6 4 .2 o 2 4
Mean monthly mlnlmum January temperature (’C)
‘33 a .
5 e
5 3 1
E g I
z- n i i
2 g i I
o ‘ I
E b
i 3 t i
C .
5 4 I l
I 4 eStudy area i
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a 4 -2 o 2 4 6
Mean monthly mlnlmum January temperature ('0)
T 7T" * “rib ‘W7# _—"‘1
l l
. I
‘ .
1
. l
E: 3 1
.§ 2 ' '3
x l
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0 i
E 2‘
= 3
g 5 .
" l
. i
eStudy area I
; 2 ePlN_AR| ‘
6 4 2 o 2 4 a

Mean monthly mlnlmum January temperature ('C)

Figure 2-8. Distribution of the three morphotypes in climatic space with regard to
mean monthly minimum and maximum January temperatures. Data are from the
distribution study (“SCAT”).

52

and maximum January temperatures where Mixed and PIN_ARI were found
were —0.6 and 10.4 °C, and -0.6 and 10.3 °C, respectively. If the anomalous 3-
needled trees are removed, then the warmest minimum and maximum January
temperature where PIN_PON were found were —2.0 and 9.5 °C, respectively;
otherwise, these temperatures are -0.8 and 12.3 °C, respectively.

For summer climate space, the three morphotypes were found in the
coolest and wetters portions of the study region (Figure 2-9). Excluding the
anomalous trees, PIN_PON was located in areas with no warmer maximum June
temperature than 26.6 °C, while Mixed and PIN_ARl were found in areas as
warm as 28.6 °C. Although overlapping in the wettest areas of the study region,
Mixed and PIN_ARI were found in drier areas (down to 89.2 mm of July
precipitation) than PIN_PON (down to 96.0 mm of July precipitation). Including
the anomalous PIN_PON trees in these bivariate plots suggests that this taxon

would occupy a larger climatic niche space than the other taxa.

53

 

Mean monthly July preclpltatlon (mm)

 

 

   
   

 

 

 

 

a PIN_PON
40
20 25 :n :5
Mean monthly maximum June temperature ('0)
$10 . 2 V
E
c :
o 5 ; 3
S
n
_>~ .
3. so I
_,, 1
5
C i
° i
5.. i
g 0 Study area 3
a Mixed
40
20 25 :0 35 43
Mean monthly maxlmum June temperature ('C)
1‘0 A ‘ — “ ”i ’ — “i
i 120
g 0
C ‘ .
o ,
3 i
e m i
g ,
2*
5'. so
_>«
5
C
e
E
i 9 Study area
5 - PIN_ARI
40
20 30 40

25 35
Mean monthly maxlmum June temperature ('C)

Figure 2-9. Distribution of the three morphotypes in climatic space with regard to
mean monthly maximum June temperature and monthly July precipitation. Data
are from the distribution study (“SCAT")

54

Model comparison
The Maxent modeling algorithm does not appear to be sensitive to the

spatial extent of calibration (“training”) data (Figure 2-10). The rasters for
environmental explanatory variables of maximum June temperature and annual
precipitation were clipped in either rectangular or irregular polygon shapes
around the Santa Catalina Mountains, in both cases excluding much of the lower
elevation desert and foothills from the modeled area. Models were generated for
each polygon shape using the PIN_PON data, excluding duplicate occurrences
in the same pixel, from the distribution study (“SCAT”). Based on both threshold—
dependent and -independent statistics, models generated from the rectangular-
shaped environmental data performed better than the polygon-shaped data
(Table 2-3). Consequently, all further models were based on environmental data

conforming to the former shape.

Table 2-3. Comparison of influence of spatial extent by shape of calibration area on
model output. Species data are PIN_PON from SCAT; environmental data are
maximum June temperature and annual precipitation. Threshold-dependent statistics
are based on the lowest presence threshold as described in the Methods: cumulative
threshold (“cum.thr"), fractional predicted area (“fr.prd.ar"), and binomial probability (“P-
value"). The threshold-independent statistics are based on the area under the receiver
operating (ROC) curve (AUC) (see Methods).

 

 

 

Lowest presence threshold AUC from test data
Shape Cum.thr Fr.prd.ar P-value N AUC Std dev
Polygon 3.689 0.198 4.589E-07 9 0.974 0.009
Rectangle 3.656 0.140 3.036E-08 9 0.982 0.006

 

55

 

 

 

 

 

 

N

[:10-10
_10-25
_25-50
_50-100

 

 

 

 

Figure 2-10. Distribution of cumulative probability of suitable habitat for PIN_PON
relative to maximum June temperature and annual precipitation in two spatial
extents (“polygon" and “rectangle") for Maxent model calibration. Data are from
the distribution study (“SCAT")

56

Fourteen models using the Maxent algorithm were generated for
comparison for each morphotype, with all but one model being highly statistically
significant (Table 2-4). Incorporating the occurrence data from finer spatial
scaled studies with the distribution data (“SCAT”) for the modeling often
increased the significance of the model but generally decreased the AUC, or
ability of the model to maximize both sensitivity and specificity, probably due to
increasing the overlap of taxa in the same environmental pixel across steep
transition zones (e.g., south slope of Mount Lemmon, Epperson et al. (2001)).
Removing duplicate occurrences within the same environmental pixel, or spatial
pseudoreplication, generally decreased the model’s ability to fit the data but also
decreases the likelihood of model overfitting. Understandably, the models with
the highest AUC were generated with the topographic data at 10-m resolution, in

comparison to the 800-m resolution for the environmental data.

57

 

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59

Excluding climate-based models with spatial pseudoreplication, the
simplest models in terms of number of variables were the most significant and
had highest AUC values (Table 2-4). When available in the suite of explanatory
variables, maximum June and January temperatures had the highest coefficients
and individually contributed most to the models. As the number of variables were
reduced, maximum June temperature became the most important for PIN_PON,
while maximum January temperature became the most important for Mixed and
PIN_ARI. Monsoonal precipitation was consistently less important than
maximum June and winter temperatures in predicting suitable habitat for all three
morphotypes. Although statistically very close, models using only maximum
January and June (or July) temperatures had highest success in correctly
predicting the test data.

Two models from each morphotype were more closely analyzed for
differences in predicted suitable habitat, or niche differentiation, between the
taxa. The Lit model, whose output in Figure 2-11 is shown with large class
breaks to highlight coarse patterns, demonstrated marked differences between
morphotypes. Suitable habitat (>50% cumulative probability) for PIN_PON was
restricted to 1728 ha in the two high-elevation regions within the Santa Catalina
Mountains: Mount Lemmon and Mount Bigelow/Kellogg complex. Suitable
habitat for PIN_ARI included 3456 ha and was located on the west and south
slopes of Mount Lemmon and Mount Bigelow/Kellogg complex, and southeast of
Palisade Ranger Station. Suitable habitat for Mixed included 3008 ha,

overlapped that for PIN_PON, and overlapped the southeastern portion of

60

 

 

. PIN_PON

 

 

 

 

 

  

 

 

Mixed
N
[:1 0-10
10-25 J.
- 25 - 50
- 50 - 100 PIN_ARI

 

Figure 2-11. Distribution of cumulative probability of suitable habitat for the three
morphotypes in the region for the Lit model by Maxent. Data are from the
distribution study (“SCAT").

61

PIN_ARl’s predicted distribution. Area estimates for finer class breaks are

shown in Table 2-5.

Table 2-5. Climatically suitable area estimates for the three morphotypes based on
cumulative probabilities from the Lit and Lit.Thresh models.

 

 

50-75% 75-100%

Morphotype Model Hectares Acres Hectares Acres
PIN_PON Lit 1 152 2847 576 1423

Lit.Thresh 0 0 21 12 5219
Mixed Lit 1728 4270 1280 3163

Lit.Thresh 2304 5693 2368 5851
PIN_ARl Lit 2112 5219 1344 3321

Lit.Thresh 2816 6958 3008 7433

 

The second set of models used the same data but were limited to the
threshold (“Lit.Thresh”) or binary mathematical function in the Maxent algorithm
to identify climatic thresholds above or below which suitable habitat would not be
predicted. These models had lower significance and/or similar AUC than the Lit
models. The spatial extent of suitable habitat was approximately the same for
PIN_PON but larger for Mixed and PIN_ARI as compared to using the full suite of
mathematical functions in the Lit model, but the overlap of the other taxa’s niche
space by Mixed is much more apparent in this set of models (Figure 212).
Although there was little differentiation in threshold climatic values between
PIN_ARI and Mixed, the latter and PIN_PON are clearly separated by minimum
January temperature (A=8.1 °C) and possibly even July precipitation (A=1.6 mm)

(Table 2-6).

62

 

  

. PIN_PON

 

 

 

 

 

Mixed

 

 

N

[3040
_10-25 1‘
_25-50 1

- 50 - 100 PIN_ARI

  

 

 

 

Figure 2—12. Distribution of cumulative probability of suitable habitat for the three
morphotypes in the region for the Lit.Threshold model by Maxent. Data are from
the distribution study ("SCAT").

63

Table 2-6. Thresholds for the three morphotypes based on the Lit.Thresh model. No
coefficients were generated for minimum January temperature for the PIN_ARI model.
Data are from the distribution study (“SCAT”).

 

Minimum January Maximum January Maximum June July precipitation

 

 

Morphotype temperature (°C) temperature (°C) temperature (°C) (mm)
PIN_PON -20.1 10.6 26.6 11.0
Mixed -12.0 10.2 28.3 9.4
PIN_ARI n/a 10.2 28.1 9.3
Model prediction

Based on the ability of one species’ model to correctly predict occurrences
of each of the taxa, there is clear ecological differentiation between PIN_PON
and PIN_ARI, but Mixed appears to be an ecological intermediate (Table 2-7). In
all cases, the Lit.Thresh models were more likely to predict occurrences than
were the Lit models. Although the Mixed and PIN_ARl models predicted their
own species’ occurrences more than the other’s species, the PIN_PON models
were more likely to predict Mixed than PIN_PON occurrences. All of the models

had similar ability to predict PIN_ARI occurrences.

Table 2-7. Ecological similarity matrix based on the ability of one taxon’s model
(columns) to predict the documented occurrences of each of the taxa (rows). Values are
mean probabilities of pixels wherein an occurrence was documented for the given
model; duplicate locations by species are excluded. Data are from the distribution study
(“SCAT”).

 

 

 

PIN_PON Mixed PIN_ARl

Model Lit Lit.thresh Lit Lit.thresh Lit Lit.thresh
PIN_PON 0.64 0.80 0.73 0.86 0.53 0.73
Mixed 0.50 0.64 0.62 0.75 0.53 0.71
PIN_ARl 0.33 0.51 0.49 0.63 0.59 0.70

 

Discussion

The data and analyses presented in this paper give biogeographic and
ecological evidence for possible hybridization between the 3-needled
Southwestern Rocky Mountain ponderosa pine (PIN_PON) and the 5-needled
Arizona pine (PIN_ARI). Close to 90% of the trees sampled in the distribution
study produce fascicles intermediate to pure (sz=0.0) PIN_PON and PIN_ARl.
While PIN_PON generally occupied a cooler and moister climatic niche, the
same bivariate plots showed mixed-needle (Mixed) trees overlapping PIN_ARI.
The Maxent models demonstrated the limiting influence of winter temperature on
Mixed and PIN_ARI, while PIN_PON was more limited by temperature during the
arid foresummer. Furthermore, PIN_PON appears to tolerate areas with an 8° C
colder minimum January temperature than the Mixed trees. However, because
of the broader distribution for the Mixed trees, suitable habitat predicted for this
taxon overlapped the other species. The interpretation for these results must be
tempered by a number of factors, including quality of data entering the models,

model assumptions and selection, and ecophysiological considerations.

Data quality

The original intent for collecting species occurrence data was to
characterize the distribution for the three taxa, and thus a particular subjective
sampling strategy was employed. It was understood that their distributions
varied across elevation (Dodge 1963, Epperson et al. 2001), but that elevation

was not the sole factor determining location for each taxon (T elewski

65

unpublished data). For example, in March 2000, Telewski (unpublished data)
located a tree producing mean 3.02 needles per fascicle at low elevation (~1800
m) near Bear Canyon, while the rest of the trees were mixed and 5-needled.
Within my distribution study, I located 8 (“anomalous”) trees that also
unexpectedly produced less than mean 3.2 needles per fascicle: Romero Pass,
Catalina Camp, and near Lizard Rock. Interestingly, the climate at these lower
elevation sites could be moderated by cold-air drainage patterns (Adams 2007):
Mule Ears is adjacent to a northerly exposed pass through Samaniego Ridge;
Romero Pass is located at the origin to a large canyon draining northwest to
Romero Canyon and southeast to Upper Sabino Canyon; Catalina Camp is
situated in the valley between two prominent ridges extending north from the
main Santa Catalina ridgeline; and the Lizard Rock site is situated in a low-lying
pocket with a substantial watercourse. These disjunct PIN_PON trees could
simply be remnants of a once larger population at these elevations, surviving but
lacking a suitable regeneration niche. The Maxent models included these and
potentially other disjunct low—elevation trees, thus interpretations of the observed
differences between distributions of the taxa are strengthened.

In retrospect, the sampling strategy did not prove to be efficient for
characterizing and modeling the distribution of the taxa. All of the trees to be
sampled were selected by various, usually subjective, means. For example, at
Palisade Rock, PIN_PON and PIN_ARl were selected as pairs in close proximity
for ecophysiological studies. Along the south and north face of Mount Lemmon,

as well as for Kimball Peak Saddle, Bear Canyon, and Wilderness of Rocks,

66

trees were selected either for accessibility, reproductive status, or systematically
for spatial analyses (Epperson et al. 2001, 2003). Each of these trees had a
unique set of geographic coordinates.

In an effort to maximize efficiency in species data collection, up to four
trees per point-location were sampled for the distribution study. These points
were located within centers and expected edges (Telewski unpublished data) of
each taxon’s distribution. At each predetermined point-location, four trees from a
point-centered quarter could be represented by one to three taxa, which results
in spatial pseudoreplication for this point-location. This error is compounded by
the fact that multiple sampling points were located within a given pixel of
environmental data, thus removing such duplication due to relatively low
resolution of the explanatory variables resulted in drastic reduction of the sample
sizes for each taxon in the modeling. For example, including duplicate samples
in the modeling consistently increased the predictive performance of the models
and likely produced overfitted models. Furthermore, I believe that performance
statistics were consistently lower for models using all of the occurrence data
(SCAT and non-SCAT), even with larger sample sizes, as compared to just the
distribution study data (Table 2-4) because the former includes data from the
very steep transition zones of PIN_PON to PIN_ARI on Mount Lemmon
(Epperson et al. 2001) and Palisade Rock (Kilgore unpublished data), yet each
case occurs within a single environmental pixel. The resultant smaller sample
sizes, even with 30% of the occurrences allocated to model verification, are not

expected to have had a significant effect on the model output (Stockwell and

67

Peterson 2002, Engler et al. 2004, and Pearson et al. 2007). Overall, the
distribution study data were more dispersed, thus allowing better discrimination
between their distributions across the field of environmental data.

Additional bias in the sampling strategies was introduced clue to
geography and two major disturbance events. The Santa Catalina Mountains are
treacherously rugged but have a major paved road to the summit as well as
extensive hiking trails. All of the sampled trees were close to primary and
secondary roads or hiking trails. With the exception of the Front Range and the
north slope of the Mount Bigelow-Kellogg ridge, most of the potential habitat for
the three taxa was sampled, relative to the dimensions of the environmental
pixels. However, the Bullock Fire (2002) extensively burned the forests on the
north slope, while the Aspen Fire (2003) burned through most of the remainder of
the forests in the Santa Catalina Mountains, except for the extreme southwest
side of the Front Range (CNF 2002, 2003). Consequently, in most areas
(especially Samaniego Ridge, north slope of Mount Lemmon, Wilderness of
Rocks, Box Camp Ridge, and north of Mount Bigelow), live or dead trees with
accessible needles were sometimes difficult or impossible to locate. Distribution
data for some of these areas are functionally lost but can be inferred from the
habitat suitability models.

The parametric assumption of randomly sampling the entire study region
for distribution modeling is unrealistic and unfeasible. Hirzel and Guisan (2002)
found that random sampling was the least accurate and robust method as

compared to regular and environmentally-informed systematic sampling in

68

correctly predicting presences of a virtual species. Instead, Araujo and Guisan
(2006) argue that a model-based environmental stratification approach be used
to direct additional sampling, assuming that the abiotic range of the taxon has
been preliminarily sampled. Increasing sampling-effort at the extremes of a
range, or tails of the unimodal species response curve, increases the accuracy in
estimating species distributions (Mohler 1983). Fortuitously, this was the
approach taken for the distribution study, given the limitations of accessibility and
living forest. In the case of the Santa Catalina Mountains, the model outputs
suggest that sampling effort be focused on the Front Range where suitable
habitat for all three morphotypes is predicted (Figures 2-11 and 2-12).

Although the Maxent model output provided additional support to the
literature for the limiting influence of summer drought and winter cold on the
distribution of ponderosa pine, the relatively large resolution of the environmental
data restricted the full use of species data, as explained earlier. As expected,
models using the 10-m resolution topographic data produced much finer scaled
predictions (Figure 2-13), but a species’ association to elevation in the Santa
Catalina Mountains is not transferable outside this system (Phillips et al. 2006).
Instead, climate constrains the macroscale distribution of species (Woodward
1987), reducing historical contingencies, and associations with climate can be
projected to another system. Furthermore, the objective here is to determine if
any difference exists between species in their response to limiting climatic

factors.

69

 

 

PIN_PON

 

 

 

 

 

 

Mixed
N
C] 0 - 10
- 10-25
- 25 - 50 h’
- 50 - 100 PIN_ARI

 

 

 

Figure 2-13. Distribution of cumulative probability of suitable habitat for the three
morphotypes in the region for the NED.Asp model by Maxent. Data are from the
distribution study (“SCAT”).

70

Unfortunately, the highest resolution climatic data available for the study region is
30—arcsec, or 800-m, resolution (PRISM 2006). These continuous data

were generated by interpolating climatic data from monitoring stations using an
algorithm developed for mountainous terrain: Parameter-elevation Regressions
on Independent Slopes Model (PRISM 2006). While taking into account spatial
scale and pattern of orographic processes using a digital elevation model
(PRISM 2006), which should detect topographically depressed drainageways,
the climatic data are scaled up to BOO-m resolution and thereby lose much of that
information. Also, the accuracy of the resultant gridded climatic data is limited to
the actual data collected. In the Santa Catalina Mountains, an on-Iine
clearinghouse (Western Regional Climate Center, URL:
www.wrcc.dri.edu/summary/Climsmaz.html) lists climate data for only two
locations: Palisade Ranger Station (2426 m elevation) for 1965—1981; and Mount
Lemmon (2344-2374 m elevation) for 1950-1956 and 1958-1996. Additional
“unofficial” data from Mount Lemmon Ski Valley (A. Corona, National Oceanic
and Atmospheric Administration, Tucson, personal communication) and Green
Mountain (W. Hart, CNF, personal communication) exist but were not likely used
by PRISM (2006). Consequently, the accuracy of the explanatory variables used
in the modeling is determined by a combination of interpolation of limited climatic

data and orographic extrapolation in the PRISM (2006) algorithm.

71

Model assumptions and selection

A fundamental assumption of species distribution models (SDMs) is that
they accurately approximate the species’ ecological niche from known
occurrences and their association with particular values of climatic variables. In
general, ecological niches are highly conserved, at least since the last glacial
maximum (Peterson et al. 1999, Martinez-Meyer and Peterson 2006), so
projecting the models to other locations should be informative. The output to
such correlative models is a map of potential suitable habitat and, in fact, not
potential geographic distribution because the models do not take into account
spatially explicit features such as dispersal (Araujo and Guisan 2006). Other
factors such as competition, historical contingency, and human fragmentation of
populations and habitat can also alter the potential geographic distribution for a
species. For example, Brown et al. (1996) suggest that species are limited along
a key environmental gradient by abiotic stresses at one extreme and biotic
stresses at the other extreme. Vetaas (2002) supported this theory with cold
temperatures as an absolute boundary, while a mixture of competition and warm
temperature forms the other boundary in Himalayan Rhododendron species.
However, the Maxent models suggested that primarily warm temperatures during
a particularly dry period (arid foresummer) for PIN_PON and cold temperatures
during winter for PIN_ARI explain a sufficient amount of the variability in each
taxon’s distribution to geographically segregate their distribution based on
potential suitable habitat. Consequently, competition between these taxa may

occur but does not need to be included in the models to differentiate their

72

ecological niches. Like PIN_ARI, the upper elevation distribution of the Mixed
trees appears to be limited by winter temperatures, but its distribution overlaps
both taxa. If the Mixed trees represent hybridization between PIN_PON and
PIN_ARI (Peloquin 1984) and if hybrid trees possess “new adaptive systems” as
suggested by Anderson and Stebbins (1954), then the Mixed trees in this system
may have absorbed adaptive features such as greater cold tolerance from
PIN_PON and higher heat tolerance from PIN_ARI. Competition between for
limited resources during establishment of these sympatric taxa and their offspring
is therefore expected.

Input data can be processed to remove areas where a species cannot
occur for one reason or another despite climatic suitability, but, in general,
Maxent models approximate Hutchinson’s (1957) realized niche on a coarse
climatic scale (Phillips et al. 2006). As a consequence, Maxent and other SDMs
will predict suitable habitat larger than a species’ actual distribution (Phillips et al.
2006). Species are often absent from suitable habitat, and vice versa (Pulliam
2000). However, predicting larger potential ranges proves useful in targeting
future sampling efforts and identifying potential restoration locations.

A second fundamental assumption of SDMs is that modeled populations
or species are in equilibrium with their environment (Guisan and Thuiller 2005).
Alternatively, modelers are interested in how distant from equilibrium are current
distributions (Araujo and Pearson 2005). Natural dispersal ability, landscape
barriers, human fragmentation of habitat, and species interactions are factors

that can restrict the ability of a species to track climate change (Davis et al. 1998,

73

Pearson and Dawson 2003). For example, Leathwick (1998) found that the
distribution of disjunct Nothofagus species in New Zealand corresponds more to
suboptimal habitat due to slow dispersal ability. Due to producing wind-borne
pollen and winged seeds, ponderosa pine can disperse relatively long distances
(Conkle and Critchfield 1988, Epperson et al. 2001). However, trees greater than
1.5 m in height were selected for the distribution study, which may bias the
climatic interpretation of the model output toward conditions present at the time
of their establishment rather than current conditions. Ponderosa pine are long-
lived trees; for example, most of the trees cored on Green Mountain were around
300 years old, but one dated back to 1460 (Graybill 1986). On the other hand,
dendrochronologists usually seek out the oldest trees for extending chronologies,
while the nearest tree to the point-center was selected for this study. The trees
sampled for the distribution study (n=671, mean diameter at breast height = 34.4
cm, range = 0.9-399.0 cm, standard deviation = 22.4 cm) more likely represent
climatic conditions from the last 20-250 years. Given the 2° C rise in mean
temperature over the last century in this region (USEPA 1998), which translates
to an elevational shift of 267 m (Shreve 1915), focusing on presence and
taxonomy of young trees would bring the model predictions closer to equilibrium
with current climatic conditions.

The Santa Catalina Mountains present numerous physical challenges to
surmount for non-vagile organisms to migrate in response to climate change.
The steep canyon walls, xeric southern exposures, and shallow lithosols reduce

the ability for seeds to disperse and establish higher in elevation. Conversely,

74

the deeply etched valleys that bear floodwaters from higher elevations provide
mesic refugia, sometimes from fire as well, for trees left behind in the march
upward. Since larger surviving individuals produce a disproportionately large
number of pistillate cones, these disjunct trees receive pollen from the invading
lower elevation pines which produce pollen in all mature age classes (Conkle and
Critchfield 1988). Assuming vigorous offspring, the stage for hybridization is
prepared in part due to the landscape structure, whose complex topography
allows for disequilibrium in distribution at the landscape scale.

Nevertheless, at the available spatial resolution, certain climatic variables
consistently gained larger coefficients than other variables across the variety of
models for each species. Surprisingly, precipitation variables were always at
least secondary in importance to either summer (all taxa) or winter (PIN_ARI and
Mixed) temperature. Use and examination of the most parsimonious models (Lit
and Lit.Thresh) supported by physiological tolerances indicated in the literature
appears to be appropriate for these taxa. Winter temperature was likely not
limiting to PIN_PON because little of the study region existed above the upper
elevation distribution for this taxon; of the Southwestern mountain islands, only
the Pinalenos and Chiricahua Mountains have substantial habitat above the
ponderosa pine zone. Because of the bimodal distribution of precipitation in the
Southwest, early summer (June) is the most xeric period of the growing season.
Spring-germinating seeds are thus quickly exposed to drought conditions, an
interaction of high temperature and low soil moisture. Consequently, cold winter

temperatures constrain upward migration of Mixed and PIN_ARI and warm

75

temperatures prevent establishment of PIN_PON at its lower elevation
distribution. These hypotheses could easily be tested by establishing Climatically
monitored common gardens at different elevations (sensu Clausen et al. 1940).
Exclusive use of the binary, or threshold, mathematical function
(Lit.Thresh model) for detecting differences in cutoffs in species distribution
appears to be unique in this study. Physiological tolerance for variation in a
climatic variable is not expected to be binary, in fact the response is typically
close to unimodal, but forcing the coefficients in the Maxent algorithm into binary
mode seems to have allowed quantification of ecological niche differentiation
between taxa with overlapping distributions. In this study, suitable (or at least
occupied) habitat differed between PIN_PON and the other taxa by -8.1° C in
January, -1.5° C in June, and 1.6 mm precipitation in July (Table 2-6), implying
that cold temperatures in winter restrict PIN_ARI and Mixed from PIN_PON
habitat, and that the warm arid foresummer restricts PIN_PON from growing in
PIN_ARI and Mixed habitat. These conclusions are supported by the other
models, but now a quantified difference can be related to other studies examining
ecophysiological differences between the taxa and incorporated into models

examining the effects of climate change on species distribution.

Distribution and ecological differentiation
The spatial distribution of occurrences (Figure 2-6), climatic niche space
(Figures 2-8 and 2-9), and predicted suitable habitat maps (Figures 2-11 through

2-13) are all based on a single morphologic character (Peloquin 1984) with high

76

heritability (~0.6, Rehfeldt et al. 1996): mean needles per fascicle. In addition,
elevation (Dodge 1963) and previous year’s July-August precipitation (Haller
1965) are correlated to the number of needles per fascicle in trees producing
variable numbers of needles. Despite these confounding factors, the separation
of distribution and ecological niche space is apparent for those trees classified as
PIN_PON and PIN_ARI, even when the intratree variation in mean needle
number (Figure 2-4) is relatively high. Ecophysiologic results support the
ecological differentiation between PIN_PON and PIN_ARI. Dodge (1963)
monitored radial expansion and reported tree-ring characteristics from sympatric
PIN_PON and PIN_ARI trees near Mount Bigelow. Although both taxa ceased
radial expansion simultaneously, PIN_ARI lagged initiation by 6 weeks yet
produced significantly larger ring-widths over time. Furthermore, PIN_PON
chronologies had higher autocorrelation (0.65 versus 0.24), and PIN_PON
produced twice as many intra-annual rings, which was interpreted to be a genetic
expression of adaptation to more consistent precipitation patterns during the
growing season typical of the Rocky Mountains (Dodge 1963) or greater
sensitivity to drought at the lower edge of its distribution (Fritts 1974). Besides
needle number, Peloquin (1971) also noted significant differences between
PIN_PON and PIN_ARl in number of resin canals in the needles and
monoterpene ratios from sap. Morphologic, phenologic, and growth responses
are clearly differentiated between PIN_PON and PIN_ARI.

The biogeographic and phylogenetic quandary are the trees classified as

Mixed, or those producing 3.2 to 4.6 needles per fascicle (Peloquin 1984).

77

Dodge (1963) found a needle cline, implying mixed-needle trees, in the Santa
Catalina, Rincon, Chiricahua, Huachuca, and Santa Rita Mountains, but not in
the nearby Galiuro or Pinaleno Mountains, which had only 3—needled trees.
Based on community associations, Lowe (1961) describes the more southern
mountains (Chiricahua, Huachuca, and Santa Rita Mountains) as outlying Sierra
Madrean systems, while the other more northern mountains are extensions of the
Rocky Mountains. Based on the distribution of needle and cone morphologies,
Dodge (1963) concluded that the Santa Catalina and Rincon Mountains express
the junction of the Rocky Mountain — Sierra Madrean flora more than the other
mountains. Consequently, these mountains will have the highest likelihood for
populations of Southwestern ponderosa and Arizona pines to interact and
produce the needle number cllne (Dodge 1963). From monoterpenoid analysis,
as well as needle and cone morphologies, Peloquin (1971) concluded that trees
producing mixed-needled fascicles, even those sampled below Mount Bigelow,
were F1 and advanced hybrids between Southwestern ponderosa and Arizona
pines. Dodge (1963) reported that dendrochronological characteristics for Mixed
trees at the Mount Bigelow site were intermediate but more similar to PIN_PON
than to PIN_ARI. F urtherrnore, Conkle and Critchfield (1988) point out the
moderate crossability of Ponderosae, in general, and especially across species
that shared glacial refugia. Supporting Peloquin’s (1971) results with a common
garden approach, Rehfeldt (1993) concluded that introgression between the
Ponderosae in southern Arizona is common. However, Rehfeldt partially

recanted with the development of a novel Taxon X (Rehfeldt et al. 1996) and

78

ascribed the geographic cline in needle number to within population genetic
variability (Rehfeldt 1999). In the Santa Catalina Mountains, spatial structuring of
needle number could indicate hybridization (Epperson et al. 2001), while the lack
of allozyme structure and differentiation support either advanced introgression or
insufficient time since isolation for differentiation to have occurred (Epperson et
al. 2003). Unpublished chNA data (Epperson et al.) indicate either
unidirectional cytoplasmic hybridization (Watano et al. 1995) between Arizona
and Southwestern ponderosa pines or genetic barriers to unlimited pollen flow
between Arizona pine and Taxon X.

The hybridization hypothesis is supported by the results of this study.
Despite arbitrary divisions between continuous needle numbers, the spatial
extent of predicted suitable habitat for Mixed trees was similar at high elevation
but visibly extended lower in elevation than suitable habitat for PIN_PON
(Figures 2-11 through 213). Although Rehfeldt (1999) aligned the Mixed trees
with PIN_PON, they in fact appear to be more climatically associated with
PIN_ARI than PIN_PON (Table 2-6). Therefore, PIN_PON and Mixed do not
share climatic tolerances, despite their sympatry. Given the results of prior work
with these taxa (Dodge 1963, Peloquin 1971, Conkle and Critchfield 1988,
Rehfeldt 1993, and Epperson et al. 2001), as well as similar patterns of
hybridization in other Pinus (e.g., Watano et al. 1996), and the biogeographic
evidence provided in this study, I deduce that the Mixed trees represent
hybridization between PIN_PON and PIN_ARI (sensu Anderson and Stebbins

1954). Modeling the spatial variability of characteristics distinguishing PIN_PON

79

and PIN_ARI, such as monoterpenoid ratios and genetic markers (e.g.,
cp/mtlnuclear DNA), would lead to greater understanding of the historical
progression of migration and putative introgression of the Ponderosae in the

Santa Catalina Mountains.

Projection

A natural extension of SDMs is to spatially or temporally project the
habitat, or climate, suitability model derived for each species (Guisan and Thuiller
2005, Hijmans and Graham 2006). Spatial projection to other mountain islands
in the Southwest would identify areas to sample for not only validating the model
but also conservation purposes. Although the Galiuro and Pinaleno Mountains
are close to the Santa Catalina Mountains, Dodge (1963) did not find any
PIN_ARI despite seemingly suitable habitat. Projection of the models derived in
the Santa Catalina Mountains to these other systems can identify climatically
limiting conditions and contribute to the understanding of biogeographic patterns.
Conversely, such projection would highlight the importance of other factors, such
as historical contingency in the lack of migration during the last glacial maximum
to these systems from the Sierra Madrean PIN_ARI. Projecting to a
geographically proximal and climatically similar but independent location could
improve the model through validation and subsequent fine-tuning of variable
selection and parameterization. This process could also be used to assess the
ability of the model to project to different climate scenarios (Guisan and Thuiller

2005). In both cases, biotic interactions may differ and thus the projected

80

“realized niche” may vary depending on the parameterization of the original
model. Since these models do not currently incorporate spatially explicit
processes like dispersal, their output is potential suitable habitat, not potential
geographic distribution. However, in comparison to a mechanistic model and
other niche modeling algorithms, Maxent consistently performs well in predicting
current, past, and future ranges in response to climate change (Hijmans and
Graham 2006). Such an approach would be useful in predicting the potential
response by taxa isolated on mountain islands to climate change, as well as

identifying potential habitat for climatically induced plant migration.

81

CHAPTER 3

INFLUENCE OF SEED GERMINATION ECOLOGY ON THE DISTRIBUTION
OF PONDEROSAE IN THE SANTA CATALINA MOUNTAINS OF ARIZONA

Introduction

The realized distribution for any species is determined by the
establishment of seedlings, thus germination and survival through establishment
are critical life history stages (Bazzaz 1979). Steep environmental gradients,
such as those found in montane systems, present particular challenges for seeds
dispersed beyond a relatively narrow bioclimatic window. At higher elevation,
seeds are faced with more mesic conditions, such as colder air and soil
temperatures, higher litter depth, higher soil moisture, and lower light due to
shading from the more productive canopy (Whittaker and Niering 1975, Barton
1993). Seeds dispersed below the current range are faced with more xeric
conditions than those of its seed-producing parent. In addition, biotic
interactions, such as competition from shallow-rooted forbs. and disturbance
regime (e.g., fire frequency) different from a seed-tree’s locally adapted habitat
could affect eventual establishment. On the contrary, dispersal and
establishment outside of a species’ current distribution could suggest that the
species is not in equilibrium with its environment. However, due to restricted
species niches in mountains, potential species migration here is sensitive to
variations and change in climate (Tranquillini 1979), and thus the germination

ecology for a species will influence the outcome of potential species migration.

82

Most montane tree seeds exhibit some sort of physiological dormancy,
likely selected to avoid germination at a time with unfavorable environmental
conditions for seedling establishment (Baskin and Baskin 1998). This dormancy
is normally manifested as a chilling requirement, or cold stratification, before
seed germination occurs. In some cases, such as the Carices (Schutze and
Rave 1999), germination occurs regardless of stratification but is higher with
colder stratification. Given that most seeds mature and disperse by the end of a
growing season (Young and Young 1992, Baskin and Baskin 1998), a chilling
requirement in cold, or higher elevation, conditions should increase the likelihood
of seedling establishment in the subsequent growing season(s). Even after a
year’s growth, though, disruption of the delicate root system of the seedling
through frost-heaving can lead to high mortality (Heidmann 1987).

The timing of germination during the growing season relative to local
climate conditions, as well as irregular disturbances such as fire or logging, can
also influence germination and establishment. For example, Larson (1961, as
referenced by Howard 2003b) noted that Southwestern ponderosa pine seedling
mortality is highest in regions with a bimodal precipitation pattern, which is
characterized by winter precipitation, an arid foresummer drought (May-June),
summer monsoon precipitation (July-August), and a moderate fall drought
(Sheppard et al. 2002). Barton (1993) showed that the early summer drought
limits pine seedling establishment lower in elevation, as higher elevation species
germinated but did not survive the first year. In contrast, lower elevation pines

germinated and survived for at least two years above their distributional limit

83

(Barton 1993). The fall drought also affects ponderosa pine seedling mortality
(Jones 1967, Larson and Schubert 1970, cited in Howard 2003b). In Arizona,
Schmid and Mitchell (1986, cited in Howard 2003b) measured highest first-year
survivorship (13-19%) by seedlings that germinated during the early part of the
monsoon, with lower (2-7%) survivorship by those germinating later in August.
Like the climatic niche, the temporal window for germination and successful
establishment confines opportunities for species migration.

The closely related Ponderosae are roughly segregated, but sympatric in
places, by elevation and factors contributing to more or less xeric conditions in
the Santa Catalina Mountains of southern Arizona. Southwestern ponderosa
pine (Pinus ponderosa var. scopulorum Engelmannii) co-occurs with Arizona
corkbark fir (Abies Iasiocarpa var. arizonica) at high elevation (>2650 m) and with
Douglas-fir (Pseudotsuga menziesii Franco) and Southwestern white pine (Pinus
strobifonnis) throughout most of its elevational distribution (Whittaker and Niering
1965). At its steep (Epperson et al. 2001) xeric boundary (~2430-2530 m
elevation), Southwestern ponderosa pine (“PIN_PON”) transitions to Arizona pine
(Pinus arizonica Engelmannii), which extends down to ~1760 m elevation. This
narrow boundary suggests that abiotic conditions may be limiting the upward
migration of Arizona pine (“PIN_ARI") and downward migration of PIN_PON.
Since prechilling is required to germinate stored (i.e., over-wintered) seed from
PIN_PON, but notedly not PIN_ARI (Krugman and Jenkinson 1974), the lack of
cold conditions at low elevation could also prevent germination of PIN_PON

seeds. Further, putative hybrids (“Mixed”) between PIN_PON and PIN_ARI have

84

been reported throughout the range of both species as producing an intermediate
number of needles per fascicle (Dodge 1963, Peloquin 1984, Rehfeldt et al.
1996, Epperson et al. 2001). If a product of hybridization, then the ability of this
morphotype to overcome the lower and upper elevational barriers experienced by
the other taxa (Kilgore, unpublished data) suggests that differences in
germination ecophysiology that may exist between the parental taxa may have
been reduced through hybridization.

The principal aim for this study is to determine if there is a genetic
difference in seed gerrninability and response to stratification by PIN_PON and
PIN_ARI, and to relate these expected differences to potential species migration
beyond current distributions. Given the close genetic relatedness of these taxa,
little difference in total germination is expected. Although prechilling is not
necessary for PIN_ARI seed (Young and Young 1992), stratification is expected
to increase germination rate of seeds from both taxa (Baskin and Baskin 1998).
Further, if lower elevation conifers can establish at high elevation, but not
conversely (Barton 1993), then PIN_ARI germinants should have high
survivorship at high elevation, and PIN_PON germinants should have relatively

low survivorship at low elevation.

Methods
Seeds used in these experiments were collected from mature trees in the
Santa Catalina Mountains in September-October 2005. In general, PIN_PON

cones mature and seeds disperse in August-September, while PIN_ARI cone

85

ripening and seed dispersal are delayed by a month (Krugman and Jenkinson
1974). The PIN_PON trees near the summit of Mount Lemmon have been
studied for needle number production for at least 10 years and are part of a
larger research program (Epperson et al. 2001, 2003). Cones from PIN_ARI
were collected from Rose Canyon, an area previously considered to be
composed of “pure” 5-needled trees (F. Telewski, personal communication). The
site effect will be reduced by collecting seed from all three morphotypes at a
transition zone near Palisade Rock; at least maternal genetic contribution will be
determined by known morphotype. Annual variability in seed lots will be
controlled by using seed from one cone production year.

With the aid of a 23-ft pole-pruner, branches containing abundant needles
and multiple ripe cones were harvested from the mid- to upper-crown of each
tree. Even for trees of known species, needles were counted to determine
species designation (Peloquin 1984, Epperson et al. 2001). Cones were air-
dried at room temperature in paper bags, and seeds were removed from the
cones by shaking and removing cone bracts in early January 2006. Unopened
cones were heated 14—27 h at 40° C to encourage opening (Young and Young
1992). Empty, parasitized, or otherwise unhealthy appearing seeds were
discarded, while whole seeds were dewinged by hand, inventoried (Table 3-1),

and stored in labeled small manila envelopes at room temperature.

86

Cold stratification experiment

The controlled seed germination test as a function of cold stratification
treatments was conducted in the laboratory at Michigan State University. The
experimental design was a factorial with three sites, three taxa, and three
stratification periods (0, 15, and 30 days). For each stratification period, an equal
number (n=115) of seeds from 3-8 trees from each site and needle type were

selected based on number of trees and seeds available (Table 3-1).

87

Table 3-1. Seeds inventoried from cones collected from the Santa Catalina Mountains in
September-October 2005. Site refers to south slope of Mount Lemmon (MTL), south
slope between Palisade Rock and Mount Bigelow (PAL), northeast of Lizard Rock (LIZ),
and entrance drive to Rose Canyon Lake (RC). Morphotype refers to producing mean 3-
(PIN_PON), mixed, or 5-needled (PIN_ARI) fascicles (Peloquin 1984). Tree refers to
specific tree sampled as part of other research or specifically for this experiment. The
first number in the last column refers to the number of seeds used from each tree per
cold stratification treatment, while a second number refers to seeds used in the
reciprocal transplant experiment (see Methods).

 

 

 

 

 

 

 

 

 

Number of
Number seeds per
Site Morphotype Tree of seeds treatment
MTL PIN_PON MTL 9 296 15 / 20
MTL 5 388 15 I 20
MTL 24 800 15 / 20
MTL 23 722 15 / 20
MTL 15 31 10
MTL 132 458 15 l 20
MTL 11 2388 15 / 20
MTL 104 282 15
MTL 1.5m behind junction 911 0
MTL junction of MTL+MeadowTrail 481 0
MTL 9.2m@280° from MTL 104 554 0
MTL 20m@210° from 60"dome 360 0
MTL 23m@330° from RadioTowerFence 299 7 0
PAL PIN_PON PAL 1 133 41
PAL 41 53 17
PAL 3 62 20
PAL 12 74 24
PAL 40 ...- 40 13
PAL Mixed PAL 28 270 87
PAL 32 48 16
PAL 30 36 12
PAL PIN_ARI PAL 14 332 30 / 20
PAL 2 104 30 / 10
PAL 10 366 32 l 20
PAL 16 71 23
PAL 49 11 0 / 10
RC PIN_ARI RC PINARI GroupPicnic 29.5cm dbh 214 37/20
RC Tree#15 28 0 l 20
.- RC Tree#16 _. n/a 0 I 20 g
LIZ PIN_ARI LIZ 1 43 14
LIZ 3 17 5
LIZ PINARI 10cm dbh 3 0
LIZ PINARI 353° from LIZ 2 71 23
LIZ PINARI 61.50m dbh 155 36
LIZ PINARI 5 0

 

Seed handling and stratification methods for ponderosa pine vary widely in

the literature (e.g., Stefferud 1961, Smith 1986, Weber and Sorensen 1990,

88

Young and Young 1992, Evans et al. 2001, Wenny and Dumroese 2001,
Dreesen 2003), while little is published on germination characteristics for Arizona
pine (except see Young and Young 1992). In general, seeds are rinsed with a
fungicide, soaked for some period of time to induce imbibition and reduce
germination inhibitors (Evans et al. 2001), placed into media or plastic bag, and
stored (2-8 wk) at a constant cool temperature (0-5° C). Smith (1986) reported
no effect of pregermination fungicides on germination success of ponderosa pine
seeds. A contract grower for the Coronado National Forest and Trees for Mount
Lemmon stores (“cold stratifies”) ponderosa pine seeds from southern Arizona at
1-2° C for variable lengths of time, rinses the seeds with 10% bleach solution for
5 minutes before rinsing and soaking the seeds for 12-24 h, and then
immediately plants seeds into soil with ~85% germination success (B. Blake,
Northern Arizona University, personal communication).

The seed handling methods used in this paper combine elements from the
literature, especially Weber and Sorensen (1990), with practices in use by CNF
growers. In mid-January 2006, seeds from each tree by stratification treatment
(see Table 3-1) were placed into a perforated and labeled 4-mL Whirl-Pak Write-
On Bag (Nasco, Fort Atkinson, Wisconsin, USA); perforation of the plastic bag
walls was accomplished with a wallpaper removing tool to allow water and air
infiltration to the seeds. Approximately 25 h before each stratification treatment,
or sowing for the no stratification treatment, the seeds were surface-sterilized in
10% bleach solution for 30 min, rinsed with distilled water, and soaked in a

running distilled water bath at room temperature for 24 h. The seeds were air-

89

dried on individually labeled paper towels, while the bags were oven—dried at 40°
C. The dry seeds were returned to their appropriate bags, which were pooled
into a 1-gal Zip-Loo (SC Johnson, Racine, WI, USA) bag containing 1 L perlite
moistened by 200 mL distilled water. The Zip-Loc bag containing the Whirl-Pak
bags of seeds was stored in a standard refrigerator kept at 2-4° C. Each
stratification treatment was temporally staggered so that all seeds would begin
incubation at the same time; the seeds for the 0- and 15-day treatments were
stored in Whirl-Pak bags at room temperature before surface sterilization.

In mid-February 2006, stratified seeds were removed from refrigeration,
and the no-stratification seeds were surface sterilized in preparation for
incubation and germination; no seeds germinated prior to sowing. Following
Weber and Sorensen (1990), seeds from each Whirl-Pak bag were sown into
polystyrene Petri plates (100mm Not TC-Treated Culture Dish, Product #430591,
Corning Life Sciences, Corning, NY, USA) containing 15 mL fine (#4) vermiculite
saturated with 13 mL deionized (DI) distilled water (NANOpure ultrapure water
system, Model D4741, Barnstead lntemational, Dubuque, IA, USA) and covered
by a 90-mm round filter paper (Whatman Grade 1; Whatman, lnc., Florham Park,
New Jersey, USA). Appropriately labeled lids allow gas exchange by design.
The plates containing seeds were randomly arranged within an aerated growth
chamber (Model E-15, Environmental Growth Chambers, Chagrin Falls, OH,
USA) and kept in the dark at 20° C; Li et al. (1994) report no effect of light on

germination of stratified ponderosa pine seed. DI water was added to plates as

90

necessary to maintain moist (but not saturated) filter papers, and plates were
regularly shifted in the growth chamber to avoid chamber effects.

Germinated seeds were assessed more frequently (e.g., twice daily) early
in the experiment and then at least daily as the number of new germinants
curtailed. Germination was counted when the radicle reached ~1 mm in length
(Weber and Sorensen 1990). Seeds covered by fungi were not removed
because many of them germinated even when infected. As temporally feasible,
germinated seeds were removed with forceps sterilized with ethanol and planted
into individual pots with soil for another component of this dissertation research.
Ungerminated seeds were counted at the end of the experiment and considered
functionally nonviable in the analyses.

The time to initial germination, total percent, and time to complete actual
germination were determined from each tree within each stratification treatment.
Effects of site on the response variables within 3-needle (MTL and PAL sites)
and 5-needle (PAL and RC/LIZ sites) were estimated by two-factor analysis of
variance (ANOVA), then effects of species and stratification were similarly
estimated for the PAL site. All response data were transformed by natural
logarithm to normalize residuals. Finally, the pooled effects of species and
stratification were estimated with two-factor ANOVA. Given the unbalanced
design, Type II sums of squares (SS; Langsrud 2003) were used to test
hypotheses using the ‘car’ (Companion to Applied Regression; Fox 2006)
package in R (R Development Core Team 2007). Means were compared at

each factor level using Tukey’s Honest Significant Difference (HSD) in R, which

91

incorporates an adjustment for unbalanced designs (R Development Core Team

2007).

Reciprocal germination experiment

Two sites were selected for the reciprocal germination experiment: the
top of a ridge extending off the north slope of Mount Lemmon (32.445°N
110.789°W, ~2720 m elevation), and the top of a small west-facing ridge near
Willow Canyon (32.387°N 110.694°W, ~2185 m elevation). These sites
represent the top of the mountain beyond the known distribution of PIN_ARI and
below the known distribution of PIN_PON, respectively. The soil at the Mount
Lemmon site is derived from micaceous sandstone and sandy phyllite, while the
soil around Willow Canyon is derived from muscovite—garnet leucogranite (Force
1997). The vegetation at the Mount Lemmon site is dominated by Douglas-fir
(Pseudotsuga menziesir) and PIN_PON; the 2003 Aspen Fire eliminated
regeneration and killed most of the overstory at this site. Willow Canyon
vegetation is dominated by PIN_ARI, Arizona madrone (Arbutus arizonica), and
oaks (Quercus spp.); the Aspen Fire eliminated ground vegetation, soil organic
matter, and small-diameter trees at this site but little of the overstory was killed.
In January 2006, canopy openness measured by spherical densiometer
(Lemmon 1957) was not significantly different between the two sites (Mount
Lemmon: 1? =16.6%, s=2.6%; Willow Canyon: 56 =22.9%, s=12.2%; Wilcoxon
Rank Sum Test, W=38.5, p-value = 0.4055; R Development Core Team 2007).

Seeds from six PIN_PON (mean 2752 m elevation) and seven PIN_ARI

(Palisade Rock — mean 2526 m elevation; Rose Canyon - mean 2159 m

92

elevation) trees were selected for the reciprocal germination experiment (Table
3-1). In late January 2006, a total of twelve seeds, one from each contributing
tree, were sown on a prepared seedbed free from litter within each of ten
predator-exclusion cages. Six aluminum nails centered longitudinally and
spaced by 10 cm were pushed into the soil to serve as sowing and monitoring
guides (Figure 3-1). Each seed was placed 5 cm above or below the nail in a
predetermined randomized pattern. Seeds were covered with 1 cm of light duff
before cages were placed over the seeds (Figure 3-2). The cages were
constructed from 0.5-in (1 .3-cm) hardware cloth to provide a 20-cm wide x 61-cm
long seedbed with 15 cm of space for vertical growth and 3 cm of cloth folded
back under the soil. Landscape pins and tags listing cage number and contact

information were installed at two opposing comers of the cage.

93

 

Figure 3-1. Prepared seedbed within the predator—exclusion cage at the Willow
Canyon site. Note the six aluminum nails either side of which seeds were sown.

 

Figure 3-2. Predator-exclusion cage after placement over sown seeds at the Willow
Canyon site (January 2006).

Seed germination was assessed at both sites in June 2006 and mid-
October 2006. No germinants were observed in any cage at either site in June
2006 (G. Friedlander, personal communication), thus no documentation was
required. In October 2006, the germinants in each cage were digitally
photographed from above, and live and dead pine germinants within each cage
were mapped relative to the aluminum nails and sides of the cage. Patterns of
erosion evidenced by lack of duff were also mapped. The roofs of two cages at
the Mount Lemmon site were bent and repaired in the field; substantial force from
a round object (e.g., bear paw) was required to create the pattern along the
crease of the roofs and corners. No germinants appeared to have been
damaged.

Without referring to the identity of seeds arranged within each cage,
probable planted (versus naturally sown) germinants were identified on the maps
and then color-coded by species of seed. Each cage was considered to be a
replicate in a balanced, two-factor (site and species) design, and thus percent
germination was calculated for each species in each cage. Differences in
germination resulting from site, species, and their interaction effects were
summarized and analyzed by two-factor ANOVA, and means were compared at

each factor level by Tukey’s HSD in R (R Development Core Team 2007).

95

Results
Cold stratification experiment

Seed coats began cracking two days after incubation began, and the first
germinants appeared by the third day. Most of the germination occurred within
the first 8 days with a number of seeds germinating for another 12 days; the
experiment was terminated after day 22. No trend in total percent germination
across site, species, or stratification was observed (Figure 3-3); range in values
was 75% (PIN_ARI from PAL site in 0-day stratification) to 93% (PIN_ARI from
RC/LIZ site in 15-day stratification). Stratification increased the mean time to first
and last germination events for all morphotypes; values ranged from 77.6 h (3.2
d; PIN_PON from MTL site in O-day stratification) to 111.4 h (4.6 d; PIN_ARI from
RC/LIZ site in 30-day stratification). There was a large amount of variability in
time to last seed germination for PIN_ARI, especially in the shorter stratification
treatments. Mean time to last germination ranged from 99.7 (4.2 d; PIN_PON
from MTL site in 0-day stratification) to 271.1 h (11.3 d; Mixed from PAL site in

30-day stratification).

96

 

 

 

 

 

 

 

      

 

100 -
23 so -
S
E 60 1 Clo—day
5 B15—day
8. ISO-day
- 40
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201
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PIN_PON PIN_ARI
Q:
= I V
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g / EMS—day
a / I30—day
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Mmd PIN ARI
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5 200 « ”may
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3 1504
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'—
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o I

 

 

PIN_PON Med PIN_ARI

Figure 3-3. Mean seed germination (top), time to first germination (middle), and elapsed
time to the last seed germination (bottom) for the full data set. Species data are pooled
across sites. Data are mean values; bars are one standard deviation.

97

Site effects within each of the PIN_PON and PIN_ARI groups were either
not significant. In the PIN_PON group, responses to stratification treatments by
seeds from MTL and PAL overlapped for all response variables (Figure 3-4).
However, the time to first and completion of realized germination were
significantly affected by stratification treatment (Table 3-2); all painivise
comparisons between stratification treatments were significant (p<0.05) for time
to first germination, while only the 0- and 15—day stratification treatments were
not different for time to completion of germination. Removing the non-significant
interaction term, as in all of the analyses presented in this section, had no effect
on identifying significant model terms, but rather only slightly affected their level

of significance, which will not be reported.

 

 

1000 O O 500 OWL-initiate
O 450 APAL-inltiate
8 Q Q OMTL-corrplete ‘
55‘ 80W 0 g 40° APAL-corrplete
- O gs ‘
2 6° 3 8 30° .
g Q 0 .133 zso .
g "' 0
§ 40 .3 £200 : 2
t- O “ 150
g A I I
20 7‘ 100; Q g
OMTL 50
APAL
o . r . o . . .
o 15 30 o 15 30
Stratification treatment (day) Stratification treatrmnt (day)

Figure 3-4. Germination as a function of stratification treatment for seeds from
PIN_PON trees at MTL and PAL.

98

Table 3-2. Type II tests of 2-factor ANOVA of percent germination (pergerm), time to first
germination (initiate), and time to last realized germination (complete) for PIN_PON seed
as a function of site (MTL and PAL), stratification (strat; 0-, 15-, and 30—day), and their
interaction. Determination of site effect was made by Tukey’s HSD. Adjusted R2 for the
three models are -0.1029, 0.4235, and 0.4122, respectively. Significant (a<0.05) terms
are bolded.

 

 

Site effect

Response Adj-p Term Df SS F-value P-value

Ln(pergerm) 0.6454 Site 1 0.01405 0.2156 0.6454
Strat 2 0.02647 0.2031 0.8172
Site'strat 2 0.05426 0.4163 0.6629
Residuals 33 2.15052

Ln(initiate) 0.7028 Site 1 0.00088 0.0797 0.7794
Strat 2 0.35205 1 5.91 25 1.451E-05
Site'strat 2 0.01 1 19 0.5060 0.6075
Residuals 33 0.36505

Ln(complete) 0.3221 Site 1 0.08894 1.0103 0.3221
Strat 2 2.4821 8 14.0971 3.756E-05

Site'strat 2 0.21532 1.2229 0.3074
Residuals 33 2.90527

 

In the PIN_ARI group, responses were similarly distributed, but
stratification appeared less important across the dependent variables (Figure 3-
5). Site and stratification were not significant (p>0.05) effects on percent
germination or time to completion of germination, but both were significant
(p=0.043 and 0.010, respectively) for time before initiation of germination (Table
3-3). The response of the seeds from RC/LIZ in the 30-day cold stratification
treatment influences these results more than any of the other relevant pairwise
comparisons; the 0- and 30-day stratification treatments within RC/LIZ was
slightly less than significant (p=0.084) and across both sites was highly
significant (p=0.008). Interestingly, the Type-Ill test reports nonsignificant p-
values for site (p=0.450) and stratification (p=0.198) in the model for initiation of

germination. Furthermore, a one-way ANOVA test of site on time to initial

99

germination was minimally significant (ln(initiate)~site, p=0.071). Due to the sole
influence of the 30-day stratified seed from RC/LIZ, site effects between PAL and

RC/LIZ are considered to be inconsequential for the analysis of the full data set.

 

 

A PAL - initiate
10° 3 B 8 600 D LIZ/RC - initiate
a E ‘ 4 PAL - corrplete
.. I LIZ/RC - corrplete ‘ ‘
a! 80 3 U i 500 a I
:1 . I
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g 60 A 8 8
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20 F 5 I
. m 10° E i E
D LIZ/RC
0 V 1 ‘ 0 r I 1
0 15 30 0 15 30
Stratification treatment (day) Stratification treatment (day)

Figure 3-5. Germination as a function of stratification treatment for seeds from PIN_ARI
trees at PAL and RC/LIZ.

Table 3-3. Type II tests of 2-factor ANOVA of percent germination (pergerm), time to first
germination (initiate), and time to last realized germination (complete) for PIN_ARI seed
as a function of site (PAL and RC/LIZ), stratification (strat; 0-, 15-, and 30-day), and their
interaction. Determination of site effect was made by Tukey's HSD. Adjusted R2 for the
three models are 0.0396, 0.3054, and -0.1518, respectively. Significant (a<0.05) terms
are bolded.

 

 

Site effect

Resmnse Adj-p Term Df SS F-value P-value

Ln(pergerm) 0.0871 Site 1 0.09728 3.2220 0.08706
Strat 2 0.07894 1 .3073 0.29169
Site'strat 2 0.00712 0.1 178 0.88943
Residuals 21 0.63405

Ln(initiate) 0.0427 Site 1 0.07790 4.6543 0.04271
Strat 2 0.1 9128 5.7141 0.01044
Site'strat 2 0.00587 0.1754 0.84031
Residuals 21 0.35149

Ln(complete) 0.9280 Site 1 0.0024 0.0084 0.9280
Strat 2 0.2285 0.3962 0.6778
Site*strat 2 0.2228 0.3863 0.6843
Residuals 21 6.0550

 

100

Within the PAL site containing all three morphotypes, there were no
effects of species on any of the response variables (Table 3-4). Time to initiation
was influenced by stratification, especially the 30-day treatment; the mean
difference between the 0- and 30-day and the 15- and 30-day treatments were

significant (p=0.001 and 0.033, respectively; Figure 3-6).

0 O-day - initiate
A 15-day - initiate
600 a 30-day - initiate

 

 

100 I C 0 I O-day - complete
A ‘ 500 A 15—day - complete I
80 I I 30-day - complete ‘
25 a ' '
c I ‘5: 400
g 60 g 3 S A ‘
E o g E 300 ‘
a A .5 E ‘ 7
- 4o 8
g g 200 7 '
2 o O-day F - '
0 A 15-day 100 a a
I 30-day
o r r 1 0 fi
PIN_PON Mixed PIN-ARI PIN_PON Mixed PIN ARI
Ilo ho
Morphotype '13 MN

Figure 3-6. Germination as a function of stratification treatment for combined
morphotypes at the PAL site.

101

Table 3-4. Type II tests of 2-factor ANOVA of percent germination (pergerm), time to first
germination (initiate), and time to last realized germination (complete) for the PAL site as
a function of morphotype (PIN_PON, Mixed, and PIN_ARI), stratification (strat; 0-, 15-,
and 30-day), and their interaction. Painivise comparison of PIN_PON and PIN_ARI (3-5
effect) was made by Tukey’s HSD. Adjusted R2 for the three models are -0.1774,
0.2311, and 0.0015, respectively. Significant (a<0.05) terms are bolded.

 

 

3-5 effect

Response Adj-p Term Df SS F-value P-value

Ln(pergerm) 0.9909 Morphotype 2 0.02273 0.2831 0.7556
Strat 2 0.01380 0.1719 0.8430
Site*strat 4 0.07290 0.4540 0.7686
Residuals 27 1 .08392

Ln(initiate) 0.9028 Morphotype 2 0.01074 0.3453 0.71 1 1
Strat 2 0.25405 8.1721 0.0017
Site*strat 4 0.02307 0.371 1 0.8271
Residuals 27 0.41969

Ln(complete) 0.2327 Morphotype 2 0.8094 1.5417 0.2323
Strat 2 0.9196 1.7516 0.1926
Site'strat 4 0.3844 0.3662 0.8305
Residuals 27 7.0872

 

Because site effect was not significant, the data were pooled across sites
(Figure 3-3) and analyzed by morphotype and stratification treatments. None of
the terms, including interaction, explained a significant (p>0.05) portion of the
variance in percent germinated seed (Table 3-5). For time to initiation of
germination, stratification was highly significant (p<0.001), but the effect of
morphotype was not significant (p=0.28; PIN_ARI versus PIN_PON pairwise
comparison p=0.26). Time to complete germination was significantly affected by
both species and stratification (both p=0.001). Painivise comparisons in this
model between PIN_ARI and PIN_PON (p=0.001), 15- and 0-day stratification
(p=0.010), and 30- and 0-day stratification (p=0.002) were highly significant; the
Mixed-needled trees were only marginally different (p=0.106) from PIN_PON.
However, PIN_PON and PIN_ARI were only significantly different in the 0-day

stratification treatment (Table 3-6). Although there was no significant interaction

102

(p=0.28), morphotype and stratification treatment affected the time to completion

of germination.

Table 3-5. Type II tests of 2-factor ANOVA of natural-log-transformed percent realized
germination (pergerm), time to first germination (initiate), and time to last germination
(complete) for pooled sites as a function of morphotype (PIN_PON, Mixed, and
PIN_ARI), stratification (strat; 0-, 15- and 30-day), and their interaction. Pairwise
comparison of PIN_PON and PIN_ARl (3-5 effect) was made by Tukey’s HSD. Adjusted
R2 for the three models are -0.0666, 0.388, and 0.2657, respectively. Significant
(a<0.05) terms are bolded.

 

 

3-5 effect

Response Adj-p Term Df 88 F-value P-value

Ln(pergerm) 0.5939 Morphotype 2 0.04697 0.4984 0.6098
Strat 2 0.08355 0.8867 0.4169
Morphotype'strat 4 0.02873 0.1525 0.9613
Residuals 66 3.10959

Ln(initiate) 0.2598 Morphotype 2 0.03384 1.3004 0.2793
Strat 2 0.66130 25.4099 6.56E-09
Morphotype*strat 4 0.01932 0.3712 0.8283
Residuals 27 0.85883

Ln(complete) 0.0011 Morphotype 2 2.5232 7.5459 0.0011
Strat 2 2.4268 7.2575 00014
Morphotype‘strat 4 0.8651 1.2935 0.2816
Residuals 27

 

Table 3-6. Pairwise comparisons (Tukey's HSD) of PIN_PON and PIN_ARI seeds for
natural-log-transformed percent realized germination (pergerm), time to first germination
(initiate), and time to last germination (complete) as a function of stratification treatment
for pooled sites. Data are reported as adjusted p-values; significant values are bolded.

 

 

Stratification pergerm initiate complete
O-day 1.0000 0.9168 0.021 2
15-day 0.9860 0.9993 0.2557
30-day 0.9993 0.9974 0.9999

 

Reciprocal germination experiment
Approximately one-half of the PIN_PON seeds germinated the first
growing season at both sites, while only around one-fourth of the PIN_ARI seeds

germinated between the two sites (Figure 3-7). Consequently, site was not

103

significant but species was significant in the ANOVA (Table 3-6). The overall
interaction term was not significant (Table 3-7), but a significant difference exists
between germination of PIN_PON and PIN_ARI seeds at the high elevation site
on Mount Lemmon (Table 3-8). There was no difference in germination of
PIN_ARl seeds from Palisade Rock and Rose Canyon (x2=1.6897, df=1, p-

value=0.1936; R Development Core Team 2007).

100 -

 

 

 

 

 

El PIN_PON
I PIN_ARI V
80 '
55
v 60
C
.2
76
.5
E
5 40
0
l-
20 "l J
o l—

 

 

 

Mount Lemmon Willow Canyon

Figure 3-7. Germination of PIN_PON and PlNl__ARl seeds planted at high (Mount
Lemmon) and low (Willow Canyon) elevation. Values are mean percent germination;
bars are one standard deviation.

Table 3-7. ANOVA for germination success of PIN_PON and PIN_ARI seeds at the
Mount Lemmon and Willow Canyon sites (adjusted R2=0.1625). Significant (a<0.05)
terms are bolded.

 

 

Term Df SS MS F-value P—value
Site 1 837.2 837.2 1.0287 0.3172
Morphotype 1 7049.0 1049.0 8.6613 0.0057
Site'morphotype 1 714.0 714 0.8773 0.3552
Residuals 3 29298.7 813.9

 

Table 3-8. Multiple comparisons between site (Mount Lemmon - MTL and Willow
Canyon - WC), morphotype, and interactions by Tukey’s HSD. Significant (a<0.05)
comparisons are bolded.

 

 

 

Group 1 Group 2 Mean difference P-value
WC MTL 9.15 0.3172
PIN_ARI PIN_PON -26.55 0.0057
WC PIN_PON MTL PIN_PON 0.7 0.9999
WC PIN_ARl MTL PIN_ARI 17.6 0.5200
WC PIN_ARI WC PIN_PON -18.1 0.4962
MTL PIN_ARI MTL PIN_PON -35.0 0.0445
Discussion

Effects of stratification on germination success and rate

In the controlled cold stratification experiment, a similar percentage (75-
93°/o) of PIN_PON and PIN_ARI seeds germinated across stratification
treatments. Although molds were present in some Petri dishes, seeds continued
to germinate and thus were not considered to be affected by the molds. Seeds
from PIN_ARI appeared (Figure 3-3) to have higher germination rates than
PIN_PON with longer stratification periods, but this difference was not significant
due to a large amount of intraspecies variation.

In contrast to the stratification experiment, more PIN_PON than PIN_ARI
seeds from the same seed lots used in the laboratory experiment (Table 3-1)
germinated at both high (Mount Lemmon site; 52 vs. 13%, p=0.0445) and low
(Willow Canyon site; 52 vs. 34%, p=0.4962) sites, although the difference at the
latter site was not significant (Figure 3-7). Since the seeds in the present study
were sown into the field in January, approximately the coldest time of year,
“natural” cold stratification is assumed. In October, a large number of germinants

were growing around the predator-exclosure cages at the Willow Canyon site

105

(personal observation), likely due to release of seeds by overlying trees during
the winter. Consequently, selection of germinants from seeds sown for this
experiment was highly conservative; i.e., I included only those germinants falling
exactly in the expected location, which ostensibly led to a lower perceived
germination rate for both taxa. Also, runoff appeared to have removed the light
duff over the seeds in portions of two cages at the Willow Canyon site; expected
seeds from these portions were removed from the calculations. No ponderosa
pine germinants were observed outside of the cages at the Mount Lemmon site;
instead, forbs had sprouted within and around the cages, possibly shading or
displacing seeds away from the expected location. Like the stratification
experiment, there was a large amount of intraspecies variability in total
germination (Figure 3-7).

In the controlled laboratory experiment, cold stratification significantly
increased the amount of elapsed time after sowing before seeds began to
germinate as well as the length of time before the last seeds germinated (Figure
3-3). There seemed to be no species difference in time to first germination, while
PIN_ARI generally took longer to complete germination than PIN_PON in all
stratification treatments and noticeably longer than Mixed in the 0-day control
treatment. Distinct from all other patterns of stratification effects, PIN_ARI seeds
in the longest stratification treatment actually completed germination in less time
than those in the 15-day treatment and about the same amount of time as those
in the 0—day control treatment (Figure 3-3). This idiosyncrasy is due to seeds

from 2 of the 5 PIN_ARI trees from RC/LIZ completing their (100% actual)

106

germination relatively early (162 h; 6.7 d) in the incubation period and thus
biasing the mean and trend observed in the other morphotypes.

The effects of stratification on germination success and rate in ponderosa
pine seeds varies, as was demonstrated in the present experiments. In
laboratory tests, Barton (1930) also found higher initial but similar final
germination by stratified and unstratified ponderosa pine seed. Smith (1986)
also reported higher initial rate but found lower overall germination of stratified
(f = 25% over 30-90 days) versus unstratified (f =38%) ponderosa pine seed
from New Mexico and Colorado. The effects of stratification were supported by a
second test of the same seed batch conducted at a production greenhouse (f =
7% over 31-55 days versus 3? =12% for unstratified seed; Smith 1986); the
significantly lower germination at the greenhouse as compared to the laboratory
could not be explained. Weber and Sorensen (1990) found that stratification
increased germination rate but also uniformity and total percentage, especially at
lower incubation temperatures, in ponderosa pine (Pinus ponderosa var.
ponderosa) from central Oregon. All whole seeds germinated after 30 days of
stratification followed by incubation at 20° C, the same temperature used in this
study, with fewer days before the majority of seeds germinated in longer
stratification periods (Weber and Sorensen 1990). Following sowing of
ponderosa pine seed in Fort Valley Experimental Forest of northern Arizona,
Schmid and Mitchell (1986, cited by Howard 2003b) found 61-90% germination

beginning 12-37 days after sowing.

107

From the controlled stratification experiment, I conclude that there is no
difference in germinability between any of the morphotypes tested in this study.
In addition, stratification by itself did not appear to affect germination success as
demonstrated in the cold stratification experiment. The difference in observed
germination success between PIN_PON and PIN_ARI in the field must be due to
some other factor, such as timing of germination (see below). However,
emergence is only the first critical threshold in seedling establishment; germinant
survival through the first arid foresummer (May-June 2007) has not been
determined. In Barton’s (1993) study of elevational distribution of desertic pines
in nearby Chiricahua Mountains, the species with the lowest elevational
distribution (Pinus discolor) had higher emergence and first-year survival at high
than low elevation, which is an opposite trend to PIN_ARI in the present study.
However, all of the germinants of the higher elevation species (P. engelmannii
and P. leiophylla var. chihuahuana) had died at lower elevation by the end of the
first arid foresummer (Barton 1993). In the present study, the PIN_PON
germinants, then, are expected to have lower survivorship at the Willow Canyon
site as compared to the Mount Lemmon site. The long-term implication of
survival by young seedlings through multiple seasons of drought is the

determination of the community composition.

Implications of variable germination period

Seed germination is affected by a multitude of abiotic factors influencing

the seed physiology, including temperature, water potential, light, smoke, and soil

108

chemistry (Bewley and Black 1985, Baskin and Baskin 1998). The survival of the
resultant seedling is highly dependent on the narrow temporal congruence of
these factors, especially temperature and water potential, or moisture content.
Given that most montane trees produce and disperse mature seeds in the fall, it
is no surprise that physiological dormancy is induced at maturity to endure the
winter (Kozlowski et al. 1991, Baskin and Baskin 1998). Germinants would have
precious few weeks to establish root systems and store photosynthates before
the onset of freezing conditions. Where snow cover is absent, freezing injury
severely reduces seedling survivorship (Germino et al. 2002). In pines,
populations that are adapted to colder temperatures (i.e., higher in elevation)
tend to have deeper dormancy and longer chilling requirements (Weber and
Sorensen 1990, Skordilis and Thanos 1995, Allen and Meyer 1998). However,
dormancy will vary within a given seed lot (Kozlowski et al. 1991) and even within
mother plants (Andersson and Milbert 1998). Ponderosa pine is known for
producing mature seeds in the fall that can germinate under light but that
generally require prechilling for maximum (dark) germination (Li et al. 1994).
However, more slowly drying seeds could become more dormant than those that
dry fast (Baskin and Baskin 1998); in other words, seeds from more xeric
habitats at low elevation could be less dormant than those from higher elevation.
Although there was no species difference in time to initial germination in this
study, PIN_PON did complete germination faster (i.e., more dormant and thus
more influenced by the stratification effect) than PIN_ARI, the lower elevation

species.

109

Physiological dormancy is usually broken in seeds by imbibing water
during the stratification process (Baskin and Baskin 1998). Once the minimum
cardinal temperature for germination is reached (Bewley and Black 1985),
ponderosa pine germinants begin to appear in the spring through epigeal
germination (Oliver and Ryker 1990). Since there is no innate soil seed bank in
pines (Keeley and Zedler 1998; except for Pinus albicaulis in Tomback et al.
2001), all viable seeds should germinate when minimum moisture content and
thermal loads coincide (Alvarado and Bradford 2002). However, pine seeds that
germinate in the spring in the Santa Catalina Mountains and similar mountain
islands of the semiarid Southwest will face the arid foresummer with little growing
time to establish root systems that can access deeper water. Schubert (1974,
cited in Oliver and Ryker 1990) noted that water potentials more negative than
0.7 MPa significantly reduced seed germination, as well as root penetration, root
dry weight, and cotyledon length in southwestern ponderosa pine germinants. If
seeds were to germinate during this cooler, moist period followed by the arid
foresummer, drought stress would ultimately kill them. In fact, by 27 June 2006,
there were no pine germinants observed around or in any of the predator-
exclusion cages at either Mount Lemmon or Willow Canyon site (G. Friedlander,
personal communication); this observation indicates that no pine seeds had yet
germinated.

Consequently, despite more northern Rocky Mountain ponderosa pine
populations producing seed that germinate in the spring (Young and Young

1992), the Southwestern race (Conkle and Critchfield 1987) as well as Arizona

110

pine trees produce seed that undergo a secondary conditional dormancy. Baskin
and Baskin (1998) describe primary dormancy as that which a seed possessed
when freshly mature, while secondary conditional dormancy occurs when primary
dormant seeds become partially or fully nondorrnant but then are induced into a
second conditional dormancy under certain environmental conditions. The moist
stratified seeds of PIN_PON and PIN_ARI are exposed to sufficiently warm but
inconsistent (i.e., frequent night frosts) spring temperatures but do not germinate.
Instead, as their water potential increases (i.e., the seeds lose moisture) with the
coming arid foresummer, they undergo an induced secondary conditional
dormancy. This secondary dormancy is then broken by the summer monsoon
precipitation when the soil temperatures are consistently warm and soil is moist.
Figure 3-8 illustrates the hypothesized path through secondary conditional
dormancy and subsequent successful germination relative to the hydrologic
seasons.

We should then not be surprised that the species found at higher
elevation, and thus presumably more sensitive to drought (Barton 1993),
completes the entire germination process more rapidly and uniformly than lower
elevation species. As the summer monsoon subsides, air and soil temperatures
remain high, and soon the fall drought will impose stress on the young seedlings.
Therefore, there is strong selection for rapid initiation and completion of
germination in the narrow window of adequate soil moisture and consistently
warm temperatures before both the fall drought ensues and winter delivers

freezing conditions. Conversely, total loss of a year’s regeneration efforts could

111

occur as a result of precipitation events before the arid foresummer concludes,
while species whose seeds take longer to germinate may endure the remaining
drought prior to monsoon.

Ponderosa pine seedlings rapidly grow tap roots, which enables them to
survive in xeric soils (Howard 2003b), yet seedling mortality is highest in the arid
Southwest (Howard 2003b). Ponderosa pine seedlings in Arizona that
germinated earlier in the monsoon were more likely to survive the first year than
those that germinated in August (Schmid and Mitchell 1986, cited by Howard
2003b). Given that there is greater selection for extreme conditions than for the
mean (Ledig 1998) and that PIN_PON are faced with more limiting conditions as
the growing season is shorter at high elevation, it is sensible that the germination
and competition for soil-water resources occurs more rapidly for PIN_PON than
for PIN_ARI, which is shown in part by the more rapid completion of germination

after stratification by PIN_PON in this study.

112

Summer Monsoon ‘

 

Seeds mature
in cones

 

 

 

 

Seeds on ground

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

' ‘ imbibe moisture
Foregsgimer Md erminate
Seeds dry Germinants
and enter die from sepergiaester Fa|| Drought
finndr‘taio‘rlal drought stress dormancy
A y / and disperse
Early I Seeds on
s rin “ ground
P 9 \ germinate
\ \ \ I/I‘
\ or /
\ “I
Seeds imbibe

 

 

 

 

. 1
morsture \

 

 

Winter Rains/Snow

Figure 3-8. A proposed path of selection for conditional (secondary) dormancy of seeds
produced by Arizona and Southwestern ponderosa pine in the mountain islands of the
Southwest experiencing bimodal precipitation patterns. Relevant hydrologic seasons
outline the path.

Biogeographic implications in a changing climate

Based on projections from both the Intergovernmental Panel on Climate
Change and United Kingdom Hadley Centre’s general circulation models, spring
and fall temperatures in Arizona will increase by 1.7-2.2 °C, and winter and
summer temperatures will increase by 2.8 °C by 2100 (USEPA 1998).
Precipitation is projected to increase in spring (20%), decrease slightly in
summer (-15-0%), increase in fall (30%), and increase in winter (60%) (USEPA
1998). The actual effects of these projected climatic changes on biological

functions, commonly indicated by some measure of actual versus potential

113

evapotranspiration, are difficult to ascertain, especially when complex topography
and elevation are considered. However, the projections do suggest that the
Ponderosae in Arizona may experience a warming climate, especially during the
winter and summer, and the bimodal precipitation pattern characteristic of
southeastern Arizona may be somewhat more smoothed across the seasons,
especially by reducing the intense monsoonal precipitation and severity of the fall
drought. However, the projections are based on the entire state of Arizona, of
which the southeastern portion that contains the mountains islands of
Pondemsae is most affected by the rising subtropical high-pressure ridge
(“Bermuda High”) and its associated intense convective storms (Sheppard et al.
2002). Consequently, I can only suggest with some confidence that all seasonal
temperatures are expected to increase (“global warming”) and that precipitation
patterns may be shifted toward a wetter fall and winter.

In other parts of the world, global warming has already resulted in
altitudinal shifts of over 100 m in treeline and over 300 m in advanced
regeneration of tree species (e.g., Kullman 2002). In colder climates, the milder
winters reduce freezing-related injuries and extend the growing season,
especially important to germination (Hobbie and Chapin 1998) and resultant
seedlings (Kullman 2002). In the Santa Catalina Mountains and other mountain
islands of the Southwest, the overall warmer climate but wetter fall may extend
the temporal window for germination and successful establishment for both
PIN_PON and PIN_ARI. Dodge’s (1963) observation of dead trees and stumps

of “ponderosa pine” below the current elevation of both PIN_PON and PIN_ARI

114

in the Santa Catalina Mountains indicates that at least one of the two species is
migrating upward in elevation, likely in response to climate change. In fact, the
annual average temperature has increased by 2 °C and the annual precipitation
has increased by at least 15% in the Tucson area over the last century (USEPA
1998). Given the presumed strong influence of drought in limiting the more xeric
lower elevational distribution for both species and that more humid fall conditions
are projected for the next century, both taxa may be able to expand their overall
range in the mountain islands. However, if the additional moisture is not
physiologically useful (i.e., increased storm intensities), then drought will become
even more severe in limiting their lower elevations. Given the low soil-water
holding capacity of the shallow lithosols overlying steep bedrock gradients in the
Santa Catalina Mountains, as well as the other mountain islands of the
Southwest, though, distribution expansion is likely only to occur toward more

mesic conditions at high elevation.

115

CHAPTER 4

DIFFERENTIAL COLD TOLERANCE SEGREGATES DISTRIBUTION OF
PONDEROSAE IN THE SANTA CATALINA MOUNTAINS OF ARIZONA

Introduction

Cold conditions restrict the upper boundary, whether high latitude or
elevation, of plant distribution through negative carbon balance (or inability to
process acquired carbon; Hoch and Korner 2003), interrupted phenological cycle,
and poor resistance to secondary factors, such as wind force, snow abrasion,
parasites, and high heat (Tranquillini 1979). Since closely related species
(Sutinen et al. 1992, Lindgren and Hallgren 1993, Hawkins et al. 2003, Nippert et
al. 2004), populations within species (DeHayes 1977, Kolb et al. 1985, Schaberg
et al. 1995), and even hybrid species (Lu et al. 2007) vary in their cold tolerance,
their upper elevational limit should vary accordingly. Seedlings, in particular, are
more sensitive to the severe abiotic conditions present at upper elevation limits
(Cui and Smith 1991, Johnson et al. 2004) and should thus present stronger
signals of differential interspecific cold tolerance.

The development of cold tolerance, or hardening, is related to genetic
endogenous rhythms and enhanced by exposure to environmental conditions
that limit metabolism and carbon acquisition (Havranek and Tranquillini 1995).
The endogenous control is observed when dormant plants recover little under
favorable conditions until later in winter (Pisek and Schiessl 1947, cited by
Havranek and Tranquillini 1995). The initiation of predormancy is triggered by

shorter day lengths, which is associated with lower daily temperatures.

116

Consequently, chemical reaction rates (i.e., metabolism), biosynthesis, gas
exchange, water and nutrient acquisition, and carbon assimilation decrease, with
primary growth finally terminating at the end of the season (Larcher 2003). Even
before freezing temperatures are experienced by plants, lower temperatures
induce changes in cellular structures in preparation for hardening. The changes
occur at different rates in different organs of the plant; e.g., cold hardiness
develops slower and to higher temperatures in roots than in other organs
(Kozlowski et al. 1991). Protoplastic osmotic potential increases, and thus
intracellular freezing point decreases, from accumulation of sugars, amino acids,
and ions, the vacuole fissions, and intracellular water moves to extracellular wall
spaces, which also increases the osmotic potential and translocates initial
(“extracellular”) ice crystal formation outside the cellular wall barrier (Larcher
2003). Exposure to near-zero freezing temperatures induces production of cold-
stable phospholipids and stress proteins that stabilize biomembranes (Larcher
2003). Cellular survival is dependent on the ability to prevent ice crystal
formation within the cell; not only do the crystals destroy membrane integrity, but
they increase cellular dehydration due to the higher vapor pressure above the ice
than the supercooled cytoplasm. As cytoplasmic desiccation increases, as in
drought conditions, the concentration of salt ions and organic acids reach a toxic
level which results in enzyme inactivation, biomembrane phospholipid
deterioration, protein disassociation, and ATPase inactivity (Larcher 2003).
During dormancy, conifer leaves continue to absorb light, even though low

temperatures inhibit the enzyme-mediated rate of carbon assimilation (Berry and

117

Bjorkman 1980). Consequently, the light energy in excess of that used for
photochemistry must be dissipated lest oxidized radicals damage the
photosynthetic machinery. In fact, conditions where the amount of absorbed light
energy is greater than biochemically useful can occur during other stressful times
of the growing season (e.g., drought) or day (e.g., midday solar peak), as well. In
photosynthetic leaves, light energy is absorbed by accessory pigments (e.g.,
carotenoids, chlorophyll b and c) and chlorophyll (chl) a in the thylakoid
membrane of the chloroplast. When the pigment molecule absorbs a photon of
light, an electron is elevated to an excited state; this energy is transferred by
resonance to chl a and then to a specialized chl a (“trap”) molecule in one of two
thylakoid membrane-bound photosystems (Nobel 2005). Production of a variety
of pigments increases the ability to absorb photons from a broader range of the
electromagnetic spectrum, but this can be harmful under limitation of electron
sinks, such as during dormancy.

Light energy absorbed by Chi a can be dissipated via excitation transfer
through photochemistry or through nonphotochemical quenching (NPQ), such as '
fluorescence, phosphorescence, or radiationless transfer (i.e., heat; Nobel 2005).
During noncyclic electron flow in photosynthesis, energy from an excited electron
is transferred by resonance to photosystem I (PSI) or II (PSII) to drive electron
transport and eventually reduce NADP+ (Larcher 2003). Under high light
conditions, some of the energy is lost by deexcitation of the electron to its ground
state with concomitant production of radiation slightly lower in energy (A=1 KJ

moi-1), or longer wavelength (A=4 nm), than the 662-nm absorption peak (Nobel

118

2005). This radiation, fluorescence, originates largely from the antennae
pigments associated with PSII (Larcher 2003). Phosphorescent radiation is
emitted when a pigment molecule deexcites from a triplet state; the probability of
an excited electron reversing its orbital spin (i.e., “triplet state) is low, thus
phosphorescence is rare (Nobel 2005). Last, excess absorbed light energy can
be dissipated by heat in a variety of ways. Higher-energy photons, such as those
in the 430-nm absorption peak of chi a, excite electrons to a higher but unstable
energy level; the electron quickly transitions to a lower-energy excited state with
the energy difference resulting in production of heat (Nobel 2005).

Another energy-dissipating mechanism through heat is by downregulating
photosynthesis (Oquist and Huner 2003, Adams et al. 2004). When the thylakoid
lumen acidifies from a buildup of protons due to reduced rate of proton pumping
by electron transport, the carotenoid violaxanthin (V) is de-epoxidised to
antheraxanthin (A) and then zeaxanthin (Z) (Bjorkman and Demmig-Adams
1994). This occurs when the electron transport chain becomes sink limited; i.e.,
environmental conditions reduce ATP consumption and photosynthate
production. Normally, violaxanthin transfers absorbed light energy to pigments
associated with chl a, while zeaxanthin thermally dissipates the energy (Adams
et al. 2004). In a review by Oquist and Huner (2003), decreased total chlorophyll
content, increased VAZ pool, and increased ratio of V to A+Z are concluded as
the primary means of NPQ in lodgepole pine (Pinus contorta). As the
plastoquinone pool becomes more reduced from photochemically excessive light,

transcription of genes coding for the light-harvesting polypeptides and reaction-

119

center D1 protein in PSII is repressed, while the chlorophyll-binding PsbS protein
from PSII becomes involved in the de-epoxidation of V to A+Z (Oquist and Huner
2003). The reverse reaction, Z+A to V, can occur within minutes in nondorrnant
leaves or hours in fully hardened leaves (e.g., 100 h for Pinus ponderosa;
Verhoeven et al. 1999).

Damage by cold, or lack of cold tolerance, can be measured by a number
of destructive and non-invasive methods following natural (e.g., Nippert et al.
2004) or controlled (e.g., Burr et al. 1990) freezing events, and different methods
are often used complementarily (Burr et al. 2001). The non—destructive
measurement of chlorophyll fluorescence yields an indicator of quantum
efficiency, or efficiency of the photosynthetic apparatus. In dark-acclimated
leaves, all reaction-centers in the ETC are oxidized, thus the steady-state
fluorescence (F0) can be measured with a fluorometer. A rapid pulse, or series of
pulses, of saturating light then reduces all reaction-centers (i.e., QA in PSII), and
photochemically excess light energy is reradiated as fluorescence, which is
recorded as Fm. The ratio of (Fm-Fo)/Fm, or Fv/Fm, is a measure of the potential
maximum PSII quantum yield, or efficiency of PSII (Schreiber et al. 1994).

During the daytime, the quantum yield of open (oxidized) PSII centers, also
phrased as the excitation capture efficiency of PSII, can be determined by first
reducing GA with a weak far red light source that exclusively drives PSI and thus
drains electrons from PSII, measuring steady-state fluorescence (Fo’), flashing a
saturation pulse to measure Fm’, and calculating Fv’lFm’ as for dark-acclimated

leaves (Schreiber et al. 1994). Both measures of variable fluorescence give an

120

indication of injury or reduction in function at the photosystem-level in the
chloroplast as a function of cold or other stress.

Another method commonly used to assess cold damage and hardiness is
by visual inspection of tissues after an incubation period (7-10 days) following the
freezing event (Burr et al. 2001). While survival into future years following
freezing at certain temperatures is a better test of cold hardiness, industry and
researchers demand more rapid results. Once-frozen tissues, such as needles,
buds, and cambia, are examined, and injury is quantified as percent ‘browning’ or
mortality. This method requires expertise to accurately gauge existence and
extent of tissue damage, but it can produce results consistent with other methods
(e.g., Lindgren and Hallgren 1993). Furthermore, survival after freezing is an
integrated response to cold damage and recovery. However, this method
requires destruction of stems and/or roots to examine cambial damage.

Measurement of electrolyte leakage following cellular membrane
disruption by ice formation is another destructive method for assessing damage
by freezing temperatures. Prepared tissue samples normally release some
amount of cellular electrolytes into a solution; after membrane destruction by
freezing, the electrical conductivity (ECO) of the prepared tissue samples in
solution increases. Cells are Iysed by heating to release the remaining
electrolytes, and the solution is remeasured (ECf). Relative conductivity is
calculated variably as ECO/ECf (Ritchie 1991) or (ECO-B1)/(ECf'BZ) where B1 and
82 are EC values for blanks before and after heating (Burr et al. 2001). This

method is relative fast, inexpensive, but does not produce a measure of mortality

121

(Burr et al. 2001). Also, the results can vary significantly by variation in plant
health status (Burr et al. 2001).

Other methods for assessing cold injury include differential thermal
analysis (DTA) and electrical impedance spectroscopy (EIS). DTA is useful for
identifying the extent to which supercooling has been employed by various
tissues by measuring the exothermic spike in response to spontaneous
nucleation of extra- and intracellular supercooled water (Burr et al. 2001). A
carefully controlled block or chamber contains both the tissue sample and an
inert reference material against which tissue temperature is compared as block
temperature decreases. DTA is less useful for some plant organs, especially
during early and late dormancy, and must be standardized to another method
(Burr et al. 2001). EIS works on the principle that different structures within the
plant, or even cell, pose impedance to an applied electrical current. Currently,
the biological meaning of EIS values is not well understood, but technological
advances in interpreting results from this method have eliminated an actual
freezing test (Burr et al. 2001).

Populations of the closely related Southwestern ponderosa pine (Pinus
ponderosa var. scopulorum Engelmannii) and Arizona pine (Pinus arizonica
Engelmannii) are known to segregate along an elevational gradient that varies in
range and absolute value depending on topographic features and local climate
conditions in the Santa Catalina Mountains of southern Arizona (Dodge 1963,
Whittaker and Niering 1965, Barton 1993). At higher elevation (2430-2650 m),

Southwestern ponderosa pine is codominant to Douglas-fir (Pseudotsuga

122

menziesii Franco) and Southwestern white pine (Pinus strobiformis) (Whittaker
and Niering 1965). Around 2430-2550 m elevation (Epperson et al. 2001), the
dominant Ponderosae transitions to Arizona pine, which extends down to ~1760
m elevation (personal observation). This differential distribution suggests that
cold winter conditions could limit the upward migration of Arizona pine. While
cold tolerance tests have been conducted on ‘ponderosa pine’ (e.g., Burr et al.
1989, 1990), no such tests have been reported for Arizona pine.

The goal for this study was to determine if there is a genetic difference in
cold tolerance between Southwestern ponderosa and Arizona pine in the Santa
Catalina Mountains that could lead to differences in elevational distribution.
lntraspeciflc variation in cold tolerance has been noted in Pinus (Hawkins et al.
2003), so local source of plant material was used in this study; however,
extrapolation to other mountains with this or other pairs of Ponderosae taxa
should be relevant. Given that Southwestern ponderosa pine has a higher
elevational distribution than Arizona pine, l hypothesized that the former species
will have higher resistance, or lower critical temperature values (LTso,
temperature at which 50% mortality or failure is detected), to freezing conditions.
This study incorporates three separate experiments involving 1-year-old potted
seedlings in highly controlled laboratory conditions, 4-year-old potted seedlings

exposed to Michigan’s climate, and small trees in the Santa Catalina Mountains.

123

Methods
Controlled whole-seedling freezing test

In September-October 2005, multiple ripe cones were harvested from
trees considered to be located within “pure” Southwestern ponderosa pine
(“PIN_PON”) or Arizona pine (“PIN_ARI") populations in the Santa Catalina
Mountains. PIN_PON trees were located near the summit of Mount Lemmon
(“MTL,” 32.4396°N, 110.7871°W (NADB3/WGS84), 2770 m elevation), while
PIN_ARI trees were located either near Rose Canyon (“RC,” 32.3964°N,
110.6932°W (NAD83NVGSS4), 2175 m elevation) or northeast of Lizard Rock
(“LIZ,” 32.3844°N, 110.6930°W (NAD83NVGS84), 2135 m elevation). Cones
were air-dried at room temperature in paper bags, and seeds were removed from
the cones by shaking and removing cone bracts in early January 2006.
Unopened cones were heated 14-27 h at 40° C to encourage opening (Young
and Young 1992). Empty, parasitized, or otherwise unhealthy appearing seeds
were discarded, while whole seeds were dewinged by hand, inventoried, and
stored in labeled small manila envelopes at room temperature. Seeds were
germinated in February-March 2006 as part of another component of this
dissertation. Germinants were carefully transferred to ShortOne TreePots (9.5
cm wide, 24 cm tall, 1.64 L volume; Stuewe & Sons, Inc., Corvallis, OR)
containing a 1:1 :3 (v/v) mix of perlite, steam-sterilized loamy sand, and Fafard52
(33% bark, 18% peat, 6% perlite, and 3% vermiculite; Conrad Fafard, lnc.,
Agawam, MA). After planting, 50 mL (~5 mm) of medium-grit crushed granite

was spread onto the soil. Seedlings were grown for 10 months in greenhouses

124

(17-30° C daily) at Michigan State University (East Lansing, MI; 42.7225°N,
84.4763°W (NADB3NVG884), 259 m elevation) with natural light (~9 h in
December), regular watering, and two applications (19 May and 8 October 2007).
of slow-release granular fertilization (Osmocote Classic, 14-14-14, 3-4 mo
release at 70° C; The Scotts Company, Marysville, OH). From the available
stock, 68-70 healthy seedlings of uniform size (i.e., similar number of fully
expanded fascicles) from each species were selected for this experiment, which
will use 60 seedlings per species. There were 8 mother trees for PIN_PON and
5 mother trees for PIN_ARI, and 8-9 PIN_PON and 14 PIN_ARI seedlings were
selected from each seed source (Table 4-1). The proportion of trees coming from

each germination treatment (see Chapter 3) did not differ (PIN_PON, 12:1 .4412,

df=2, p-value=0.4865; PIN_ARI, x2=1-9143. df=2, p-value=0.384).

Table 4-1. Seedlings and source trees from the Santa Catalina Mountains used in the
controlled whole-seedling experiment.

 

No. trees No. trees

 

Site Morphotype Source tree available used
Mount Lemmon PIN_PON MTL 9 35 9
MTL 5 38 9
MTL 24 42 9
MTL 23 38 9
MTL 15 24 8
MTL 132 16 6
MTL 11 35 9
MTL 104 34 9
Rose Canyon PIN_ARI RC PINARI GroupPicnic 90 8
Lizard Rock PIN_ARI LIZ 1 24 8
LIZ 3 20 14
LIZ PINARI 353 from LIZ 2 40 19
LIZ PINARI 61.5cm dbh 53 19

 

Measurement of cold-induced injury throughout the acclimation and

deacclimation process would yield a complete picture of differential seasonal

125

susceptibility to cold, or cold tolerance. However, in ponderosa pine, Burr et al.
(1990) observed little difference in tissue hardiness prior to acclimation and large
differences during deacclimation. Due to limited numbers of seedlings available
for this experiment, investigation of cold-induced injury was restricted to three
periods corresponding to the hardening, full acclimation, and deacclimation
stages.

The seedlings underwent simulated field conditions for hardening, winter,
and dehardening (Table 4-2) following the methods of Burr et al. (1989).
Seedlings were hardened for 7 weeks on short-days (SD, 10-h light, 300 umol m'
2 s") at 10°l3° C in a standard walk-in growth chamber (TCZ controller,
Environmental Growth Chambers, Chagrin, OH). Winter conditions were
imposed at 5°/-3° C SD (280-340 umol rn'2 s") for 4 weeks in a controlled growth
chamber (Model GC-20 BDAF, ENCONAIR, Ecological Chambers, Inc.,
Winnipeg, Manitoba). Dehardening occurred under long-days (16-h natural light
extended by sodium vapor bulbs) at 16°l25° C in a greenhouse. When needed,

seedlings were equally watered at all stages with distilled water.

Table 4-2. Conditions for hardening, acclimating, and deacclimating PIN_PON and
PIN_ARI seedlings for the whole-seedling freezing test. Relative timing and date for the
freezing tests in each period are noted in the last two columns.

 

 

 

 

Day Night Freezing test
Length Length Temp temp
Stage (wk) Date (h) (° C) (° C) Week Date
Hardening 7 22Dec-9Feb 10 10 3 5.4 29Jan
Winter 4 9Feb-9Mar 10 5 -3 10 23Feb
Deacclimation 2 9-23Mar 16 25 16 14 23Mar

 

126

Cold injury was inflicted by whole-plant freezing at 5.4, 10, and 14 weeks
(Tables 4-2), and response to freezing was measured by before-and-after dark-
adapted chlorophyll fluorescence (DACF), freezing-induced electrolyte leakage
(FIEL), and visual scoring of needles, buds, and cambia. Twelve hours prior to
starting each test, 20 randomly selected seedlings from each species were
watered to field capacity to reduce effects of different plant—water status (J. Flore,
personal communication), and attached needles from 3-4 fascicles from every
plant were carefully marked with small pieces of tape at the fascicle sheath. PSII
quantum efficiency, or DACF, was measured from the taped needles with a
pulse-amplitude modulated fluorometer (6400-40 Leaf Chamber F Iuorometer, LI-
COR Biosciences, Inc., Lincoln, Nebraska, USA) attached to the base Ll-COR
6400 Portable Photosynthesis System. The fluorometer was calibrated before
the first set of measurements, zero-ed before each set, and adjusted to
recommended settings: Saturating pulse (flash) - 0.8 s saturating multiple flash
of ~8800-9000 umol m'2 s", 20KHz modulation, and 50 Hz averaging filter; no
Dark pulse since dark-acclimated needles were measured; and Measurement
light - intensity 2, 0.25 KHz modulation, 1Hz averaging filter, and 10 gain factor
(Ll-COR 2005a). Taped needles were then scored under fluorescent lighting for
color (hue, value, and chroma; Munsell Tissue Color Chart, 1977 ed.) as in
Boorse et al. (1998). Aftenrvard, the seedlings were moved from the growth
chamber (or greenhouse for the deacclimation treatment) to a completely dark
room (20 °C) for at least 30 minutes before DACF was remeasured from the

same taped needles to determine if DACF is a function of temperature or

127

photosynthetic down-regulation associated with physiological acclimation (B.
Pratt, personal communication). In both the Hardening and Acclimation stages
(Table 4-2), DACF was significantly (paired t-test, df=19, p<0.05) higher when
measured at room temperature, thus all analyses use values recorded at room
temperature.

The 20 selected seedlings from each species were then moved directly to
a programmable (Model CN-2042 TC, Omega Engineering, Inc., Stamford,
Connecticut, USA) large-capacity freezer. The freezing rate, duration at test
temperature, and rate of thaw were meant to simulate conditions experienced by
rooted seedlings in the field during extreme cold events. Sixteen of the seedlings
per species were randomly arranged within the freezer, a fan at each end of the
freezer circulated air, and at least 10 thermocouples were distributed in the
seedling crowns throughout the freezer to monitor temperature distribution and
level; four seedlings per species served as controls and were moved to a cold
room (1° C). The ambient freezer temperature was lowered rapidly to 0° C and
then at a rate of 5° C h'1 (Cannell and Sheppard 1982, Burr et al. 1989) to each
test temperature. The same amount of damage is incurred between 1 and 4 h at
a given freezing temperature (Cannell and Sheppard 1982); consequently, after
the average crown temperature stabilized for 2 h at each of four test
temperatures (Table 4-3), four seedlings of each species were quickly removed
from the freezer, placed in a cooler with ice packs, and immediately transported

to the cold room (1° C) to thaw (Burr et al. 1989).

128

Table 4-3. Temperature profiles for each of the freezing tests outlined in Table 4-2. The
bolded steps are those times after which four seedlings from each species were
removed from the freezer.

 

 

 

Hardening Stage Winter Stage Deacclimation Stage
Step Duration (h) Temp (°C) Duration (hL Temp (°C) Duration (h) Temp (°C)
00 ~1 :30 Down to 0 ~1:30 Down to 0 ~1:30 Down to 0
01 1:00 0 1:00 0 1:00 0
02 3:00 -15 2:00 -10 0:36 -3
03 2:10 -15 2:10 -10 2:10 -3
04 1:00 -20 1:00 -15 0:36 -6
05 2:10 -20 2:10 -15 2:10 -6
06 1 :00 -25 1 :00 -20 0:36 -9
07 2:10 -25 2:10 -20 2:10 -9
08 1 :00 -30 1 :00 -25 0:36 -12
09 2:10 -30 2:10 -25 2:10 -12

 

After thawing for 12-21 h, depending on relative time of removal from the
freezer, the seedlings were moved to the laboratory (~20° C) for the FIEL
measurements. Two-four healthy-appearing and non-taped fascicles from each
tree were excised at the base of the fascicle sheath with a razor blade and then
cut into 1-cm segments on a dry glass plate. The seedlings were moved to the
greenhouse (described above). The cut segments were quickly rinsed in
deionized (Dl) distilled water (NANOpure Ultrapure Water System, Model D4741,
Barnstead International, Dubuque, IA, USA) in a Petri dish, and then 40
segments were carefully removed with forceps and placed into a 55-mL culture
tube (25x150 mm, Pyrex No. 9826, Coming Incorporated Life Sciences, Lowell,
MA) containing 10 mL of DI water. A pilot test of seedlings frozen down to -15° C
indicated that 20 segments produced an electrical conductivity (EC) reading 3.7
times lower than the EC meter’s calibration setting (447 118), thus doubling the
number of segments would safely increase the absolute range and precision of

EC measurements. The tubes were capped, vortexed, incubated for 12 h in 20°

129

C distilled water, vortexed again, and measured for initial EC (CON 410, Oakton
Instruments, Vernon Hills, IL). The EC meter was recalibrated using standard
calibration (447 p8) solution before every set of measurements. With caps
loosened 45° to prevent explosion, the tube were incubated at 90° C in an oven
for 2 h to Iyse the cells and then for 12 h in 20° C water before the maximum EC
(Ritchie 1991) was measured. Marked tubes did not lose any noticeable solution
to evaporation during the incubation. The relative conductivity (RC) of the
solution was calculated as the initial conductivity value divided by the maximum
conductivity value (Ritchie 1991).

The seedlings in the greenhouse were evaluated at both 3 and 7 days
following the freezing test by DACF and visual scoring to assess any recovery of
photosynthetic function. Bud and cambia were evaluated for visible tissue injury
7 days following the freezing test (Burr et al. 1989). In natural light conditions,
apical buds were removed by razor and cut longitudinally to determine if the bud
were live (bright green) or dead (dull green or brown); percent mortality was
calculated to compare against freezer temperature. Seedlings were measured
for length from root-collar to base of apical bud, and most of the stem was
shaved with a razor to expose cambium. Live cambium, using the control trees
as a standard, was very bright green, while functionally dead cambium was dull
green or brown. The length of stem with dead cambium was measured and
converted to percent injury relative to the total length of stem. Determination of
live or dead bud and cambium requires close inspection, expertise, and

conference with an experienced person; L. Sage (Department of Horticulture,

130

MSU) provided the initial calibration, while F. Telewski assisted with every bud
and cambial scoring test.

PSII quantum efficiency (DACF), freezing-induced electrolyte leakage
(RC, relative conductivity), cambial and bud mortality, and change in Munsell
value after freezing were modeled using analysis of variance (ANOVA) in R (R
Development Core Team 2007). The full models included the main factors of
species and freezing treatment, as well as their interaction. For each phase, the
critical temperature (LTso) was estimated for each response variable, except
change in Munsell value, by visual interpolation from plots of the fitted values
from the full model (‘predict’ function; R Development Core Team 2007). The y-
axis value for estimating LT5o was determined as one-half of the mean species
response in the control trees at 3° C for DACF and RC, while 50% was used for

cambial and bud mortality.

Observational whole-seedling freezing test

In fall 2004, the nongovernmental organization named Trees for Mount
Lemmon (contact: Barbara Eisele) donated 391 two-year-old “ponderosa pine”
seedlings for our research purposes. The seeds were originally collected within
the Santa Catalina Mountains (exact locations not recorded) by a private
contractor for Coronado National Forest to replant areas affected by the Catalina
Highway reconstruction (B. Eisele, personal communication) and grown in
greenhouses near Flagstaff (W. Hart, personal communication). In early

November 2004, I retrieved the potted (n=3-7 seedlings per small pot) seedlings

131

from outdoor storage at high elevation (Summerhaven School and Radio Ridge),
counted the number of needles in every fascicle (2003-2004), labeled each
seedling with morphotype (sensu Peloquin 1984) and a unique identifying
number, and barerooted all seedlings with roots covered by moist paper toweling
and packed into plastic bags in cardboard boxes. The 6 wardrobe boxes were
shipped ground freight to Michigan State University. All seedlings were then
potted within 2 days of arrival in a standard conifer soil mix (Fafard52, Conrad
Fafard, lnc., Agawam, MA) in 2-gal plastic pots.

The seedlings were kept outdoors for about one week and then moved to
a greenhouse heated sufficiently through the winter to prevent ice formation in
the water pipes. Daytime temperatures inside the greenhouse during the winter
were usually 4-10° C, but high solar radiation raised the temperature to ~20° C
on occasion. Through spring, daytime temperatures inside the greenhouse
reached 32° C; trees were generally watered as needed, but high seedling
mortality in the greenhouse suggested that watering had been insufficient and/or
daytime temperatures had been too high. On 21 May 2005, all seedlings were
moved outside the greenhouse, with supplemental watering as needed; survival
was high during the summer. Temperatures remained above freezing until mild
frost on 27-29 October and 14 November (NCDC 2007). Beginning 22
November, nighttime (except 29 November) and daytime (except 28-29
November) temperatures stayed below 0° C, even reaching -11° C on 25

November (NCDC 2007). Consequently, soil water in the pots had frozen

132

diurnally for about a week and then likely remained frozen for at least 2 days
before measurements of PSII excitation capture efficiency began.

Light-acclimated chlorophyll fluorescence (LACF; Fv’lFm’) was measured
from every living seedling as they were moved into the heated (f =13° C)
greenhouse from 2-5 December 2005. Seedling selection was determined by a
disinterested assistant, thus the order of moving trees into the greenhouse for
measurement was not advertently biased by morphotype. LACF was measured
by the Ll-COR fluorometer, as described in the first experiment, but a 6-s dark
pulse was added to measure Fo' with the following additional settings: far red
intensity of 8 turned on 1 s before and remain on for 1 5 beyond actinic light off,
0.25 KHz modulation, and 1 Hz averaging filter (LI-COR 2005a). As before, the
fluorometer was calibrated with a standard before the first set of measurements
and zero-ed before each set. After securely clamping a sufficient number of non-
overlapping healthy needles to completely cover the aperture in the fluorometer,
measurements were initiated after base fluorescence had stabilized (dF/dT<5; Ll-
COR 2005a). Temperature inside the cuvette was simultaneously measured by
a thermocouple.

LACF was modeled with morphotype as a fixed effect and temperature as
a covariate by analysis of covariance (ANCOVA) in R (R Development Core
Team 2007). Afterward, temperature was removed from the model and LACF
was fitted to morphotype in a one-way analysis of variance (ANOVA). Given the

unbalanced design, Type II sums of squares (SS; Langsrud 2003) were used to

133

test hypotheses using the ‘car’ (Companion to Applied Regression; Fox 2006)

package in R (R Development Core Team 2007).

Small trees on the mountain

Response to natural variations in cold temperatures was measured for
small PIN_PON and PIN_ARI trees in the Santa Catalina Mountains in January
2006. Since the small-diameter trees would have been severely damaged by
coring, their ages were not determined; size does not correlate well with age for
semiarid mountainous trees (T. Harlan, personal communication). However,
standardizing for size across sites (elevation) should reduce effects related to
ability to acquire water and to tolerate above-ground climate conditions.

Trees selected for winter measurements at the MTL (n=3 PIN_PON),
Palisade Rock (“PAL,” 32.4138°N, 110.7149°W (NAD83/WGSB4), 2475 m
elevation; n=4 per species), and LIZ (n=3 PIN_ARI) sites were part of ongoing
research discussed elsewhere in this dissertation. Trees at each site were
measured within one day over 24-27 January. For each tree at both predawn
and midday, the ratio of variable to maximum chlorophyll fluorescence (Fv/Fm and
Fv’lFm’, respectively) was measured from the same attached 2005 needles
(recently taped at the fascicle sheath) on the lowest living branch with a LI-COR
fluorometer calibrated with a standard before the first set of measurements,
adjusted to settings used in the previous experiment, and zero-ed before each
set of measurements. Net photosynthesis (A) was simultaneously measured

during the midday fluorescence measurements (LACF). At predawn, DACF was

134

measured once per tree, while LACF and A were measured 3-4 times from the
same needles and averaged. Predawn and midday xylem water pressure
potentials (l-Pp) were measured from detached 2005 needles from the same
branch as the fluorescence measurements. WP give an accurate estimate of
plant water status (Kaufmann 1968, Ritchie 1975), especially diurnal changes in
status, and has been used to asses cell rupture by freezing (Brown et al. 1972).
Five-ten fascicles per tree were cut at the base of the fascicle sheath with a razor
blade, immediately placed in a small resealable plastic bag containing moist
paper toweling, and stored in a cooler with ice packs prior to measurements.
Within 2-6.5 h, each fascicle was cleanly cut with a razor ~1 cm from the
basipetal end of the fascicle sheath, inserted into a rubber grommet, and
pressurized with N2 gas in a pressure chamber (Model 1000, PMS Instruments,
Corvallis, Oregon, USA) to determine the xylem pressure potential.
Measurements were taken from 3-6 fascicles from each tree and averaged by
observer and replicates to obtain a mean Wp per tree.

DACF and LACF were modeled with site and species as fixed factors and
temperature as a covariate by ANCOVA in R (R DeVelopment Core Team 2007).
Aftenrvard, temperature was removed from the models, and DACF, LACF, lllp,
and A were modeled using two-factor ANOVA in R (R Development Core Team
2007). The full models included the main fixed factors of site and species, but
interaction was not included due to the small sample size at MTL and LIZ. Given
the unbalanced design, Type II SS (Langsrud 2003) were used to test the

ANOVA hypotheses using the ‘car’ (Companion to Applied Regression; Fox

135

2006) package in R (R Development Core Team 2007), while the default Type I

SS were used for the ANCOVA tests.

Results
Controlled whole-seedling freezing test

Freezing caused significant damage to the seedlings, especially beyond
threshold temperatures (Figures 4-1 and 4-2). The threshold was most obvious
during the Hardening stage when the difference between the controls (3° C) and
the first freezing treatment (~15° C) was greater than for the other stages; this
difference is likely due to a lack of temperature treatments closer to zero during
the Hardening freezing test. In the ANOVAs, the temperature treatments were
significant (p<0.0004, but p<0.002 for change in Munsell value) for all response
variables, but the interaction of temperature treatment and species was not
significant (p>0.523, but p>0.3306 for change in Munsell value). Given the lack
of pattern and precision in the change in Munsell value, these data will not be
reported.

Little difference in response by species was observed within the
Hardening and Deacclimation phases, except for a larger degree of cambial
mortality in PIN_ARI seedlings from the -15° C treatment during the Hardening
stage (Figure 4-2). The largest apparent species difference occurred during the
Winter stage. In this stage, PIN_PON had higher DACF in every temperature
treatment group (Figure 4-1) and lower cambial and bud mortality in the -15° and

-20° C freezing treatments (Figure 4-2). With the exception of the -3° C

136

treatment, PIN_ARI also had higher cambial and bud mortality in the -15° and -
20° C freezing treatments during the Deacclimation stage (Figure 4-2). However,
the ANOVAs indicated that the only significant (p<0.05) difference between

species is the amount of cambial mortality during the Winter stage (Table 4-4).

Table 4-4. ANOVA results for dark-acclimated chlorophyll fluorescence (DACF), relative
conductivity (RC), and cambial (Camb) and bud (Bud) mortality for PIN_PON and
PIN_ARI seedlings exposed to freezing treatments (Tx) during the acclimation phase.
Significant (a<0.05) terms are bolded.

 

 

Response Adj Rz F(p-value) Term Df SS F-value P-value

DACF 0.7111 1.96E-10 Species 1 0.0904 2.3771 0.1319
Tx 1 3.6766 96.6332 9.80542
Species'Tx 1 0.0001 0.0037 0.9517
Residuals 36 1.3697

RC 0.708 4.36E-10 Species 1 0.0113 0.9321 0.3409
Tx 1 1.1360 94.0674 1.88E-11
Species*Tx 1 0.0015 0.1202 0.7309
Residuals 36 0.4227

Camb 0.6135 3.48E-08 Species 1 0.4655 5.4571 0.0252
Tx 1 5.0310 58.9809 4.3E-09
Species*Tx 1 0.0401 0.4707 0.4971
Residuals 36 3.0708

Bud 0.3182 0.0007 Species 1 0.2250 1.2903 0.2635
Tx 1 3.4490 19.7786 8.025-05
Species*Tx 1 0.0234 0.1343 0.7162
Residuals 36 6.2776

 

Seasonal effects, or differences between acclimation stages, on response
to freezing temperatures can be demonstrated by comparing the regressions of
the response variables against treatment temperature for all stages (Figure 4-3).
Although the expected response is sigmoidal, the R2 for the linear regressions
were generally high (3? =0.81, s=0.12). The interpretation of these plots is
simpler when summarized by plots of slope and intercept against time during the

acclimation process (Figure 4-4); no standard errors are associated with these

137

plots, thus significance testing was not performed. In general, slope values are
related to the degree of hardening, or sensitivity to freezing; larger absolute
values of slopes indicates a stronger response to freezing. In other words,
slopes should converge to zero when the plant tissue has thoroughly hardened.
Intercepts indicate the degree of injury expected at 0° C; i.e., the difference
between intercepts suggests the (genetic) difference in cold tolerance between
species.

Slope values were similar for both species across the Hardening and
Winter stages but were steeper during Deacclimation (Figure 4-4). This suggests
that both species were close to the degree of hardening during the first stage as
compared to the second stage, but both species were functionally deacclimating
during the third stage and thus becoming more sensitive to freezing
temperatures. There was no species difference in sensitivity to freezing as
measured by DACF and RC, but cambium in PIN_PON appeared to be more
sensitive across stages than in PIN_ARI. However, PIN_ARI buds were more
sensitive than PIN_PON buds during the Winter and Deacclimation stages.

As for the slope values, the intercepts for the two species in the DACF and
RC plots were similar, with PIN_PON having slightly higher PSII quantum
efficiency and vaguely lower electrolyte leakage than PIN_ARI during the
Hardening and Winter stages (Figure 4-4). The hump-shaped DACF curve for
PIN_PON is considered anomalous because 2 seedlings had relatively high
DACF following the -10° C during the Winter stage and thus biased the

regression curve (Figure 4-3). With significance (p=0.025) calculated by ANOVA

138

(Table 4-4), PIN_ARI seedlings had much higher cambial mortality than
PIN_PON seedlings; furthermore, mortality increased through the acclimation
process, peaking during the Deacclimation stage, like PIN_PON. Bud mortality
also increased for both species through the acclimation process, with PIN_ARI
seedlings experiencing higher bud mortality during the Winter stage than
PIN_PON seedlings.

Table 4-5 shows the results of the second approach to examining species
differences in cold tolerance, with LTso’s interpolated from the fitted values from
the ANOVAs. The mean LTso’s for whole PIN_PON seedlings across the three
stages were -12.6, -12.9, and 96° C, while the mean LT5o’s for PIN_ARI
seedlings were -11.1, -8.4, and -7.6° C. PIN_PON was consistently more cold
tolerant across measured variables than PIN_ARI, especially during the Winter
(A=-4.4° C) when both species should have been fully hardened. The rank-order
of variables according to their insensitivity to freezing (i.e., lower LT50) changed
from buds, cambia, and needles (DACF/RC) during Hardening and Winter to
buds, needles (DACF/RC), and cambia during Deacclimation. The species
differences are generally supported by the previous method. PIN_PON had
lower LT5o's for loss of PSII quantum efficiency during Hardening and Winter, for
electrolyte leakage and cambial mortality during all stages, and for bud mortality

during the Winter stage.

139

       
  
 

—o— PIN_PON

—e— PN_PON \

 

 

 

 

--B—-PIN_ARI 0.1 * ——a—-PIN_ARI 0" 1
Y {31‘} ' ir fir fir f 0% 1
-40 -30 -2o -10 o 10 40 so -20 -10 o 10
o 9

-—e——- PIN_PON
— —a - — PIN_ARI

 

I —e— PIN_PON

  

 

 

 

 

 

   

 

 

 

 

r 0.1
l 01* --B--PIN_ARI l
I
I
. as A fl . . f a . 9e .
.30 -25 .20 .15 .10 .5 o 5 -30 .25 -2o -15 .10 -5 o 5
o 9
o a
I I a
0.7-1' ’ '
”#0
—o—PIN_PON ———o——-—PIN_PON 0,1 .
--B—-PIN_ARI -—B—-PIN_ARI
‘ - as fl
nn
'15 ‘10 '5 ° 5 -15 -1o —5 o 5
Terrperature (° C) Terrperature (°C)

Figure 4-1. Dark-acclimated chlorophyll fluorescence (left) and relative conductivity
(right) for PIN_PON and PIN_ARI after different freezing treatments across three stages
of cold acclimation: Hardening (top), Acclimation (middle), and Deacclimation (bottom).
Note the varying temperature scale between phases.

140

 

\ so
\\ w
\ 70
\ so
it 5.
\
\ 4o
\
\
so
\
—c— PIN_PON - Carib \ 2°
—l:i— PIN_ARI - Cunt: \

+PN_PON - Bud \ 1o

— I- - PN_AR| - Bud \3

= o
35 so 25 -2o 15 10 s o 5

   
  

~c— PN_PON - Cunt)
—D— PN_AR| - Cami:
—O—PIN_PON - Bud
— I- - PN ARI- Bud

 

 

-30 -25 -20 -15 -10 -5 0 5

 

100
so
so
70
so
so
40

.r w

+ PIN_PON - Camb - -

—CI—-— PIN_ARI - Camb " 3°

—e— PIN_PON - Bud 10

— I- - PIN_ARI - Bud
o

 

-15 -10 -5
Ten-persure (’ C)

Figure 4-2. Cambial and bud mortality for PIN_PON and PIN_ARI after different freezing

treatments across three stages of cold acclimation: Hardening (lap). Winter (middle),
and Deacclimation (bottom). Note the varying temperature scale between phases.

141

  
 
  

 

 

 

 

 

<> PIN_PON - Hardening - y = 0.0261x + 0.6365. R2 = 0.8543
PIN_PON - Winter - y = 0.0308x + 0.7797. R2 = 0.8981 ‘* 0-9
e PIN_PON - Deaccimaiion - y = 0.0516x + 0.5725. R2 = 0.8384 _//<> 0.8 g
D PIN_ARI - Hardening - y = 0.0237x+ 0.5798. R2 = 0.8617 0 7 g
' O
PIN_ARI - Winter - y = 0.0297x + 0.6322, R2 = 0.8928 g
I PIN_ARI - Deaccimalion - y = 0.0575x + 0.6373, R2 = 0.8639 0.6 E A
E
0.5 if, g
‘8 “c.
0.4 B
0.3 E
3?
0.2 E
a
o
0.1
a 1 0.0
-35 -30 -25 ~20 -15 -1o -5 o 5
Temperature (°C)
~ 0.8
‘\\ x '1' 0.7
9 we a~
\ “- \ I
‘~ 9 .
- \\ I r 0.5
\ \ \ e \ \o\ g.
x ‘ ~ \ .2
\ x ‘ \D \ ‘ 0.5 g
\ \ O x ‘ ‘2
‘.\ ‘ ~ ~. \ 3
\ \ ;‘ \ ‘ ‘\ \ .0- o 4 g
o PIN_PON - Hardening -y= -0.017x+ 0.2186, R2 = 0.9786 \ \ “~\ g
\ ‘\
PIN_PON - Winter - y = -0.017x + 0.1646, R2 = 0.8658 " \ \ x ‘ -\ t 03 a:
\ .
O PIN_PON - Deacclimation - y = 00344;: + 0.2285, R2 = 0.8242 \ \ . t ‘ 0 2
. \ . x _
i3 PIN_ARI - Hardening - y = -0.0172x + 0.2311, R2 = 0.9773 5 \
PIN_ARI - Winter - y = 00183:: + 0.1764, R2 = 0.8734 ’ 0.1
I PIN_ARI - Deeccimation - y = 00395x + 0.2168, R’ = 0.8246
I T T I l T r I 1 0.0
-35 -30 -25 ~20 -15 -10 -5 0 5

Temperature (°C)

Figure 4-3. Linear regression of dark-acclimated chlorophyll fluorescence, relative
conductivity, and cambial and bud mortality against treatment temperature for PIN_PON
(solid line) and PIN_ARI (dashed line) across three stages of cold acclimation:
Hardening (thin line), Winter (normal line), and Deacclimation (thick line). Note the
varying temperature scale between phases.

142

PIN_PON - I-hrdening - y = -3.52x- 0.5041, R2 = 0.6912
PIN_PON - Winter- y = -3.9142x - 4.1281, R2 = 0.7454

PIN_PON - Deacclimation - y = 4.7165x + 41.922, R2 = 0.8336

PIN_ARI - Hardening -y= -3.1349x+16.912, R2 = 0.8971

PIN_ARI - Winter - y = -3.2172x + 30.787, R2 = 0.8609

PIN_ARI - Deacclimation - y = 4.3392x + 51.929. R2; 0.6226

   

 

 

<> PIN_PON - Hardenino- y = -3.433x - 4.735, it2 = 0.7242
- PIN_PON - Winter - y = -2.6995x + 3.827, R2 = 0.4074

7

-30

   

T

-25

Y

-20

V

-15

Temperature (°C)

-10

 

-35

 

Q p|N_poN - Deacclimation - y=-3.433x-4.735, R2 = 0.7242 \ .
CI PIN_ARI - Hardening - y = 4.556x + 34.826, R2 = 0.7999 ’ \
\ a
pm AR. - Winter - y = -3.2242x + 15.796, R2 = 0.7834 _ \ \
I pm AR. _ Deaccmnafion - y = -5.9685x + 32.77, R2 = 0.8252 ‘
- m \ n
y 1 T V l..] I v
-30 -25 -20 -15 -10 -5 0
Temperature (°C)

Figure 4-3. Continued.

143

100

100

90

80

70

60

50

40

Cambial mortality (%)

Bud mortality (%)

    

 

 

 

 

 

 

 

0.07 1 «r 0.9
«r 0 8
A 0.08 -
‘83 1. 07
<
e 0.05 <
i 0.04 i . o 5
.9
a
5 0.03 J " 0"
8 ----- 8’ PIN PON-Slope 1 03
‘5 0.02 1 " I
_§- 0 PIN_ARl-Slope - 0.2
V)
°-°1 ‘ o PIN_PON- ntercept, 01
0 . 5 PIN Ant-intercept (o
0 5 1o 15
Weeks since Dec 22
0 5 10 15
0 . 1 60
0 PN_PON - Slope .
A 1:1 PN_ARI- Slope / ’ 5°
‘5’ -1 : /
8 O PN_PON - meroept /
I ..
.2 -2 1 3°
g » 20
8 '3 i 10
‘5
§ .. o
(7) 41
-10
5 ~ -20

Intercept of reoression lines (DACF)

Intercept of rearession lines (Cambl

Slope of regression lines (RC)

Slope of regression lines (Bud)

Weeks since Dec 22
5 10

15

 

-0.02 *

 

   

0.25

.0
N

1- 0.1

A
r
O
_.

 

 

o PIN_PON-Slope
1:1 PIN ARI-S
-003 1 ' b”
e PIN_PON- intercept
I PIN_ARI- htercept \
-004 — Cl
0
o
o
-11 D
-21 O
I
_3 -1
_4 4
_5 -4
-6 ..

 

 

 

5

o
8

Intercom of regression lines (RC)

Intercept of regression lines (Bud)

Figure 4-4. Slope (thin line) and intercept (thick line) for regressions of dark-acclimated
chlorophyll fluorescence, relative conductivity, and cambial and bud mortality against

treatment temperature for PIN_PON (solid line) and PIN_ARI (dashed line) across

weeks of cold acclimation. Note the varying axis scales across graphs.

Table 4-5. Cold tolerance (LTso) of PIN_PON and PIN_ARI estimated as a function of
needle dark-acclimated chlorophyll fluorescence (ndl - DACF), needle relative
conductivity (ndl - RC), and cambium and bud mortality across three stages of cold
acclimation: Hardening, Winter, and Deacclimation.

 

Hardening

Winter

Deacclimation

 

Response PIN_PON PIN_ARI PIN_PON

PIN_ARI PIN_PON PIN_ARI

 

Ndl - DACF -11.25° C -10.0° C -12.25° C

Ndl - RC -9.0 -8.0 -8.5
Cambium -14.25 -10.5 -13.75
Bud -16.0 -16.0 -17.0

-8.5° C

-7.25
-6.75
-11.25

-12.5° C -12.25° C

-9.25 -6.0
-6.25 -2.5
-10.5 -9.75

 

144

Observational whole-seedling freezing test

The quantum yield of open PSII reaction centers, measured by LACF
(Fv’lFm’), varied from 0.07 to 0.399 with a mean of 0.270 (s=0.053) across all
seedlings (n=163) (Figure 4-5). Most of the data are slightly lower than the
theoretical overall quantum yield of unstressed plants under incident light
(Fv’lFm’=0.3-0.35; Schreiber et al. 1994). Mean (:80) quantum yields for
PIN_PON, Mixed, and PIN_ARI were 0.282 (1:0.051), 0.255 ($0.051), and 0.241
(1:0.057), respectively. Air temperature when the seedlings were measured
(p=0.105) and its interaction with morphotype (p=0.524) did not significantly
affect LACF in the ANCOVA (Table 4-6). However, there was a highly significant
(p=0.0009) effect of seedling morphotype on the quantum yield. Pain/vise
comparison across morphotypes using Tukey’s HSD from a one-way ANOVA of
morphotype indicated that LACF measured from PIN_PON seedlings was
significantly higher than from both Mixed (p=0.023) and PIN_ARI (p=0.001), but
LACF measurements from Mixed and PIN_ARI were not significantly different

(p=0.569).

145

0.5 1

 

 

 

 

<> PIN_PON- y = 0.0012x + 0.2644
0 Mixed - y = 0.0002x + 0.2376
0.4 1 o 0
CI - =
PIN_ARI y 0.0038x+ 0.1461 O o 00 o
E 0.3 4
it:
'63
5 0.2 +
0.1 1
o
0 I r T I
0 5 10 15 20

Temperature (° C)

Figure 4-5. PSII quantum yield (LACF) as a function of air temperature when measured
from 3-year-old potted seedlings with frozen soil water: PIN_PON (solid line), Mixed
(dotted line), and PIN_ARI (dashed line). Coefficients are from full ANCOVA model.

Table 4-6. Type II tests of ANCOVA for PSII quantum yield (LACF) fitted to morphotype
and the covariate air temperature (Temp) for 3-year-old potted seedlings with frozen soil
water. Significant (a<0.05) terms are bolded.

 

 

Adj R2 F(p-value) Term Df SS F-value P-value

0.0876 0.0016 Morphotype 2 0.0372 7.3279 0.0009
Temp 1 0.0067 2.6570 0.1051
Morphotype *Temp 2 0.0033 0.6481 0.5244
Residuals 157 0.3980

 

Small trees on the mountain

The potential maximum PSII quantum yield, measured as DACF (Fv/Fm),
varied from 0555-0746 across all sites, with the highest means recorded at MTL
and LIZ, while the realized PSII quantum yield under incipient light, measured as
LACF (Fv’lFm’), ranged from 0300-0509, with the trees at the intermediate PAL

site exhibiting the highest Fv’lFm’ readings (Figure 4-7). The values of DACF

146

were larger than of LACF because the PS reaction-centers are not fully oxidized
thus increasing the relative minimal yield (F0) and heat dissipation, which reduces
maximum fluorescence (Schreiber in Larcher 2005). Consequently, the ratio of

the difference between F0 and Fm over Fm decreases accordingly.

 

 

0.8 1
Q . I.
0.7 — '
o
0.6 - ' .I i
8 o
8 0 5 <> 0
t5 ' a o
O
3 0.44 <5 “7743““
E ~~~~~~~~ 1:11.
a. O E]
g 0.3 ~ 0
5 o PIN_PON - predawn - y = -0.0039x + 0.6779, R2 = 0.0164
0.2 -
I PIN_ARl - predawn - y = -0.0043x + 0.6764, R2 = 0.0201
0.1 T o PIN_PON-midday-y=0.0048x+0.3957,R2=0.0102
n [:1 PIN_ARI-midday-y=-0.0111x+0.5054, R2=0.7343
-2 0 2 4 6 8 10 12 14 16

Temperature (° C)

Figure 4-7. Variable chlorophyll fluorescence (FvlFm, solid; Fv’lFm’, empty) as a function
of leaf temperature for PIN_PON and PIN_ARI trees combined across sites in January

2006.

As a function of temperature, little difference in the ratio of variable to
maximum chlorophyll fluorescence is apparent between species within time
periods (Figure 4-7). However, the three midday measurements taken from
PIN_ARI trees at LIZ at lower elevation (and thus higher temperature) bias the
linear regression toward a negative slope (-0.0111) and larger intercept
(A=0.1097). Consequently, temperature was a significant (p=0.038) covariate for

the midday variable fluorescence measurements (Table 4-7). The interaction of

147

temperature and site were also significant terms for the midday xylem pressure
potential (W,,) and net photosynthesis (A) models. However, temperature varied
consistently by site, which generated 5-56 times higher SS than temperature
across all of the ANCOVA models, and was thus confounded site; consequently,

temperature was dropped from all further analyses.

Table 4-7. ANCOVA results for predawn dark-acclimated chlorophyll fluorescence
(pDACF), midday light-acclimated chlorophyll fluorescence (mLACF), predawn (ppsi)
and midday (mpsi) xylem pressure potential, and midday net photosynthesis (mphoto)
for PIN_PON and PIN_ARI trees at three sites (MTL, PAL, and LIZ) in the Santa
Catalina Mountains in January 2006. Significant (a<0.05) terms are bolded.

 

 

Response Adj R2 F(p-value) Term Df SS F-value P-value

pDACF -0.3344 0.9644 Species 1 0.0001 0.0332 0.8580
Site 2 0.0010 0.1353 0.8746
Temp(covariate) 1 0.0000 0.0103 0.9206
Species'Temp 1 0.0000 0.0010 0.9747
Site'Temp 2 0.0053 0.7112 0.5079
Residuals 14 0.0518

mLACF 0.5936 0.0037 Species 1 0.0045 3.6257 0.0777
Site 2 0.0331 1 3.3687 0.0006
Tem p(covariate) 1 0.0065 5.241 7 0.0381
Species'Temp 1 0.0009 0.7450 0.4026
Site'Tem p 2 0.0016 0.6595 0.5324
Residuals 14 0.0173

ppsi 0.5209 0.0106 Species 1 0.4541 9.3342 0.0086
Site 2 0.9304 9.5616 0.0024
Temp(covariate) 1 0.0412 0.8477 0.3728
Species'Temp 1 0.0097 0.1999 0.6616
Site'Temp 2 0.0158 0.1627 0.8514
Residuals 14 0.6811

mpsi 0.7777 0.0003 Species 1 0.4700 20.9632 0.0006
Site 2 0.9589 21.3854 0.0001
Tem p(covariate) 1 0.0170 0.7583 0.4001
Species'Temp 1 0.0004 0.0167 0.8992
Site‘Temp 2 0.2008 4.4782 0.0352
Residuals 14 0.2690

mphoto 0.6589 0.0012 Species 1 12.2521 13.8766 0.0023
Site 2 16.9186 9.581 0 0.0024
Temp(covariate) 1 0.3441 0.3897 0.5425
Species'Temp 1 3.3358 3.7781 0.0723
Site'Temp 2 9.1430 5.1 777 0.0207
Residuals 14 12.3610

 

148

Since the interaction of species and site was not calculated by R due to
the small number of trees (n=3) measured at each site, the resultant ANOVA
models contained only the fixed factors of species and site. Using all of the data,
the effect of species was not significant (p<0.05) in any model (Table 4-8). The
significant effect of site on 4", and A are apparent in Figures 4-8 and 4-9, with
trees at lower elevation having lower W9 and A. Predawn and midday Wp varied
significantly (paired t—test, t=5.1532, df=19, p=5.65E-05) across all sites, with the
largest difference observed within both species at PAL (Figure 4-8). However,
analysis of the midday LACF and A models indicated a few major outliers. An
MTL tree had 36% higher Fv’lFm’ than the other two trees, while a different MTL
tree had 53% lower A than the other two, and one LlZ tree had 4% of the A as
the other two trees. Despite consistent Fv’lFm’ readings, this LIZ tree had highly
variable A ()7 =0.253, s=0.759). When these data points were removed, the

species effect shifted slightly toward significance for both models (Table 4-8).

149

Table 4-8. Type II ANOVA results for predawn dark-acclimated chlorophyll fluorescence
(pDACF), midday light-acclimated chlorophyll fluorescence (mLACF), predawn (ppsi)
and midday (mpsi) xylem pressure potential, and midday net photosynthesis (mphoto)
for PIN_PON and PIN_ARl trees at three sites (MTL, PAL, and LIZ) in the Santa
Catalina Mountains in January 2006. The numeral “2" following a response name
indicates removal of outlier data points. Significant (a<0.05) terms are bolded.

 

 

Response Adj Rz F(p-value) Term Df SS F-value P-value

pDACF -0.1441 0.9483 Species 1 0.0001 0.0215 0.8851
Site 2 0.0010 0.1578 0.8852
Residuals 18 0.0571

mLACF 0.5189 0.0009 Species 1 0.0062 4.2386 0.0543
Site 2 0.0331 11.2944 0.0007
Residuals 18 0.0264

mLACF2 0.6782 4.892e-05 Species 1 0.0062 6.0578 0.02484
Site 2 0.0421 20.5039 2.95E-05
Residuals 17 0.0174

ppsi 0.5908 0.0002 Species 1 0.0084 0.2027 0.6579
Site 2 0.9304 11.1955 0.0007
Residuals 18 0.7479

mpsi 0.6981 5.13E-05 Species 1 0.0051 0.1668 0.6884
Site 2 0.9589 15.7453 0.0002
Residuals 18 0.4872

mphoto 0.4595 0.0026 Species 1 3.9908 2.8524 0.1085
Site 2 16.9186 6.0462 0.0098
Residuals 18 25.1839

mphotoZ 0.4673 0.0042 Specles 1 3.9908 5.9438 0.0268
Site 2 3.9617 2.9502 0.0812
Residuals 18 10.7429

 

150

MTL - PIN_PON PAL - PIN_PON PAL - PIN_ARI LIZ - PIN_ARI

 

 

 

 

 

I Predawn
El Midday

 

Xylem pressure potential (MPa)

 

Figure 4-8. Xylem pressure potential (l-Pp) from excised needles of PIN_PON and
PIN_ARI trees at three sites (MTL, PAL, and LIZ) in the Santa Catalina Mountains in
January 2006. Values are means; bars are :l:1 standard deviation.

 

 

 

 

Net photosynthesis (pmol m'2 s")

 

 

 

 

 

 

 

 

 

o r l 1 I
MTL - PIN_PON PAL - PIN_PON PAL - PIN_ARI LIZ- PIN_ARI

 

Figure 4-9. Midday net photosynthesis (A) results for PIN_PON and PIN_ARI trees at
three sites (MTL, PAL, and LIZ) in the Santa Catalina Mountains in January 2006.
Values are means; bars are :1 standard deviation.

151

Following removal of the outlying data points, the species difference in
midday LACF and A become more apparent (Table 4-9). PIN_PON trees had
higher potential and realized PSII quantum yield and net carbon acquisition,
while leaf water status was more favorable (i.e., less negative) than in PIN_ARI

trees. All responses indicate higher light and water efficiency by PIN_PON.

Table 4-9. Mean responses by PIN_PON and PIN_ARI trees by site (MTL, PAL, and
LIZ) and across sites (Combined) following removal of three outlying data values.
Responses listed in Table 4-8. Standard deviations in parentheses. Significant (p<0.05)
responses (Table 4-8) are bolded.

 

PIN_PON PIN_ARI
Response MTL PAL Combined PAL LIZ Combined
pDACF 0.68 (0.49) 0.66 (0.07) 0.68 (0.06) 0.67 (0.05) 0.66 (0.05) 0.66 (0.05)
mLACF2 0.32 (0.02) 0.46 (0.04) 0.43 (0.07) 0.35 (0.03) 0.42 (0.02) 0.40 (0.04)
ppsi -1.3 (0.3) -1.6 (0.1) -1.5 (0.2) -2.2 (0.4) -1.6 (0.1) -1.8 (0.3)
mpsi -1.3 (0.2) -1.9 (0.1) -1.8 (0.3) -2.3 (0.3) -2.0 (0.1) -2.1 (0.2)
mphoto2 6.2 (0.6) 6.0 (0.9) 6.0 (0.8) 3.4 (1.2) 5.0 (0.7) 4.7 (1.0)

Discussion
Control/ed whole-seedling freezing test

Differential cold tolerance of organs across the three “seasons” of cold
hardiness was expected (Sakai and Larcher 1987). In this study, needle tissue
was less cold tolerant than cambial and bud tissue during the Hardening and
Winter stages, while cambium became the least cold tolerant during
Deacclimation (Table 4-10). Bud tissue was the most hardy until Deacclimation
when its LTso was similar to that of needle tissue. In fact, little difference in
needle cold hardiness across seasons was observed, with species differences
only during the Winter stage (Table 4-5). In contrast, Burr et al. (1990) calculated

much lower LT50 values as well as a 3° C increase in needle cold tolerance from

152

hardening through simulated winter, and a subsequent 8° C decrease in cold
tolerance during deacclimation (Figure 4-10). Their LT50 was estimated by
interpolating 50% index of injury from an inverted, modified Gauss sigmoid model
of electrolyte leakage against test temperature (Burr et al. 1990), while this study
estimated LT50 for needles by interpolating the temperature from a linear
regression at 50% of the mean control (3° C) response. Low response values at
higher test temperatures increase the (-) slope (Figure 4-3), thus increasing the
interpolated LTso value, which could contribute to the higher LT50’s observed in
this study in comparison to Burr et al. (1990). Consequently, tissue and species

differences in LT5o’s are more informative than their absolute values.

Table 4-10. Cold tolerance (LTso) of combined PIN_PON and PIN_ARI tissues from this
study and from Burr et al. (1990) across three stages of cold acclimation: Hardening,
Winter, and Deacclimation. The Winter value for Southwestern ponderosa pine needles
for Burr et al. (1990) was interpolated between two values.

 

 

Response Hardening Winter Deacclimation
Needle -9.6° C 91° C -10.0° C
Needle (Burr et al. 1990) -18.7 [-22] -13.6
Cambium -12.4 -10.2 -4.4

Bud -16.0 -14.1 -10.1

 

The lower cold tolerance of needle tissue by both PIN_PON and PIN_ARI
during the Hardening and Winter stages suggests that hardening, and thus
photosynthetic downregulation, had not fully occurred. The PSII quantum
efficiency for the combined species (0.78) in the control treatments was close to
the theoretical optimum of 0.832 for healthy plants (Bjérkman and Demmig
1987); the mean value dropped to 0.71 during deacclimation. However,

PIN_PON had 4-6% higher PSII quantum efficiency in the first two stages and

153

then 10% lower efficiency during deacclimation than PIN_ARI in the control
groups (Figure 4-1).

Photosynthetic downregulation in conifers is inducted by a combination of
environmental influences, including day length, temperature, and radiation, in
concert with respiratory demand for processed carbon. If the plant remains
metabolically active, then reduction in photosynthesis becomes less sink
regulated. While the temperature and photoperiod in this study closely followed
Burr et al. (1990), l was unable to maintain the same light level. Consequently,
leaf-level light was ~300 pmol m'2 s'1 for the Acclimation and Winter stages, with
natural lighting exceeding this level during Deacclimation; in contrast, Burr et al.
(1989, 1990) sustained 518 pmol m'2 s", which is closer to the light saturation
value (600 umol m'2 s") reported by Helms (1972) for mature ponderosa pine
trees. Although pigment analysis was not conducted, I would expect little
conversion of V to A+Z. Instead, the seedlings in this study were
photosynthetically active to maintain respiratory carbon demand, thus the rate of
hardening relative to other tissues was delayed with slow recovery during
deacclimation (Table 4-5).

Resistance to freezing temperatures by the bud tissue was consistently
higher than by the cambium across all stages of cold tolerance (Tables 4-5 and
4-9). The method for examining but not measuring injury was the same across
these two tissues; response was continuous for cambium and binary for bud.
Consequently, the error distribution should have varied, yet the data were parallel

within species (Figure 4-2), suggesting only a difference in tissue cold tolerance

154

and not a problem with data analysis. Sakai and Larcher (1987) note that bud
resistance develops slightly later than needle resistance; this study found a 3-5°
C increase in resistance by needles than buds, again suggesting that
photosynthesis was not fully downregulated. Cambial tissue of PIN_ARI had
successively more injury across acclimation stages even in the control groups,
suggesting that sustained cold growing conditions can impose substantial

mortality in PIN_ARI seedlings.

Observational whole-seedling freezing test

Despite having severely reduced water supply due to the frozen soil, and
likely frozen stern storage, the 3-year-old seedlings had remarkably high PSll
quantum efficiency (5 =0.270, s=0.053). On a colder day (-8 to -10° C versus
mean maximum -2° C), Verhoeven et al. (1999) measured Fv’lFm’ for detached
ponderosa pine needles between 015-020. PSII quantum efficiency should
decrease with temperature, and ponderosa pine has been noted to retain some
photosynthetic capacity through winter (Marshall et al. 2001). However, given
the duration (week) of subfreezing maximum temperatures preceding this test,
extracellular and xylem freezing was expected to have reduced PSII quantum
efficiency beyond that observed in this study. Incipient light levels were not
measured, but the seedlings were exposed to at least diffuse bright to sunny
days. The “leaf temperature” plotted in Figure 4-5 represents that measured by
the thermocouple inside the fluorometer’s cuvette, which was representative of

air temperature inside the greenhouse. Seedlings were measured immediately

155

(i.e., within 1.5 min) as they were transferred to the lower-light greenhouse (13°
C); the time between outdoor and greenhouse temperature did not likely raise
inner leaf temperatures sufficiently to affect PSll efficiency. This supposition is
borne out by the lack of significance of the temperature term (Table 4-6).
Verhoeven et al. (1999) recorded an increase in the ratio F.,/Fm of 0.23 15 min
after moving samples from -23° C to room temperature, a much larger change in
temperature of approximately 43° C. Consequently, temperature likely had little
effect on PSII quantum efficiency observed in this study.

The significant finding from this test is that PIN_PON seedlings exhibited
higher PSII quantum efficiency than the Mixed and PIN_ARI seedlings (Table 4-
6). In addition, the Mixed trees, which may be hybrids between PIN_PON and
PIN_ARI (Peloquin 1984, Rehfeldt et al. 1996, Epperson et al. 2001), had a
mean PSII quantum efficiency intermediate to the two purported parent species,
although the difference to PIN_ARl was not significant (p=0.569). lnterrnediate
cold tolerance has also been observed in white x Himalayan blue pine hybrids

(Lu et al. 2007).

Small trees on the mountain

Removal of three significant outlier data points improved the
demonstration of differential realized PSII quantum yield and net carbon
acquisition by ‘small’ PIN_PON and PIN_ARI trees growing in the Santa Catalina

Mountains. Although the sample sizes were small (n=2-3) at the elevational

156

extremes, sufficient number of sympatric trees were present at PAL to detect a
species effect.

The pattern of net photosynthesis, in combination with the xylem pressure
potential and chlorophyll fluorescence data, across sites suggests different
limiting factors to survival during the winter in these mountains. The mean
maximum PSII quantum yields for both species were lower than recorded by the
hardening and acclimated seedlings in the controlled laboratory experiment, but
similar to the deacclimating seedlings. This suggests that the Santa Catalina
Mountain trees were more innately hardened but yet able to utilize light
resources during the day. The PIN_ARI trees at LlZ had higher net
photosynthesis but also larger change in needle water status than the PIN_ARI
trees at PAL; the higher temperatures lower in elevation stimulate higher rates of
respiration and thus demand for carbon gain, despite the more xeric conditions at
LlZ. The PIN_PON trees at the high-elevation MTL site also exhibited higher net
photosynthesis than those at PAL but had much lower realized PSII quantum
efficiency with no change in needle water status; these trees are likely much
more hardened than those at PAL, suggesting a strong site effect (Table 4-7).
However, gas exchange is occurring, as evidenced by substantial net
photosynthesis and diurnal increases in xylem pressure potential (Table 4-9).
Most obvious when compared at PAL, PIN_PON trees have higher PSII
excitation capture efficiency, higher net photosynthetic rates, and smaller
changes in needle water status, a critical component to surviving winter

desiccation (Sakai and Larcher 1987), than do PIN_ARI trees.

157

Implications of differential cold tolerance on distribution

All of the experiments in this study have demonstrated that PIN_PON is
more cold tolerant than PIN_ARI, while only the controlled laboratory experiment
quantified differences in cold tolerance. Using the most sensitive (i.e., highest
LTso) tissues to calculate species differences, one finds a 1°, 125°, and 075° C
difference across the three acclimation stages for needle electrolyte leakage for
the first two values and bud mortality for the third value. Given that needle and
bud primordia in Pinus are similarly tolerant to cold (Sakai and Larcher 1987), a
mean of 1° C difference can be assumed from this experiment. This seemingly
minor temperature differential translates to 133 m in elevation, using Shreve’s
(1915) data of an adiabatic lapse rate of 75° C per 1000 m elevation for the
Santa Catalina Mountains. Given a current upper elevational boundary for
PIN_ARI of 2548 m on the south face of Mount Lemmon (Epperson et al. 2001)
and the 133-m differential predicted by differences in cold tolerance in this study,
then the upper boundary for PIN_PON should be ~112 m below the very peak of
the mountain. In fact, on this aspect, PIN_PON extends to the top and north side
of the mountain, suggesting that the difference in cold tolerance estimated by this
experiment is conservative. Instead, assuming that the top of the mountain is the
upper elevational boundary for PIN_PON, then the differential cold tolerance
would be 18° C, based on Shreve’s (1915) model. This difference is not very
large nor far from the species differences estimated from the controlled
laboratory experiment (Table 4-5). In fact, the difference in cold tolerance as

related to elevational difference will become moot if winter temperatures in the

158

region increase by the projected 2.8° C by 2100 (USEPA 1998). Kupfer et al.
(2005) predict an upward migration of communities with a 53% loss in montane
forest on Madrean mountain islands such as the Santa Catalina Mountains given
projected local climatic changes (USEPA 1998). Consequently, assuming
minimal dispersal limitation and disturbance (or lack of disturbance), winter
conditions at the top of the Santa Catalina Mountains will become suitable for

PIN_ARI within this century.

159

CHAPTER 5

THE ROLE OF DROUGHT TOLERANCE IN STRUCTURING PONDEROSAE
DISTRIBUTION IN THE SANTA CATALINA MOUNTAINS OF ARIZONA

Introduction

While tolerance to cold conditions in determining high latitude or elevation
limits in pine distribution is well known, the cause of the lower elevation boundary
is less well understood (Rundel and Yoder 1998). The tacit assumption is that
intolerance to more xeric conditions, or drought, constrains regeneration at lower
elevation (Barton 1993). Daubenmire (1943) showed that soil drought is the
primary determinant for lower elevation distribution for Rocky Mountain conifer
seedlings, while Haller (1959) and Yeaton et al. (1980) demonstrated the lethal
effect of drought stress on ponderosa pine (Pinus ponderosa) seedlings.
However, the extensive range of habitats in which ponderosa pine can be found
implies that a suite of genetically controlled and phenotypically plastic
mechanisms have evolved that allow survival in a variety of soil-water conditions
(Rundel and Yoder 1998).

Plants must trade-off the maintenance of whole-plant water status for the
requirement of the carbon substrate for photosynthesis. The entry of carbon into
the plant requires concomitant loss of water through transpiration at the stomata.
As air temperature increases, the water vapor gradient increases at the stomata.
thereby increasing stomatal conductance (9.), or loss of water through the
stomata. This increases the tension (negative pressure, \Pp) of water within the

root-stem-leaf pathway, which is exacerbated by low soil water availability.

160

When WP becomes sufficiently negative, cavitation, or air infusion into the xylem,
occurs and an embolism is formed (Sperry and Tyree 1990). Cavitation results in
a sustained increased resistance to water transport in those vessels or tracheids
and, if sufficient amount of hydraulic conduits cavitate, wilting with eventual plant
death (Tyree and Sperry 1988). The most direct means of preventing cavitation
is by stomatal closure (Sperry et al. 1993) and recovery of plant water status
through water uptake in the roots. It is thought that stomatal closure is stimulated
by negative feedback from cells surrounding the stomatal complex in response to
cell water potential (Hubbard et al. 2001) and/or root signaling via abscisic acid
(Jackson et al. 1995).

Plants have evolved a number of other mechanisms by which water loss
relative to carbon acquisition is balanced. Rooting architecture varies genetically
and depending on soil characteristics in ways to help access available soil water.
For example, ponderosa pine can develop very deep roots (24 m; Stone and
Kalisz 1991) to access deep water sources and interstitial bedrock water (Rundel
and Yoder 1998). Roots also vary in their ability to extract available water;
Fowells and Kirk (1945) reported ponderosa pine seedlings extracting water from
soil below the wilting point of sunflowers. Pines also maintain a significant
portion of functional xylem sapwood with storage of reserve water (Rundel and
Yoder 1998). In comparison to populations growing in more mesic sites,
genetically undifferentiated populations of ponderosa pine have reduced total leaf
surface area (A) relative to sapwood area (As), even with higher whole-tree leaf

specific hydraulic conductance (KL), thereby buffering total water loss (Maherali

161

et al. 2002). The anatomical properties of xylem also contribute to cavitation
resistance. For example, larger conduits reduce overall hydraulic resistance, but
larger diameter vessels are more susceptible to irreparable cavitation than
tracheids. Stronger pit membranes are less porous and thus cause higher
resistance to both water flow and air infusion (Piflol and Sala 2000). At the leaf-
level, higher water-use efficiency (WUE), whether by increasing photosynthetic
rate (A), decreasing 9., or effectively dissipating heat, results in better
maintenance of whole-plant water status relative to carbon gain (Monson and
Grant 1989, Rundel and Yoder 1998). In contrast, Zhang et al. (1997) found no
relationship between drought tolerance and high WUE, measured by
instantaneous gas exchange and carbon isotope ratio (6130), a measure of
integrated WUE (Farquhar et al. 1989), in highly controlled ponderosa pine
seedlings. Furthermore, in greenhouse studies, ponderosa pine seedlings from
geographically variable sources differed in biomass allocation patterns but not in
9,, A, or needle WP (Cregg 1994).

The combination of anatomical and ecophysiological attributes
contributing to drought tolerance, especially the apparent trade-off between KL
and resistance to cavitation, is an active area for research. Recent evidence
indicates that ponderosa pine is highly plastic (Maherali 2002), reduces ALzAs in
xeric habitats (Carey et al. 1998), maintains high WP in drought conditions
(Hubbard et al. 2001), and has similar vulnerability to cavitation across habitats
and climates (Maherali and DeLucia 2000) but increased stomatal control (Pinol

and Sala 2000) and sapwood water storage (Stout and Sala 2003) relative to

162

codominant Douglas-fir (Pseudotsuga menziesir). These combinations of
adaptations to low soil-water and high vapor pressure deficit contribute to
differential survival across environmental gradients. For example, within a group
of Southwestern montane conifers distributed along an elevational gradient,
seedlings from lower elevation species suffered less photosynthetic depression
yet maintained higher plant water status under imposed drought (Barton and
Teeri 1993). Mechanistically linking distribution limits to ecophysiological
responses should improve our understanding of species responses to climate.

In the Santa Catalina Mountains of southern Arizona, populations of the
Southwestern ponderosa pine (Pinus ponderosa var. scopulorum Engelmannii)
exhibit a discontinuous lower elevational boundary suggesting an abiotic
influence. Although located in the Sonoran Desert, the forests in the Santa
Catalina Mountains experience reduced frostfree seasons (17 weeks at 2438 m;
Shreve 1915) due to high elevation (2793 in peak). In addition, the climate is
characterized by a bimodal distribution of precipitation as a result of an infusion
of subtropical high pressure from the south during the summer months
(Sheppard et al. 2002); consequently, a pronounced drought (“arid foresummer”)
occurs through late spring until the mid-summer rains (“monsoon”) arrive. At the
lower elevational boundary for Southwestern ponderosa pine, a closely related
Ponderosae, Arizona pine (Pinus arizonica Engelmannii), overlaps in distribution
forming a transition zone at approximately 2430-2550 m in elevation on a south
aspect (Epperson et al. 2001). This transition zone has been noted in Santa

Catalina Mountains (Whittaker and Niering 1965) and other mountain islands of

163

the Southwest (Dodge 1963, Barton 1993), but only Barton and Teeri (1993)
have sought an ecophysiological explanation for the lower elevational limits.
Arizona pine was included in Barton’s dissertation work but was not represented
by sufficient replicates for analysis (A. Barton, personal communication).

The objective for this study was to determine if critical measures of
drought tolerance indicated a genetic difference between Southwestern
ponderosa pine and Arizona pine. If evidence suggests that the higher elevation
Southwestern ponderosa pine (“PIN_PON”) were less drought tolerant than
Arizona pine (“PIN_ARI”), then the correlation of their differential distribution by
elevation would be supported by physiological (mechanistic) differences. In one
series of experiments, I measured diurnal A, transpiration (Tr), and W9 from
coincident ‘small’ PIN_PON and PIN_ARI trees across three hydrologically
important seasons: winter (January-February), arid foresummer (June), and
monsoon (August); W,, was compared to trees from other elevations, as well. A
second experiment tested for differences in integrated WUE (61°C) and nutrient
dynamics in needle (leaf) tissue of coincident PIN_PON and PIN_ARI. The third
experiment investigated photosynthetic function, measured as Photosystem ll
(PSII) excitation capture efficiency (Fv’lFm’; Schreiber et al. 1994), relative to
dehydrating needles of PIN_PON and PIN_ARI. In the final experiment, xylem
vulnerability to cavitation was measured from PIN_PON and PIN_ARI stems
across an elevational gradient; the laboratory work for this portion of the study

was conducted by Anna Jacobsen (MSU, Michigan State University).

164

Methods
Tree and site selection

At three locations across an elevational gradient, I selected “small”
PIN_PON and PIN_ARI trees for the ecophysiological experiments in this study
(Table 5-1). Small trees were chosen because they are more sensitive to
changes in soil water availability and temperature than larger trees with more
developed root systems and larger stem storage capability (Rundel and Yonder
1998). The Aspen fire (CNF 2003) eliminated most of the small-diameter trees
and virtually all seedlings from most of the Ponderosae forest in the Santa
Catalina Mountains (personal observation). Consequently, I attempted to
standardize the size by diameter across sites. A second criterion was to locate
similarly sized PIN_PON and PIN_ARI trees as close as possible to each other
(“paired”) without other nearby trees influencing potential soil water availability.
At each site, species designation was determined by calculating the mean
number of needles per fascicle across 5 years of needles in one terminal shoot; a
mean less than 3.2 was considered to be PIN_PON, and a mean greater than
4.6 needles per fascicle was a PIN_ARI tree (Peloquin 1984). Trees with mean
needle number between 3.2 and 4.6 were not used in this study because they
may represent hybrids between PIN_PON and PIN_ARI (Peloquin 1984,

Rehfeldt et al. 1996, Epperson et al. 2001).

165

Table 5-1. Characteristics of ‘small’ trees used for gas exchange and xylem pressure
potential measurements in 2005-06 at the Mount Lemmon (MTL), Palisade Rock (PAL),
and Lizard Rock (LIZ) sites.

 

DBH Height
Site Location (NAD83) Elev (m) Tree Species (cm) (m)
Mount Lemmon 32.4396°N, 110.7871°W 2770 MTL 221 PIN_PON 19.3 6.2
MTL 222 PIN_PON 5.1 2.3
MTL 223 PIN_PON 6.8 3.0
Palisade Rock 32.4138°N, 110.7149°W 2475 PAL 1 PIN_PON 12.9 4.4
PAL 2 PIN_ARI 13.0 3.9
PAL 3 PIN_PON 19.3 5.3
PAL 4 PIN_ARI 12.4 4.1
PAL 5 PIN_PON 17.0 2.4
PAL 6 PIN_ARI 8.1 3.4
Lizard Rock 32.3844°N, 110.6930°W 2135 LIZ 1 PIN_ARI 17.7 3.6
LIZ 2 PIN_ARI 12.2 3.1
LIZ 3 PIN_ARI 7.0 2.7

I selected three major sites for this study: Mount Lemmon (MTL),
Palisade Rock (PAL), and Lizard Rock (LIZ). The MTL site is located on a gentle
slope just south of the summit of Mount Lemmon. Three PIN_PON trees at the
north edge of the forest cover were selected; PIN_ARI has not been documented
at this elevation. The PAL site is located downslope of the Mount Bigelow radio
towers and upslope from the Palisade Ranger Station (Coronado National
Forest). Brown (1968) and Budelsky (1969) measured gas exchange from
“ponderosa pine” at this site, but they did not differentiate between PIN_PON and
PIN_ARl. This site was not selected due to this previous work but rather
because it was identified as a possible steep transition zone between the two
taxa; Epperson et al. (2001) documented the steep transition on the south face of
Mount Lemmon at similar elevation. Three “pairs” of PIN_PON — PIN_ARI trees
growing closely (<3 m) were located and chosen for this study. Another 44 trees

were identified, with 5 from each species used to increase sample size for the WP

166

measurements. The LIZ site is located along a small ridge northeast of Lizard
Rock and across the Catalina Highway. The xeric ridge is bare bedrock with
pockets of disintegrated granite, while the valleys on both sides of the ridge
receive floodwater and are thus much more mesic. Three PIN_ARI trees were
selected from this site; PIN_PON has not been documented in this area. All of
the trees were labeled with both aluminum write-on and nailed disc tags.
Additional PIN_ARI trees for the xylem vulnerability study were selected from a
ridge south of Rose Canyon Lake (RC, 32.3964°N, 110.6932°W (NAD83), 2175

m elevation) but were not labeled.

Seasonal gas exchange and xylem pressure potential

Diurnal net photosynthetic (A) and transpiration (Tr) rates were measured
from PIN_PON and PIN_ARI needles to investigate relative changes in gas
exchange through the course of the day and across seasons. Needles from
three “pairs” of PIN_PON — PIN_ARI trees were selected for measurements from
the terminal shoot on the lowest living branch with southern exposure. Within an
hour prior to measurements, three PIN_PON fascicles (9 needles) and two
PIN_ARI fascicles (10 needles) were taped at the fascicle sheath so as to
prevent needle crossing within the cuvette, but the needles remained attached to
the branches; this number of needles per species covered the entire cuvette with
no overlap. One pair of trees was measured per day for the arid foresummer
(14/16/18 June 2005) and monsoon (2/4/6 August 2005) seasons, while all three

pairs were measured in the same day during the winter (31 January 2006).

167

Consequently, except for winter, species comparisons are limited to
measurements from the paired trees. Frequency of measurements varied but
were approximately hourly, with 10 points taken per tree, while measurements
were taken from predawn through afternoon during the monsoon and through
dusk during winter and the arid foresummer; regular afternoon thunderstorms
(and even funnel clouds) during the monsoon prevented many opportunities for
data collection.

Gas exchange was measured using the standard 2x3-cm sensor head in
a Ll-COR 6400 Portable Photosynthesis System (LI-COR Biosciences, Inc.,
Lincoln, Nebraska, USA). The actinic light, infrared gas analyzer (IRGA), and
002 mixer were calibrated prior to measurements each day, and the lRGAs were
matched prior to each set of paired measurements within a time period. Carbon
dioxide concentration was maintained at approximately 400 pmol C02 mol",
which was usually the ambient concentration, with C02 gas cartridges (6400-01
C02 Injector, Ll-COR Biosciences, Inc.). Relative humidity was stabilized across
measurements from the paired trees within a time period. A constant block
temperature was used to stabilize leaf temperature across trees within a set of
paired measurements. Incident light (6400-028, Ll-COR Biosciences, Inc.) for
paired measurements was selected based on maximum light intensity (9901-013
External Quantum Sensor, Ll-COR Biosciences, Inc.) under full sky prior to each
set of measurements. Leaf temperature was measured by contact thermocouple

(6400-04 Leaf Temperature Thermocouple, Ll-COR Biosciences, Inc.) inside the

168

cuvette. Power was supplied by a number of rechargeable batteries (6400-03
Rechargeable Battery, Ll-COR Biosciences, Inc.).

Diurnal patterns in gas exchange are difficult to statistically compare
across trees, species, days, and seasons because changes in instantaneous gas
exchange occur rapidly in response to highly variable conditions, such as solar
radiation, temperature, and wind (Jarvis 1976). Consequently, analysis of this
experiment is limited to intrapair comparisons of patterns in response to mean
climatic fluctuations (e.g, incident light and temperature). The rate of change
(i.e., slope) in A and Tr in response to leaf temperature was compared by
species and season with paired t-test in R (R Development Core Team 2007),
assuming species within a day’s measurements represents the before-and-after
treatment.

Coincident with the diurnal gas exchange measurements, xylem pressure
potential (W,,) was measured from PIN_PON and PIN_ARI fascicles to assess
whole-tree water relations. At predawn, midday, and dusk (arid foresummer
only), fascicles were harvested from the trees used for the gas exchange
experiment, as well as 5 additional trees of each species to increase sample
size. On other days, diurnal WP was also measured from PIN_PON trees at MTL
and PIN_ARI trees at LIZ (Table 5-1). Fascicles were selected from a different
branch but same whorl number as that from which diurnal gas exchange was
being measured. Each of 5-10 fascicles was excised at the base of the fascicle
sheath with a razor blade, immediately placed into a prelabeled resealable plastic

bag with moist paper toweling, and then placed in a small cooler containing ice

169

packs. Within 2.5 h, LPy, was measured from up to 7 fascicles per tree with a
pressure chamber (PMS Instruments, Inc., Corvallis, Oregon, USA) and
averaged for each tree. Seasonal differences in predawn and midday 91,, across
sites were compared by the Welch two-sample t-test, while species differences
by season and time of day were examined with univariate ANOVA; site effects
were nested within species. All statistical analyses were conducted in R (R

Development Core Team 2007).

Integrated water-use efficiency and nitrogen dynamics

Needle tissue harvested from sympatric PIN_PON and PIN_ARI trees in
the Santa Catalina Mountains was analytically measured for species and annual
differences in integrated water-use efficiency (WUE), as measured by carbon
isotope composition (61°C; Farquhar et al. 1989). Given the physiological
relationships between foliar nitrogen content (%N) and 51°C (Sparks and
Ehleringer 1997) and nitrogen isotope composition (615N; Hobbie et al. 2000),
these parameters were also measured in the same analysis to investigate
potential differences in potential photosynthetic capacity (Reich et al. 1995) and
nitrogen acquisition (%N, 515M). Amount of structural carbon (%C) in the
samples was analyzed as a signal for differences in carbon-to-nitrogen (CzN)
aflocafion.

Needle tissue was collected in the late afternoon through early evening of
26 October 2005 for both this and the following experiment (Photosynthetic

function during dehydration). I harvested one healthy lower branch ~0.8-m in

170

length from each of 11 PIN_PON trees near the summit of Mount Lemmon, 12
PIN_PON trees and 8 PIN_ARI trees at PAL, 6 PIN_ARI trees from ridges
surrounding Rose Canyon Lake (RCL), and 4 PIN_ARI trees at LIZ. Sampled
trees included those listed in Table 5-1. Immediately after removal from the tree,
each branch was placed in a separate plastic bag containing moist paper
toweling inside a large cooler with ice packs. To reduce the effect of differential
soil water availability, the base of the branches were submersed in water in a 5-
gal bucket, ~1 cm was cut off from each end to restore sapflow into the branch,
and the base of the branches remained in water for ~ 7 h before being sealed
with instant cyanoacrylate adhesive (All Purpose Instant Krazy Glue Gel, Krazy
Glue, Columbus, Ohio, USA). Sealed branches were returned to the plastic bags
with moist toweling, placed into the cooler with ice packs, and shipped by air to
MSU. Approximately 20 h after the branch ends were sealed, the branch ends
were recut under water in a 5-gal bucket and rehydrated in a humid walk-in
cooler room (4° C) for ~58 h. After removing and processing fascicles for the
next experiment (see Photosynthetic function during dehydration), the remaining
fascicles on the terminal shoot were excised by razor blade at the base of the
fascicle sheath, separated by year of production, and placed into prelabeled
envelopes (No. 10 28-lb Heavy-Duty Brown Kraft Envelope, Columbian,
Stamford, Connecticutt, USA) to dry at room temperature.

In early June 2006, I selected samples from 10 trees from each species
growing at the PAL site based on trees having the largest range of annual

production (e.g., 2001-2005 fascicles) in sampled fascicles; needles from an 11th

171

PIN_PON tree were also included, but, at the time, there was some doubt as to
its taxonomic nature because of finding more >3-needled fascicles in the -
samples than when in the field. All of the samples within their envelopes were
placed into the drying oven (60° C) for at least 24 h before processing (CPSIL
2006). At least 5 (up to 12) fascicles with the appr0priate number of complete,
uninjured, and consistently colored needles (e.g., PIN_PON fascicles had to
have 3 needles) were selected from each year for each tree, trimmed by razor 1
cm from the terminus of the fascicle and from the distal portion of the needle, and
cut into ~1-cm pieces on a dry glass plate. The pieces were poured into a small
stainless steel canister containing a small stainless steel rod, both of which were
thoroughly cleaned, rinsed with deionized distilled water (NANOpure Ultrapure
Water System, Model D4741, Barnstead International, Dubuque, IA, USA), and
dried between samples. The tissue was pulverized within the covered canister
by a ball-mill grinder (Model 6, WlG-L-BUG Amalgamator, Crescent Dental
Manufacturing Company, Lyons, Illinois, USA) for 5-10 min per sample until
becoming a fine powder (CPSIL 2006). [Milling with a standard hand-held coffee
grinder and UDY Cyclone Sample Mill (0.5- and 1.0-mm screens; Seedburo
Equipment Company, Chicago, Illinois, USA), even for 15+ min, produced visibly
heterogeneous tissue particles unsuitable for isotopic analysis (CPSIL 2006).]
The pulverized tissue was stored in new 1-dram glass vials (Gerresheimer Glass,
lnc., Vineland, New Jersey, USA) that were color-taped by needle-year and
labeled by tree, species, and year. The capped vials were stored at room

temperature.

172

The powdered samples were prepared for laboratory analysis by removing
and packaging ~4.000-mg aliquots into 5x9-mm pressed tin capsules (COSTECH
Analytical Technologies, Inc., Valencia, California, USA). The capsules were
weighed before and after adding a sample followed by crimping with a
microanalytical balance (Model CP2P, Sartorius AG, Goettingen, Germany).
Sterile technique was used to prevent sample contamination since the entire
capsule is consumed in a mass spectrometer. Crimping was accomplished by
placing the preweighed capsule in one of three depressions in an aluminum
block (10 x 5 cm) and carefully pinching and then shaping the capsule to a tight
ball with two pairs of sterile forceps (CPSIL 2006). Eighty-five capsules, in
addition to a number of duplicates to check the analysis, containing pulverized
needle tissue from 21 trees were placed into a 96-well cell plate (Greiner Bio-
One North America, Inc., Monroe, North Carolina, USA) and shipped overnight
on 20 June 2006 for analysis at the Colorado Plateau Stable Isotope Laboratory
(CPSIL, Northern Arizona University, Flagstaff, Arizona, USA). The samples
were processed on 5 August 2006 by a continuous-flow gas isotope-ratio mass
spectrometer (Therrno-Finnigan Deltap'“° Advantage, Therrno Fisher Scientific,
Inc., Waltham, Massachusetts, USA) interfaced with an elemental analyzer
(ECS4010, COSTECH Analytical Technologies, Inc., Valencia, California, USA);
specifications can be found at CPSIL (2006). CPSIL used a variety of standards
to check raw isotope data (NIST peach leaves, pine needles, apple leaves,
tomato leaves, bovine liver, and mussel tissue; and caffeine - Aldrich), normalize

raw isotope data (NAEA CH6, CH7, N1, and N2), and correct raw %C and %N

173

data (acetanilide, BBOT, cystine, methionine, sulfanilamide, cyclohexanone, and
nicotinamide). Their external precision from the standards is reported as :l:0.10%o
or better for 613C and 10.20%» or better for 615N; 613C and 615N are expressed
relative to VPDB and air, respectively (CPSIL 2006).

The analysis of 61°C, 6‘5N, %C, %N, and C:N could be approached by
assuming independence in the variables across years within a given tree or by
assuming that they represent repeated measures from the same individuals
across years. The physiological questions relate to the relationship of timing of
current-year needle production to current-year nitrogen and carbon acquisition,
and the lability of nitrogen and carbon from one year’s needles to another year.
Budelsky (1969) showed that ponderosa pine needles at the PAL site begin
slowly elongating in mid-May, have the highest rate of elongation during July and
early August, and complete extension by the end of August. Consequently, I will
assume that current-year needle structural carbon was acquired during the
growing season. There is no a priori reason to assume that structural carbon is
labile; Billow et al. (1994) report strong seasonal differences in foliar starch and
sugar concentration, but no difference from August-October in New Mexican
Douglas-fir. Total nitrogen shifts seasonally, at least in Douglas-fir (Billow et al.
1994), and purportedly translocates from older to younger foliage (Rundel and
Yoder 1998). Consequently, both %N and (WW are expected to show some
degree of temporal autocorrelation. Potvin and Lechowicz (1990) demonstrated
complex approaches to modeling repeated measures from individuals in

ecophysiological experiments. However, the simplest statistical approach to

174

recognize repeated measures is to compare mean responses by species within a
given year by one-way ANOVA, with species as the fixed factor, which was
achieved using R (R Development Core Team 2007). Another approach is to
conduct an ANOVA on each response by nesting individual tree responses in
year, which in turn is nested in species: response ~ species (year (individual
tree»; this approach was also calculated in R. In addition, relationships between
isotopic composition and nutrient content by species were assessed in two—
factorial ANCOVAs with nutrient content as a continuous covariate to trees
nested in species. All of the nested analyses indicated no significant (p>0.26)
effects of individual trees on patterns observed across years and species (Table
5-5), thus the nested approach was considered satisfactory for investigating

species x year responses.

Photosynthetic function during dehydration

Needles collected from PIN_PON and PIN_ARI trees across an
elevational gradient in the Santa Catalina Mountains were slowly dehydrated with
concomitant measurement of PSII excitation capture efficiency to determine the
relationship of photosynthetic capacity during severe drought. PSII excitation
capture efficiency was estimated by the ratio of variable to maximum chlorophyll
fluorescence when acclimated to light. I used a pulse-amplitude modulated
fluorometer (6400-40 Leaf Chamber Fluorometer, Ll-COR Biosciences, Inc.,
Lincoln, Nebraska, USA) attached to the base LI-COR 6400 Portable

Photosynthesis System. The fluorometer was calibrated before the first set of

175

measurements, zero-ed before each set, and adjusted to recommended settings:
Saturating pulse (flash) - 0.8 s saturating multiple flash of ~8800-9000 umol m'2
s", 20KHz modulation, and 50 Hz averaging filter; a 6-s pulse of far red (“dark”)
intensity of 8 turned on 1 s before and remain on for 1 s beyond actinic light off,
0.25 KHz modulation, and 1 Hz averaging filter; and Measurement light —
intensity 2, 0.25 KHz modulation, 1Hz averaging filter, and 10 gain factor (Ll-
COR 2005a).

Needles from branches harvested on 26 October 2005 were used in this
experiment; see Methods in previous experiment for collection, processing, and
transport details. After hydrating overnight in the walk-in cooler (4° C), the
buckets of branches were moved to the laboratory. At room temperature, one
2005 fascicle per branch (i.e., tree) was excised from the terminal shoot with a
razor blade at the base of the fascicle sheath, weighed using an analytical
balance (Model AJ100, Mettler Instrument Corporation, Hightstown, New Jersey,
USA), immediately sealed with instant cyanoacrylate adhesive, and lightly
marked at the base with colored typographic correction fluid (Liquid Paper,
Sanford, Oak Brook, Illinois, USA) by species and relative elevation. After all
fascicles (n=41) were processed (~1.7 h), PSII excitation capture efficiency
(Fv’lFm’) was measured twice with the fluorometer, with a weight measurement
between readings, and averaged for the initial value. Over the next 16 days,
fascicle mass and FV’IFm’ were remeasured 30 times. Laboratory conditions
were monitored 2-3 times within each measurement period with a porometer (Ll-

1600 Steady State Porometer, LI-COR Biosciences, Inc., Lincoln, Nebraska,

176

USA). Needles were exposed to fluorescent lighting (3 =9.5:I:1.1SD pmol m-2 6")
during normal laboratory hours; air temperature was relatively high
(a? =26.718.7SD °C), and relative humidity varied with Michigan’s weather
()7 =29.2i14.BSD %). After the last mass and Fv’lFm’ measurements, fascicles
were dried in individual paper envelopes (No. 10 28-lb Heavy-Duty Brown Kraft
Envelope, Columbian, Stamford, Connecticutt, USA) for 6 days at 60° C in a
ventilated drying oven (Model 637G lsotemp Oven, Fisher Scientific, Inc.,
Pittsburgh, Pennsylvania, USA) and then weighed to determine if all nonlabile
water had been removed from the needles.

Relative loss of fresh (wet) mass and PSII excitation capture efficiency
were modeled using 3-factor ANCOVA in R (R Development Core Team 2007).
The former model used site and species as fixed factors and time since excision
from the branch as a (continuous) covariate. The latter model also used site and
species as fixed factors but included percent loss of fresh mass as the covariate.
Nonstructural water remaining after the last mass and Fv’lFm’ measurements was

tested against final mass by paired t-test (R Development Core Team 2007).

Xylem conductivity and vulnerability

Branch stems were collected from mature PIN_PON and PIN_ARI trees in
the Santa Catalina Mountains on 20 October 2005 for determination of stem
xylem vulnerability to cavitation and xylem specific conductivity. Throughout the
morning, stern sections approximately 0.5 m in length were cut from the lowest

living branch from trees at the MTL (n=11 trees), PAL (n= 9 trees per species),

177

RCL (n=7 trees), and LIZ (n=3 trees) sites. Stems with few curves, diameter
between 5 and 10 mm, and no branches for at least 15 cm were selected. The
cut stems were immediately double-bagged (Zip-Loo) with a moist paper towel,
placed on ice, and transported via overnight shipping to Anna Jacobsen at MSU.
Stems were refrigerated until measured, and all stems were measured within 3
days of collection. A. Jacobsen performed all sample preparation, conductivity
measurements, methods documentation, and conductivity calculations for this
experiment.

Each stem was prepared for conductivity measurements by trimming
under water from each end until a straight, unbranched segment 6-9 mm in
diameter and 14 cm in length was obtained. Stems were connected to a tubing
system and flushed with low pH degassed water (pH 2 HCI) that had been
passed through a 0.1 pm filter to remove gas emboli from stems. Stems were
flushed for 1 h at 30 kPa. This relatively low pressure was used in order to avoid
aspiration of tori in pit membranes. Hydraulic conductivity (K1,) of stems was then
measured, and stems were flushed for additional 20 min intervals until a constant
maximum hydraulic conductivity (Khmax) was obtained (usually less than 2 h).
Hydraulic conductivity of stems was measured gravimetrically (Sperry et al.
1988) using an analytical balance (Model BP 121 S, Sartorius AG, Goettingen,
Germany). Following determination of Khmax, stems were spun in a centrifuge
(Sorvall RC-SB, DuPont Instruments, Wilmington, Delaware, USA) using a
custom built rotor to generate known negative pressures (Alder et al. 1997).

Stems were then reconnected to the tubing system and the new hydraulic

178

conductivity (Kn) determined. This process was repeated with successive spins
generating more negative pressures until stems experienced >80% loss in
hydraulic conductivity. Percent loss in hydraulic conductivity (PLC) was
calculated as [Khmax- Khjl Khmax. Vulnerability-to-cavitation curves were
constructed by plotting the PLC against the water potential generated using the
centrifuge. For each stem, curves were fit with a second-order polynomial model
(Jacobsen et al. 2007) to predict the water potential at 50% loss in hydraulic
conductivity (W50). Xylem specific hydraulic conductivity (K3) of stems was
determined using the Khmax and the cross-sectional xylem area. Cross-sectional
xylem area was measured using a dissecting microscope and digital calipers.
The xylem diameter (without bark) and pith diameter were measured in two
perpendicular directions, and the area of the calculated pith area (an oval defined
by the radii in two directions) was subtracted from the xylem area (also
calculated as an oval defined by the radii in two directions). The Khmax was then
divided by this area for each stem to yield the xylem specific conductivity (kg rn'1
MPa'1 s").

W50 and K, were modeled using two-factor ANOVA in R (R Development
Core Team 2007). The full models included the main fixed factors of site and
species, as well as their interaction. Given the unbalanced design, Type II sums
of squares (SS; Langsrud 2003) were used to test hypotheses using the ‘car’
(Companion to Applied Regression; Fox 2006) package in R (R Development
Core Team 2007). Means were compared at each factor level using Tukey’s

Honest Significant Difference (HSD) in R, which incorporates an adjustment for

179

unbalanced designs (R Development Core Team 2007).

Results
Seasonal gas exchange and xylem pressure potential

Species differences in diurnal gas exchange during the winter were similar
during the morning and evening but highly variable during the midday (Figure 5-
1). Incident radiation (PAR) peaked at 3000 pmol rn'2 s'1 at midday, while leaf
temperature (T leaf) varied from -1° C at dusk to 14° C at midday (Figure 5-1).
Transpiration (Tr) values were negative around dawn and after sunset,
suggesting either that needles were absorbing moisture from the air or that the
instrument calibration was in error. At cold temperature, condensation of water
vapor on the warmer-than-ambient IRGA sensors could have generated
erroneous measurements of reference versus sample water vapor; this error is
not expected for C02 given its much lower freezing point. Tr values were highly
variable within and across species during the midday. Net photosynthesis (A)
was negative at predawn and dusk, indicating respiration. A increased rapidly for
all trees following daybreak and decreased rapidly in the late afternoon; like Tr, A
was highly variable across all trees during the midday. The rate of change in A
as a function of Tjeaf was the same for the two species (paired t-test, t=0.971,
df=2, p-value=0.434).

The paired trees demonstrated different responses in gas exchange
through the course of the day during the arid foresummer (Figure 5-2). PAR

across all three days was consistently ~2000 umol rn'2 s'1 by early morning and

180

Tleaf ('C)

 

1

I + PIN_PON (PAL 1)
-o— PIN_PON (PAL 3)
1 , +PIN_P0N(PAL 5)
, --— ~ PIN_ARI (PAL 2)
— r - PIN_ARI (PAL 4)
—-— PIN_ARI (PAL 6)

   
 
 
  
   
  

 

Tr (mmol H20 m'2 s“)
O

‘ +PIN_PON(PAL 1)

. + PIN_PON (PAL 3) y‘\
—+— PIN.PON (PAL 5)
,0 . -—I-- PIN_ARI (PAL 2)
- I» — PIN_ARI (PAL 4)
a « —I— PIN_ARI (PAL 6)

A (pmol m‘2 s")

 

Time of day

 

4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20200

Figure 5-1. Diurnal variation in net photosynthetic rate (A) and transpiration (Tr) as a
function of incident radiation (PAR) and temperature (Tleaf) for paired PIN_PON and
PIN_ARI trees at PAL during winter (January 2006).

181

to late evening before sunset; once the sun broke the horizon at ~5:45, intensity
increased rapidly. Tleaf was consistently 11-16° C in early morning, increased to
~22° C by midday, and then decreased to ~17° C by dusk. Both species
experienced active Tr and A in the early morning, followed by a midday
depression before renewal of limited gas exchange in the later afternoon. In
particular, the PIN_PON tree labeled PAL 1 precipitously dropped Tr and A
before 10:00, indicating almost complete closure of stomata (Figure 5-2, first
page). Its paired PIN_ARI tree also decreased gas exchange by almost 75% of
its early morning rate but did continue losing water through transpiration. Under
similar climatic conditions, though, another PIN_PON tree (PAL 3) decreased Tr
and A through the afternoon but did not demonstrate the severe midday
depression. Likewise, its paired PIN_ARI tree steadily decreased gas exchange
through the afternoon. The third pair of trees demonstrated a great deal of
variability in gas exchange throughout the day but did peak at around the same
time (~9:00) as the other pairs of trees. As during the winter, the two species did
not differ in the rate of change in A as a function of T..." (t=0.9375, df=2, p-
value=0.4475).

Changes in gas exchange for the same three pairs of trees tracked
climatic conditions independent of species during the monsoon season (Figure 5-
3). PAR was higher than during the arid foresummer but lower than during
winter, but it was highly variable throughout each day and peaked near 2010,
2290, and 2620 umol rn'2 6'1 across the three days of measuring the paired trees,

respectively. Treat was higher than during the arid foresummer, with temperatures

182

i

_h

PAR (pmol m'2 s“)

Tleaf (°C)

Tr (mmol i-to in2 s")

A (pmol m2 s")

i

 

 

—+—-— PIN_PON (PAL 1)
— o- - PIN_ARt (PAL 2)

   

 

 

J

—e— PIN_PON (PAL 1)
— .- -PIN_ARI (PAL 2)

 

 

081

06*

04‘

02‘

 

-0.2 J

+PIN_PON (PAL 1)
— '- -PIN_ARI (PAL 2)

 

 

 

—O-PIN_P(N (PAL 1)
{1‘ - o- - PIN_ARI (PAL 2)

 

 

 

Time of day

6.00 8.00 10:00 12:00 14:00 16:00 18:00 20:00

Figure 5-2. Diurnal variation in net photosynthetic rate (A) and transpiration (Tr) as a
function of incident radiation (PAR) and temperature (Tleaf) for paired PIN_PON and
PIN_ARI trees at PAL during the arid foresummer (June 2005). Note that the ordinate
scales for A and Tr vary across pages.

183

250° ‘ —O—-PN__PON (PAL 3)
— .— - PN_ARI (PAL 4)

 

 

15*

 

Tleaf (°C)

+ PIN_PON (PAL 3)
5 ~— .- -PN_ARI (PAL 4)

 

o . . - , , , 1

+ PN_PON (PAL 3)
2.0 4 — I- - PN_ARI (PAL 4)

1.5 ‘

Tr (mmol I-bO rn‘2 s")
9 .-
U'I o

 

 

0.0

 

0.5 1
14 ‘

—-O—PIN_PCN (PAL 3)
— n- - PIN_ARI (PAL 4)

A (pmol m" s")

 

 

Time of day

 

4:00 6:00 8200 10:00 12:00 14:00 16:00 18:00 20:00

Figure 5-2. Continued.

184

250° ‘ —o— PN_PON (PAL 5)
—- .— - PN_AR| (PAL 6)

 

+ PN_PON (PAL 5)
— I- - PIN_ARI (PAL 6)

 

+ PN_PON (PAL 5)
— n- - PN_ARI (PAL 6)

 

Tr (mmol H20 m‘2 s")
O

P
n

 

—0.4

      

-O.6
7 .
—O— PIN_PW (PAL 5)
5 . — 3' 'PIN_ARI (PAL 6)
5 .

A (umol m4 s")
u

~I._
'1 /
I’

 

 

Time of day
4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00 20:00

Figure 5-2. Continued.

185

  

 

 

 

 

+PN_PON (PAL 1)
— u- - PN_ARI (PAL 2)
,2000
'm
‘3‘
E1500
_0
E
31000
n:
<
D.
500
o c—r L
30
25
A 20
9
u; 15
2
I‘ 10
+ PIN_PON (PAL 1)
5 — e- -PIN_ARI (PAL 2)
O r
2.5 -

-—-O—- PIN_PON (PAL 1)
— I- - PIN_ARI (PAL 2)

Tr (mmol HzO m'2 s")

 

 

 
 
    

 

 

12 . +PN_PON (PAL 1)
10 _ — n. — PN_ARI (PAL 2)
:~ s4
‘ /
“I‘m a 4 / \
E l
a 6
5 . .
< 2 I
0 /. . . .
.2 Time of day
4:00 6:00 8:00 10:00 12:00 14:00 16:00 18:00

Figure 5—3. Diurnal variation in net photosynthetic rate (A) and transpiration (Tr) as a
function of incident radiation (PAR) and temperature (Tleaf) for paired PIN_PON and
PIN_ARI trees at PAL during the monsoon season (August 2005). Note that the
ordinate scales for A and Tr vary across pages.

186

Im'
A
(ll
0
O

1

 

15*

Tleaf (°C)

10*

 

+ PIN_PON (PAL 3)
— n- - PIN_ARI (PAL 4)

  

 

—o— PIN_PON (PAL 3)
— I- - PIN_ARI (PAL 4)

 

Tr (mmol I-bO rn'2 s")

 

_. $5
on or
J

... —A ..s _l
O N § m
J

A ().imol rn‘2 s")
a

 

 

Y T l T r ' I

—e— PIN_PON (PAL 3)
— I- - PIN_ARI (PAL 4)

 

—o— PN_PON (PAL 3)
— u- — PN_ARI (PAL 4)

 

 

T T I I

Time of day
6:00 8:00 10:00 12:00 14:00 16:00 18:00

Figure 5-3. Continued.

187

3000 1

N
0|
0
O
A

O
O
O

PAR (pnlgl m'is")
O 0'!
O O
O O

500 1

 

251

20..

151

Tleaf (°C)

10‘

 

 

—e— PIN_PON (PAL 5)
— u- - PIN_ARI (PAL 6)

 

—e— PN_PON (PAL 5)
-- s- - PIN_ARI (PAL 6)

 

3.0
l
2.5 1
2.0 1
1.5 1

1.0 1

Tr (mmol HzO rn'2 s")

0.5 1

 

T T r T T l 1

+ PIN_PON (PAL 5)
- '- - PIN_ARI (PAL 6)

 

 

0.0

161
14«

A (pmol rn‘2 s“)

 

 

—o— PN_PON (PAL 5)
— .- - PN_ARI (PAL 6)

‘l-v.‘

  

V1
\

 

1 7 fl

Time of day
6:00 0:00 10:00 12:00 14:00 16:00 18:00

Figure 5-3. Continued.

188

varying from 14-17° C in the morning to peak readings of 25-29° C later in the
day. Tr and A for both species paralleled changes in PAR; Tr and A increased
when PAR increased. Given this very strong apparent relationship of gas
exchange with PAR, and no visible evidence of such a relationship with Tiger, the
rate of change in A to Thar by species is not given.

Across all seasons, needle xylem pressure potential (%) from PIN_PON
and PIN_ARI trees became more negative from predawn to midday and then
partially recovered by evening (PIN_PON, t=-1.9774, df=14.239, p-value=0.068;
PIN_ARI, t=-2.3072, df=19.79, p-value=0.032), at least during the arid
foresummer (Figure 5-4). ‘Pp was most negative for both species in winter and
least negative during the monsoon season, with significant (p<0.04) differences
at each time period across every season (Table 5-2). For both individual
species, there was no significant (p<0.05) difference in predawn l-Pp between the
arid foresummer and monsoon, indicating similar soil water availability during
these two seasons; however, the difference for PIN_ARI was marginally
significant (p=0.087), suggesting a lower ability to acquire soil water relative to
PIN_PON. The differences in midday ‘Pp for PIN_PON trees across winter to the
arid foresummer and across the arid foresummer to monsoon were marginally
significant (p=0.076 and 0.063, respectivelY); relatively large variation in winter
and monsoon WP prevented finding a significant difference, although the trend
remains. Across the elevational gradient, these data suggest that the order of

“drought" conditions was winter > arid foresummer > monsoon.

189

-10 1i

w. (MPa)

-15 4

-20 ..

 

-25 J

Figure 54. Diurnal by seasonal needle xylem pressure potential (Wp) for PIN_PON and

Predawn

  

1
I

Midday

1

Evening

—<>— PIN_PON — Winter
+ PIN_PON - Arid foresummer
—-O— PIN_PON - Monsoon
- -B- - PIN_ARI - Winter

- I- - PIN_ARI - Arid foresummer
—'l— PIN_ARI - Monsoon

PIN_ARI trees growing in the Santa Catalina Mountains.

Table 5-2. Comparison of seasonal xylem water potential (W,,) of combined (both),

PIN_PON, and PIN_ARI fascicles harvested at predawn, midday, and evening across an
elevational gradient. Significant (p<0.05) terms are bolded.

 

Season 1 Season 2 Time Species t-value df p-value
Winter Arid Foresummer Predawn Both -6.5491 40.973 7.2E-08
Predawn PIN_PON 4.1688 17.22 0.0006
Predawn PIN_ARI -5.6791 14.742 4.7E-05
Winter Arid Foresummer Midday Both -3.5004 31.469 0.0014
Midday PIN_PON -1.9242 13.234 0.0761
Midday PIN_ARI 4.0673 17.900 0.0007
Winter Monsoon Pred awn Both -8.6737 40.420 9.1 B1 1
Predawn PIN_PON -7.9012 18.309 2.6E-07
Predawn PIN_ARI -5.9794 19.991 7.6E-06
Winter Monsoon Midday Both —8.6737 40.420 9.1E-11
Midday PIN_PON -3.0359 18.207 0.0070
Midday PIN_ARI 4.4044 13.659 0.0006
Arid Foresummer Monsoon Predawn Both -2.2027 41.931 0.0332
Predawn PIN_PON -1.2752 14.332 0.2225
Predawn PIN_ARI -1.8300 14.919 0.0873
Arid Foresummer Monsoon Midday Both -3.1052 28.169 0.0043
Midday PIN_PON -2.0384 12.813 0.0627
Midday PIN_ARI -2.3528 12.552 0.0357

 

190

The diurnal pattern of intraspecies variation in ll", within each season
gives an indication of individual tree variation in water loss (Figure 5-5). In
winter, more variation is observed from predawn to midday in PIN_PON but not
PIN_ARI, which in fact demonstrated less variability by midday; this result could
be due to cold conditions limiting photosynthesis for PIN_PON trees at the
highest elevation while milder conditions at PAL allowed photosynthesis, and
thus transpiratory water loss. During the arid foresummer, variation in W9
decreased for PIN_PON midday but increased by evening, while PIN_ARI trees
were quite consistent in their response throughout the day. In the monsoon
season, midday variation increased substantially for both species relative to
predawn variation. During winter and arid foresummer, less variation is expected
by midday as trees respond to the drought conditions; conversely, when soil
(liquid) water availability is high such as during the monsoon, variation is
expected to be greater during the midday as both species are actively
photosynthesizing. lntraspecies variation in recovery of water status during the

evening of the arid foresummer was larger for PIN_PON than PIN_ARI trees.

191

6 - —<>—— PIN_PON - Winter
—0— PIN_PON - Arid foresumner
-0— PIN_PON - Monsoon

5 a.
/ --e—- PIN_ARI - Winter
, ’ -— I - PIN_ARI - Arid foresurrmer
4 ~ ’ —I- PIN_ARI - Monsoon

  

so of w. (MPa)
0)

 

 

Predawn Midday Evening

Figure 5-5. Variation (standard deviation, SD) in diurnal needle xylem pressure potential
(\Pp) by season for PIN_PON and PIN_ARI trees growing in the Santa Catalina
Mountains.

Separation of WI, measurements by site (i.e., nesting) allows consideration
of species x site interactions (Figure 5-6). During winter, needles from trees
growing at higher elevation site had consistently lower (i.e., less negative) Wp
than those from lower elevation; the diurnal change in WP was largest at PAL
(A=-3.6 MPa), considered to be the intermediate site in terms of cold at high and
aridity at low elevation. Both species and site effects were significant (p<0.004)
in explaining variation in 9),, (Table 5-3). The same pattern of diurnal ll", was
observed during the arid foresummer, with higher elevation correlated with lower
WP, but the needles at the highest elevation site had the largest amount of diurnal
variation (A=-8.2 MPa). While site effects were significant (p<0.005) across
predawn, midday, and evening periods, species differences were only significant
at midday and evening (p<0.005); large variation in predawn 91,, for PIN_PON

trees at PAL reduced the species effect (Figure 5-5). During the monsoon

192

season, predawn Wp was unexpectedly highest at the lowest elevation site (LIZ,
Wp=-6.5 MPa), followed by trees from MTL and PAL (mean LlJp=-8.8 MPa), and
then the negative elevation-to-‘I’p pattern developed by midday as all WP
increased substantially (mean A=4.8 MPa). However, \Pp did not significantly

vary by species (p>0.50) nor site (p>0.09) at predawn or midday.

 

 

 

 

 

 

 

Predawn Midday Predawn Midday
0 1 1 0 1
—<>—— PIN_PON - MTL
—e— PIN_PON - PAL
-5 1 -5 1 — I- -PIN_ARI - PAL
'\ -I— PIN_ARI - LIZ
‘13.10 I. “““ $-10 «
é ‘\ §~ E o— —<>
0. \ Q.
9 -15 4 ' 3 -15 ~
—<>— PIN_PON - MTL ~ - ‘
+PIN_PON-PAL ““~-
'20 ’ — I - PIN_ARI - PAL '20 ‘
—I-- PIN_ARI - LIZ "" —————— I
-25 -25 1
Predawn Midday Evening
0 L .
-5 «l
0.5-10 \
g \
£45 1 \ \ x \ __'
\ \ -' ‘— ...-l
——<>— PIN_PON - MTL \ ____ .— —’
-20 . +PIN_PON- PAL " ‘—
— I- - PIN_ARI - PAL
-I- PIN_ARI . UZ

 

-25 1

Figure 5-6. Needle xylem pressure potential (I-Pp) by winter (top, left), arid foresummer
(bottom), and monsoon (top,n'ght) for PIN_PON and PIN_ARI trees growing along an
elevational gradient in the Santa Catalina Mountains.

193

Table 5-3. Nested ANOVA results xylem pressure potential (Wp) in needles from

PIN_PON and PIN_ARI trees across an elevation gradient, 3 seasons (“Arid” is arid
foresummer), and 2-3 daytime periods. Significant (a<0.05) models and terms are

 

 

bolded.

Season Period Adj R2 F(p-value) Term df SS F-value P-value

Winter Predawn 0.591 0.0002 Species 1 45.413 10.929 0.0039
Species(site) 2 93.039 11.1 95 0.0007
Residuals 18 74.794 4.155

Winter Midday 0.698 5.1E-05 Species 1 46.997 15.434 0.0012
Species(site) 2 95.887 15.745 0.0002
Residuals 16 48.719

Arid Predawn 0.506 0.0012 Species 1 10.128 2.7815 0.1127
Species(site) 2 79.278 10.8861 0.0008
Residuals 18 65.542

Arid Midday 0.511 0.0011 Species 1 20.119 10.2943 0.0049
Species(site) 2 28.687 7.3389 0.0047
Residuals 18 35.180

Arid Evening 0.767 1 .6E-06 Species 1 35.112 14.587 0.0013
Species(site) 2 138.178 28.703 2.5E-06
Residuals 18 43.326

Monsoon Predawn 0.126 0.1491 Species 1 2.756 0.4635 0.5047
Species(site) 2 33.049 2.7786 0.0888
Residuals 18 107.047

Monsoon Midday -0.132 0.9071 Species 1 8.30 0.3226 0.5770
Species(site) 2 5.76 0.1 1 19 0.8948
Residuals 18 463.18

 

Integrated water-use efficiency and nitrogen dynamics

Carbon isotopic composition (613C) varied strongly across years, while
nitrogen isotopic composition (615N) was relatively more stable (Figure 5-7).
Mean 61°C was -25.7%o (s=0.8%o) across years for both species, with a mean
increase of 0.891» in 2004-05 relative to the previous two years. There was no
significant species effect on 613C within each year (p>0.52, Table 54) nor across
years (p=0.94, Table 55). Mean 0‘5N was 4.4%. (s=0.8%o) across years for both

species, but PIN_PON needles discriminated against 15N more than PIN_ARI

194

needles (A=18%) for the years 2003-05, with significant (p>0.05) differences only
for 2003 (p=0.013) and 2005 (p=0.047) (Table 54).

 

  
 
  

2001 2002 2003 2004 2005

-25.0 i i t i -3.0
—<>— PIN_PON - 0130

’25'2 + "0- PIN_ARl-613C 1" '3'2
-254 4» —O—PIN_PON - 615N T -3.4
_25_6 fl +PIN_ARI-615N -- _3_6
-25.8 «~ -. -30

«)0 .... 1. ‘02

.0 -26.0 ( .40 70
-26.2 .- ,. 4.2
-26.4 4 «r .44
266 ~— 1 4.6
-26.8 4» .- 4.8
-27.0 i ._ -5.0

 

 

Figure 5-7. Annual variation in mean carbon (6‘3C, solid lines) and nitrogen (6‘5N,
dashed lines) isotope composition for leaf tissue of PIN_PON and PIN_ARI trees at PAL.
Note the different ordinate scales.

Table 54. Univariate ANOVA results for carbon isotope (513C), nitrogen isotope (6‘5N),
carbon (%C), nitrogen (%N), and carbon-to-nitrogen (C:N) content in needles for each
year from PIN_PON and PIN_ARI trees at PAL. Responses with significant (a<0.05)
differences between species for at least one year and significant terms are bolded.

 

Response Year Adj Rz Term Df SS F-value P-value
013C 2001 -0.1096 Species 1 0.1280 0.5061 0.5161
Residuals 4 1.0114
2002 -0.1161 Species 1 0.2022 0.2719 0.6207
Residuals 6 0.7439
1
1
1
1

 

2003 -0.0503 Species 0.0113 0.0418 0.8401
Residuals 9 5.1448

2004 -0.0525 Species 0.0011 0.0027 0.9588
Residuals 9 7.5773

2005 -0.0435 Species 1 0.0847 0.1668 0.6875
Residuals 19 9.6468

 

195

Table 5-4. Continued.

 

 

Response Year Adj Rz Term Df SS F-value P-value
5"N 2001 -0.0199 Species 1 0.1368 0.9023 0.3960
Residuals 4 0.6066
2002 -0. 1402 Species 1 0.0901 0.1391 0.7220
Residuals 6 3.8848
2003 0.2428 Species 1 3.3051 7.4131 0.0135
Residuals 19 8.4710
2004 0.0815 Species 1 1.5587 2.7744 0.1122
Residuals 19 10.6746
2005 0.1501 Species 1 3.4746 4.532 0.0466
Residuals 14.5671
%C 2001 -0.1146 Species 1 0.0977 0.4861 0.5241
Residuals 4 0.8043
2002 -0. 1627 Species 1 0.0044 0.0203 0.8913
Residuals 6 1 .2857
2003 0.1251 Species 1 1.4059 3.8610 0.0642
Residuals 19 6.9183
2004 0.1217 Species 1 1.3576 3.7714 0.0671
Residuals 19 6.8397
2005 -0.0375 Species 1 0.0305 0.2778 0.6043
Residuals 19 2.0843
%N 2001 0.7836 Species 1 0.0109 19.1010 0.0120
Residuals 4 0.0022
2002 0.0788 Species 1 0.0102 1.5988 0.2530
Residuals 6 0.0382
2003 -0.0294 Species 1 0.0052 0.4294 0.5201
Residuals 19 0.2318
2004 0.1217 Species 1 1.3576 3.7714 0.0671
Residuals 19 6.8397
2005 0.0626 Species 1 0.0586 2.3361 0.1429
Residuals 19 0.4764
C:N 2001 0.8096 Species 1 27.8548 22.2640 0.0092
Residuals 4 5.0045
2002 0.0654 Species 1 27.9230 1.4902 0.2680
Residuals 6 1 12.2470
2003 -0.0082 Species 1 14.8900 0.8367 0.3718
Residuals 19 338.1900
2004 -0.0422 Species 1 3.6900 0.1906 0.6673
Residuals 19 367.7400
2005 0.0599 Species 1 27.4190 2.2751 0.1479
Residuals 19 228.9850

 

Like 613C, needle tissue carbon content (%C) was relatively stable for both
species in the years 2001-03 but then dropped ~2% in the years 2004-05, which

correspond to years with higher precipitation than the drought years of 2000-02

196

in the Santa Catalina Mountains (personal observation) (Figure 5-8). In 4 of 5
years, PIN_ARI needles had higher %C than PIN_PON needles. Consequently,
both year and species were significant (Table 5-5). Conversely, nitrogen content
(%N) was ~36% lower in the older (34 year-old) foliage relative to the 2005
needles; nonetheless, the difference was not significant (p=0.571). However,
PIN_PON needles had consistently (Figure 5-8) higher %N than PIN_ARI

needles across years.

 

   

 

 

 

53.0 .. ~— 1.8
-_ 1.6
52.5 ~~
-_ 1.4
A $5
g 52.0 .. 1.2 ;-;
a C
c .9
0 --
E 1.0 §
8 515
C
c +118 0)
5 8
<0 L a. .1:
0 51'0 —<>—PIN_PON-%C 0'6 2
--Cl-— PIN_ARI- %c -. 0.4
50.5 ... —O—PIN_PON-%N 4
+PIN_ARI- %N ’ 0'2
50.0 i 4 4 i 0.0

2001 2002 2003 2004 2005

Figure 5-8. Annual variation in mean carbon (solid lines) and nitrogen (dashed lines)
content for leaf tissue of PIN_PON and PIN_ARI trees at PAL. Note the different
ordinate scales.

‘ 197

Table 5-5. Nested ANOVA results for carbon isotope (61°C), nitrogen isotope (6‘5N),
carbon (%C), nitrogen (%N), and carbon-to-nitrogen (C:N) content in needles from
PIN_PON and PIN_ARI trees at PAL. Individual tree (tree) is nested in year, which is
nested in species. Significant (a<0.05) models and terms are bolded.

 

 

Response Adj R2 F (p-valueLTerm Df SS F -value P-value

6"C 0.1989 0.0236 Species 1 0.0023 0.0049 0.9445
Species(year) 8 17.0604 4.5055 0.0003
Species(year(tree)) 10 0.8642 0.1826 0.9969
Residuals 57 26.9792

6‘5 N 0.1071 0.1286 Species 1 6.9880 12.1216 0.0010
Species(year) 8 3.8800 0.485 0.5706
Species(year(tree)) 10 5.3420 0.9266 0.5160
Residuals 57 32.8620

%C 0.5774 1 .91 E-08 Species 1 1.3775 5.0514 0.0285
Species(year) 8 29.7354 1 3.6303 8.24E-11
Species(year(tree)) 10 2.3886 0.8759 0.5604
Residuals 57 15.5436

%N 0.5964 6.08E-09 Species 1 0.0482 2.4496 0.1231
Species(year) 8 2.3547 14.9629 1 .53E-11
Species(year(tree)) 10 0.1798 0.9142 0.5267
Residuals 57 1.1212

C : N 0.6964 4.15E-12 Species 1 54.0200 3.5350 0.0652
Species(year) 8 2718.7100 22.2391 6.28E-15
Species(year(tree)) 10 181.32 1.1866 0.3191
Residuals 57 871.02

 

Nutrient content in leaf tissue was related to 613C but not to 615N. 61°C
was significantly negatively associated with %C (Figure 5-9; p=0.0001), positively
associated with %N (Figure 5-10; p=6.79E-05), and negatively associated with
C:N (p=1.51E-05); nutrient dynamics of PIN_PON and PIN_ARI relative to 513C
did not differ (p>0.45). Conversely, 615N was not significantly (p>0.17)
associated with nutrient content, but the species effect was significant for all
models (%C, p=0.0009; %N, Figure 5-11, p=0.0003; and C:N, Figure 5-12,

p=0.0003).

198

Carbon content (%)
49.5 50 50.5 51 51.5 52 52.5 53 53.5
-23.5 +

_L:

—i-

I I I I
T I I I

4)-
T

—24.0 4
-245 «
-25.0 «

..0 -25.5 4

7° -26.0 -
-26.5 -

-27.0 1

 

-27.5 1 o PIN_PON- y = -0.618x + 6.0526, R2 = 0.3007 6
28 0 . a PIN_ARI- y = -0.2495x - 12.803, R2 = 0.0935

 

Figure 5-9. Carbon isotopic composition (513C) relative to carbon content in needles from
PIN_PON (solid line) and PIN_ARI (dashed line) trees at PAL.

Nitrogen content (%)

 

        

0.7 0.9 1.1 1.3 1.5 1.7 1.9
-23.5 i i 4 4 i 4
24 0 - <> PIN_PON - y = 1.8718x - 28.22, R2 = 0.2708
' 1 a PlN__ARl-y=1.1287x-27.154,R2=0.1121 <> 0
-245 4 ,3 0O (3
DD Cl U 6) o
'25.0 " O 0 (>0 G [9
'° —26.0 . __ ,. .m— on
Cl <> 0
-26.5 i Q) ‘3
D oo 0 D

-270 ~

[II D O
-27.5 1 0 o
-28.0 —

 

Figure 5-10. Carbon isotopic composition (5‘30) relative to nitrogen content in needles
from PIN_PON (solid line) and PIN_ARI (dashed line) trees at PAL.

199

Nitrogen content (%)

 

 

 

0.7 0.9 1.1 1.3 1.5 1.7 1.9
0 I i I I i 4
o PIN_PON- y = 0.2597x - 5.0048. R2 = 0.0104
11 1 o PIN_ARI - y = 1.1971x- 5.6, R2 = 0.0772
-2 4
I3 Cl
C]
-3 a D IE) ‘3
2
91o r5J g q’ib ______
-4 7 O ..EJ-EID" "% fl -0?) C] D
”07.8.9413 9513993301: <90 0 £0
-5 4 '6 v ow O o 000 00
CI Cl
C]
-6 - C] o O
o
-7 A

Figure 5-11. Nitrogen isotopic composition (6‘5N) relative to nitrogen content in needles
from PIN_PON (solid line) and PIN_ARI (dashed line) trees at PAL.

Carbon:nitrogen (C:N)

 

 

 

20 25 3o 35 40 45 50 55 60
0 I I I I I I I I
o PIN_PON - y = -0.0077x - 4.3508, R2 = 0.0082
'1 I" o PIN_ARI - y = -0.0352x- 2.6065, R2 = 0.0821
-2 4»
D C]
_3 _,_ D C] DC]
2 D 0
31o __ _ d3 g c?
41— - ‘ 6 ~ 6 I O
800 E 004% A113)» AD o'Qe—Q~O.-_‘
-5~~ <2 00900 0585‘ ”o
[3 CI
-6 4» D o O E]
o
-7 «—

Figure 5-12. Nitrogen isotopic composition (6‘5N) relative to carbon-to-nitrogen content
(C:N) in needles from PIN_PON (solid line) and PIN_ARI (dashed line) trees at PAL.

200

Photosynthetic function during dehydration

Needles from PIN_PON and PIN_ARI trees across the elevational
gradient lost a similar amount of water mass across the first 8 d, but mass loss
asymptoted earlier for PIN_ARI than PIN_PON needles consistently across sites
(Figure 5-13). All measurements from one tree (LIZ 1) were removed due to
exceptionally low initial Fv’lFm' (0.035); resultant sample sizes for PIN_PON and
PIN_ARI were 23 and 18, respectively. There was a significant decrease in
water loss following the final drying (paired t-test, t=5.7051, df=40, p-
value=1.27E-06), indicating that some intracellular water remained after the final
measurements.

The species difference becomes more apparent when trees are grouped
across sites by species (Figure 5-14). Two-way ANOVA (Adj-R2=0.507, F (p-
value)=4.85E-06) of mass loss at the final (31") measurement indicates a
significant species (p=1.81E-07) effect but no site effect (p=0.309). However,
when analyzed as a function of time since excision from the hydrated branch,
almost twice the amount of variation in mass lost is accounted for by site
differences than species differences, although both terms were highly significant
in the ANCOVA (Table 5-6). Attempts to reduce the right skewness of the
otherwise normally distributed residuals through square, square-root, reciprocal,
natural-log, natural-log of reciprocal, and arc-sine transformation of the response
variable were not successful; i.e., skew remained and the adjusted-R2
decreased. Because all transformations identified the same significant terms,

only the level of significance varied, the linear model was retained. The

201

significant interaction of site by time elapsed since branch excision is unfortunate
and related to the order imposed to prevent measurement error; no difference in
measurements as a function of elapsed time was expected due to the rapid rate

in taking Fv’lFm’ measurements at each time period (1? =1.23 h, s=0.25 h).

100

    

 

 

90 j
80 (
SE
8 70 -
N
E
5 60 ~
E i
o PIN_PON - MTL
50 « e PIN_PON- PAL
- PIN_ARI - PAL
40 . x PIN_ARI - RCL
- PIN_ARI - LIZ
30 T 7 T I I 1
0 100 200 300 400 500 600

Elapsed time (h)

Figure 5-13. Dehydration of fascicles for PIN_PON (solid lines) and PIN_ARI (dashed
lines) stems from four sites in the Santa Catalina Mountains: MTL, PAL, RCL, and Liz.
Fitted curves are second-order polynomials.

202

   
 

 

 

90 49
80 .
:s‘
m 70 «
8
E
§ 60
u‘:
50 ~
0 PIN_PON ______
40 1 o PIN_ARI
30 r I F 1 r 1
0 100 200 300 400 500 600
Elapsed time (h)

Figure 5-14. Mean dehydration of fascicles for PIN_PON (solid lines) and PIN_ARI
(dashed lines) stems across four sites in the Santa Catalina Mountains. Fitted curves
are second-order polynomials.

Table 5-6. ANCOVA results for fresh mass loss (pmass) and PSII excitation capture
efficiency (FV’IFm’) of dehydrating needles for PIN_PON and PIN_ARI stems from four
sites in the Santa Catalina Mountains: MTL, PAL, RCL, and LIZ. Covariates for the two
models are elapsed time (Etime) and pmass, respectively. Significant (a<0.05) terms
are bolded.

 

 

Response Adj R2 F(p-value) Term Df SS F-value P-value

pmass 0.8059 <2.2E-16 Site 1 1145 25.7966 4.366-07
Species 3 640 4.8117 0.0025
Etime 1 230338 5191.3640 <2.2E-16
Slte'Etime 1 2097 47.2516 9.79E-12
Species*Etime 3 210 1.5805 0.1923
Residuals 1261 55950

Fv’lFm’ 0.7939 <2.25-16 Site 1 0.1145 19.9976 8.45E-06
Species 3 0.1520 8.8483 8.33E—06
pmass 1 27.7698 4848.7223 <2.28-16
Site‘pmass 1 0.0002 0.0272 0.8689
Species*pmass 3 0.0352 2.0491 0.1052
Residuals 1261 7.2221

 

203

PSII excitation capture efficiency relative to progression of dehydration
was consistently higher for needles from PIN_PON trees than from PIN_ARI
trees, while site differences were only apparent for PIN_ARI (Figure 5-15). While
very close, PIN_PON needles from PAL had higher Fv’lFm’ for a given relative
loss in water mass, while higher elevation PIN_ARI clearly had higher retention
of photosynthetic function through dehydration. Grouping by site increased the
apparent species difference (Figure 5-16) that is supported by the ANCOVA
results (Table 5-6). In contrast to rate and extent of dehydration, both site and
species are equally significant (p=8E-06) in contributing to variation in PSII
excitation capture efficiency; no interactions with the covariate in this model were
significant (Table 5-6). Although statistical painivise comparison by site within
species is not possible due to the significance of the covariate relative mass, the
overall site effect was significant (Table 5-6). In Figure 5-16, the difference in
photosynthetic function for a given relative amount of water loss appears minimal
across sites for PIN_PON but clear for PIN_ARI, hence the overall significant site

effect.

204

0.6 1

o PIN_PON - MTL

0,5 . e PIN_PON - PAL 0
- PIN_ARI - PAL
x PIN_ARI - RCL
°~4 ‘ - PIN_ARI - LIZ

  
 
  
   
  

0.3 1

0.2 1

Chlorophyll fluorescence (Fl/Fm')

0.1 1

 

 

30 40 50 60 70 80 90 100
Fresh mass (%)

Figure 5-15. Photosynthetic efficiency as a function of needle dehydration for PIN_PON
(solid lines) and PIN_ARI (dashed lines) stems from four sites in the Santa Catalina
Mountains: MTL, PAL, RCL, and LIZ. Fitted curves are second-order polynomials.

0.5 1

0-45 1 o PIN_PON
o PIN_ARI

.0
a

_o
g

_o
on

0.25 1

.0
N

Chlorophyll fluorescence (Rf/Fm')
.0
5‘.

.0
_I

0.05 1

 

 

30 4'0 50 60 70 8‘0 90 100
Fresh mass (%)
Figure 5-16. Photosynthetic efficiency as a function of needle dehydration for PIN_PON

(solid lines) and PIN_ARI (dashed lines) stems across four sites in the Santa Catalina
Mountains. Fitted curves are second-order polynomials.

205

Xylem conductivity and vulnerability

Stems from trees lower in elevation (i.e., more xeric) had consistently
higher loss of xylem conductivity for a given imposed xylem tension (Figure 5-
17). PIN_ARI stems were also more vulnerable to cavitation than PIN_PON
stems, but neither site nor species was significant in the ANOVA (Table 5-7).
The interaction term was not calculated due to the extremely small sample size
at the LIZ (n=1) and RCL (n=2) sites; stern disintegration (i.e., explosion) inside
the centrifuge at moderate tensions (W=-2.5 MPa) prevented completion of
individual vulnerability curves for a number of trees. One-way ANOVA of species
effect at PAL only was also not significant (p=0.3527).

Xylem specific hydraulic conductivity was likewise not different between
species, but there was a clear (Figure 5-18) and significant (p=0.005) site effect
(Table 5-7). Sample sizes for non-PAL sites were substantially larger, but the
non-significant interaction term was removed from the final model. Based on
Tukey’s HSD comparison of means, K8 of MTL stems was significantly higher
than those from PAL (p=0.041) and RC/LIZ (p=0.005), while there was no
difference in K; between stems at PAL and RC/LIZ (p=0.348). Increased sample
size could have reduced the substantial variation in Ks found within the PIN_ARI

trees and thereby separate the lower elevation sites.

206

 

 

 

 

r 100 r 100
90 '\ . 90
OPIN PON- MTL \ IPIN_ARI - PAL
7 30 ' ‘ ‘\ IPIN ARI - LIZ/RCL ‘ 8°
OPIN PON- PAL 63 \ _ A
_ v \\ 33
70 £1 \ ~ 70 e
1% \\ jS
. \ L
60 a ‘ \ 50 g
8
50 o \ \ i 50 E
E \
.9 ‘ - 40 g
40 e \ i\
'6 \ \ ‘5
3° 8 \ ‘ i” is?
20 3 I \ \bs ~ 20 —’
\\I \K '
10 \\ q \ 1 10
r r r I o I T 1 Vi‘ O
-5 -4 -3 -2 1 o -5 4 -3 -2 -1 0
Xylem pressure potential (MPa) Xylem pressure poterlial (MPa)
r 100
<>\ " 9°
\\ OPIN_PON 80
\ EIPIN_ARI £5
E
‘5
.3.

 

 

 

Xylem pressue potertid (MPa)

 

Figure 5-17. Xylem vulnerability curves for PIN_PON (solid lines) and PIN_ARI (dashed
lines) stems collected from three sites in the Santa Catalina Mountains: MTL, PAL, and
LIZ/RCL. The bottom curve combines sites by species.

Table 5-7. ANOVA results for xylem pressure potential at 50% loss of hydraulic
conductivity (Woo) and xylem specific hydraulic conductivity (Ks) for PIN_PON and
PIN_ARI stems collected from three sites in the Santa Catalina Mountains: MTL, PAL,

and LIZ/RCL. Significant (a<0.05) terms are bolded.

 

 

Response Adj R2 F(p-value) Term Df SS F-value P-value

Wp 0.0053 0.3924 Site 2 0.4493 1.0180 0.3777
Species 1 0.2424 1 .0984 0.3060
Residuals 22 4.8543

K. 0.2383 0.0121 Site 2 2.27E-05 6.4777 0.0047
Species 1 9.70E-08 0.0553 0.8157
Residuals 29 5.08E-05

 

207

0.007 1

0.006 (

0.005 « i
0.003 - J

0.002 1

 

O
O
O
A
*—
——l

 

 

 

Xylem specific hydraulic conductivity
(kg rn’ MPa1 s‘)

0.001 1

 

 

 

 

 

 

 

 

 

 

0 T I 1r I
PIN_PON - MTL PIN_PON - PAL PIN_ARI - PAL PIN_ARI - RC/LIZ

Figure 5-18. Xylem specific hydraulic conductivity (Ks) for PIN_PON and PIN_ARI stems
collected from three sites in the Santa Catalina Mountains: MTL, PAL, and LIZ/RCL.
Values are means; bars are :1 sd.

Discussion
Seasonal gas exchange and xylem pressure potential

The results from this experiment support the hypothesis (Brown 1968,
Barton and Teeri 1993) that ponderosa pine, in this case Southwestern
ponderosa pine, avoids drought effects by maintaining positive water balance
during periods of low moisture availability and actively photosynthesizing during
favorable conditions. The relative species differences, especially from the LIJp
results, suggest that PIN_PON is more drought tolerant than PIN_ARI. These
differences are most evident during the winter and less obvious during the arid
foresummer. The 3-needled PIN_PON had consistently less negative Wp than 5-

needled PIN_ARI across all seasons, sites, and periods during the day (Figure 5-

208

4). These results support Haller’s (1965) findings that ponderosa pine with fewer
needles are associated with drought conditions, but they contrast with Malusa
(1992) who found that single-needled fascicles in hybrid pinyon pine (Pinus
califomiarum x P. edulis) had more negative WP than the double-needled
fascicles and concluded that needle number did not affect water relations. This
experiment found a significant difference in LPp, with the fewer-needled species
having lower Wp, thus these data support the conclusion that PIN_PON is more
drought tolerant than PIN_ARl based on differences in Wp.

Given the hypothesis by Monson and Grant (1989) that ponderosa pine
has adapted to drier habitats by lowering gs at the expense of reduced maximum
A, the more drought tOIerant of the two species in this study should have lower A
and 93, or Tr as measured here. Conclusions about absolute differences in gas
exchange should not be made from the limited number of paired diurnal
measurements in this experiment, but patterns and variation in these values can
be discussed. The WP data suggest that winter leaf desiccation is more severe
than during the arid foresummer (Figure 5-4); however, both species showed
positive A and Tr at the intermediate-elevation site (PAL) during the high-PAR
winter (Figure 5-1). On the other hand, 41,, did not change through the day for
high-elevation PIN_PON and low-elevation PIN_ARI, suggesting no change in
water status (i.e., no Tr) and possibly no A. Osmotic adjustment from cold
hardening can increase total leaf xylem water potential by around -2 MPa
(Abrams 1988), which is insufficient to compensate for differences observed here

between winter and the other seasons at any of the sites. Consequently, we can

209

assume that both species are photosynthetically active during the winter at the
intermediate PAL but less so at MTL and LIZ. Ascertaining the degree of
photoinhibition and photosynthetic down-regulation, measured partly by ratio and
total pool sizes of leaf violaxanthin-antherxanthin-zeaxanthin (Bjdrkman and
Demmig-Adams 1994), during winter across the three sites would contribute to
understanding more about cold temperature influences on the distributions of
these species.

While gas exchange during the monsoon appeared to be directly linked to
PAR, thus soil water was not considered to be limiting, gas exchange during the
arid foresummer was not related to PAR or Tleaf, but instead was likely related to
soil moisture (Bassman 1987). In ponderosa pine, Tr rates are typically higher
than other western conifers until a threshold soil '4’ (-0.2 MPa) is reached, after
which Tr for ponderosa pine decreases immediately, long before other conifers
(Bassman 1987). Greater stomatal control, or sensitivity to leaf water status,
leading to higher overall WUE, is considered the mechanism by which ponderosa
pine survive in more xeric habitats than expected given its higher Tr. In this
study, WP was similar for coincident PIN_PON and PIN_ARI at predawn and
midday, but PIN_PON had an average 42% higher standard deviation in daily A
measurements and average 62% higher standard deviation in daily Tr than did
PIN_ARl at PAL during the arid foresummer. These data support the conclusion
that PIN_PON is more drought tolerant than PIN_ARI based on more rapid

stomatal control of transpiration.

210

Density and architecture of stomata also exert considerable control over
gas exchange (Bassman 1987, Rundel and Yoder 1998). Increased stomatal
density, as well as increased needle surface area, should lead to increased Tr.
Stomatal density was compared for a limited number of needles from PIN_PON
and PIN_ARl; no significant (p>0.05) difference in density on any side of the
needle was found. Hadley (1986) found that epicuticular waxes had a profound
influence on stomatal conductivity. In a limited inspection of scanning electron
microscopy (SEM) images (courtesy of Anna Jacobsen, MSU) from PIN_PON
and PIN_ARl needles from PAL, abundant epicuticular wax is produced by both
species, with little observed difference (Figure 5-19). Removal of the waxes with

chloroform revealed a deeply sunken stomatal aperture (Figure 5-20).

 

Figure 5-19. Stomatal openings in PIN_PON (left) and PIN_ARI (right) by SEM. Note
the abundant epicuticular waxes surrounding the stomatal pit, as well as the network of
waxes above the aperture. Scale bar equal to 20 microns. Photos courtesy of Anna
Jacobsen.

211

 

Figure 5-20. Chloroform-cleared stomatal openings in PIN_ARl of top and transverse
views by SEM. Scale bar equal to 20 microns. Photos courtesy of Anna Jacobsen.

Integrated water-use efficiency and nutrient dynamics

Discrimination against heavier isotopes occurs in plants due to physical
properties of the heavier isotope and ecophysiological responses through
fractionation (Fry 2006). Less discrimination of 13C is associated with higher
WUE because plants that produce photosynthate with relatively higher 13C close
their stomata during stressful conditions, thereby increasing the intercellular-to-
atmospheric C02 concentration (ct/ca) and increasing relative 13C concentration
inside the leaf (Dawson et al. 2002). Under normal conditions, the higher
diffusivity (Melander and Saunders 1979) and reactivity with ribulose-1,5-
bisphospate carboxylase (Park and Epstein 1961) of 1"’0 results in discrimination
of 13C. When stomata close, ci decreases, relative concentration of 12C
decreases, more 13C is processed, and photosynthates are produced without

losing water; hence, plants with greater stomatal control should have higher

212

WUE and less negative 613C. In this experiment, 613C values were low (-25.2 to -
26.6 960) relative to ponderosa pine in Idaho (18.6960; Marshall and Zhang 1994)
but comparable to ponderosa pine in northern Arizona (-24 to -25.5%o at low and
high elevation; Adams and Kolb 2004). There was no significant difference in
6130 between species across years at the coincident PAL site (Table 5-4), but
inspection of the values from 2001 and 2002 lead to some speculation (Figure 5-
7). The years 2000 and 2002 were considered drought years; the manifestation
of drought was observed in the substantially shorter needles from these years
than from 2001 and 2003. In 2001, the PIN_PON needles had relatively less 130
than the PIN_ARI needles, while the converse was true in 2002, the drought
year. These data suggest that, with a larger sample size, PIN_ARl may in fact
have higher WUE than PIN_PON, but the data are sparse to make such an
inference. This conclusion departs from the supposition made after analyzing
the LPp and gas exchange results. Comparison of 6130 and %N for both species
across the elevational gradient would lead to greater insight on differential
response to drought (Adams and Kolb 2004).

Carbon (%C) and nitrogen (%N) content varied by year, while species
differences were observed only for %C (Figure 5-8). PIN_ARI had greater
allocation of mass to structural C across most years; the higher leaf surface area
to volume ratio for this 5-needled pine should increase the relative amount of C
allocated to structural than photosynthetic cells. The increase in %N in younger
needles likely represents N reallocation within the plant rather than less N

produced and stored in 2001-02 relative to later years. Foliar nitrogen is strongly

213

correlated to maximum A on a leaf mass (not area) basis for conifers (Reich et al.
1995). Although not significant, PIN_PON needles had higher %N than PIN_ARI
needles, suggesting a slightly higher potential photosynthetic capacity by
PIN_PON.

The nitrogen isotope discrimination (615M) results suggest differential
nitrogen resource dynamics between PIN_PON and PIN_ARI. Interestingly, 615N
decreased during the drought year 2002 for both species, but more
discrimination against 15N occurred in PIN_ARI trees in subsequent years (Figure
5-8). Discrimination of 15N in plants is much more complex than 13C (Dawson et
al. 2002). Nitrogen is normally acquired by plants through the soil. While
physical discrimination against the heavier isotope does not occur across living
membranes, enzyme-mediated processes can lead to discrimination (Dawson et
al. 2002). Low nitrogen availability, osmotic stress, or drought can lead to N
isotopic fractionation, but the mechanism remains unclear (Dawson et al. 2002).
Hobbie et al. (2000) suggest that fractionation occurs during transfer of nitrogen
from mycorrhizal fungi to plants, especially at low-nitrogen sites. Given the
significant effect of ectomycorrhizal fungi in the establishment, survival, and
nutrient acquisition for Pinus (Read 1998), the results of this experiment suggest
that these fungi may be influencing the nitrogen dynamics, and thus
photosynthetic capacity, of PIN_PON and PIN_ARI trees at PAL. Although
highly speculative, increased fractionation of 15N (i.e., more negative 515N) by
PIN_PON trees may suggest lower accessibility to N and thus lower potential

growth.

214

Photosynthetic function during dehydration

Needles of trees from lower in elevation lost less mass from dehydration,
or conversely had a lower relative water content, even when potentially fully
hydrated, than needles from trees higher in elevation within a species (Figure 5-
13). Furthermore, needles from lower elevation trees, especially PIN_ARI, had
lower PSll excitation capture efficiency than those originating from higher
elevation (Figure 5-15). Given the identical pretreatment conditions, these
results suggest a plastic response in total potential needle water storage and
photosynthetic function during extreme dehydration (i.e., drought) across the
elevational gradient.

PIN_PON needles lost a larger portion of their initial mass through
dehydration, suggesting that, despite full opportunity for hydration prior to the
experiment, PIN_PON needles had a higher storage capacity for water than do
PIN_ARl needles. Furthermore, for a given relative loss of water mass,
PIN_PON trees had higher PSII excitation capture efficiency through the
dehydration period (Figure 5-16). However, needle morphology was not
characterized. Higher-needled fascicles for a given fascicle diameter and length
have a geometrically higher surface area to volume ratio. The difference in total
stored water mass per fascicle, and thus water available for photosynthesis,
observed in this experiment could be accounted for by the difference in potential
storage volume due to areazvolume relationships, hence these results should be

cautiously interpreted.

215

Xylem conductivity and vulnerability

Vulnerability of the xylem to cavitation has been suggested to be the
primary driver for drought tolerance in plants (Tyree and Ewers 1991), but plants
have evolved alternative avoidance strategies that are also important. For
example, Pil‘lol and Sala (2000) showed that increased stomatal control of water
loss compensated for lower resistance to cavitation by ponderosa pine than by
codominant species. In this study, the lower elevation species, PIN_ARI, was
more vulnerable to cavitation than PIN_PON as determined by 50% loss of
conductivity as a function of imposed negative xylem pressure (Figure 5-17);
mean W50 for PIN_PON was -3.44 MPa and for PIN_ARI was -3.16 MPa. The
difference of -0.24 MPa is not substantial nor significant (Table 5—7). A mild site
effect was also discernible but not significant.

In a study of desert versus montane ponderosa pine populations,
vulnerability to cavitation did not differ, but xylem specific hydraulic conductivity
(K3) was higher for the more xeric trees (Maherali and DeLucia 2000). In this
study, K8 was highest for PIN_PON stems from the highest elevation site (MTL),
similar across species at PAL, and lowest for PIN_ARI at low elevation (Figure 5-
18). High Ks implies an increased capability to transport water due to lower
conductive resistance, whether clue to larger tracheids, more porous pit
membranes, or fewer cavitated tracheids. Standardizing for xylem area allows
interspecies comparisons in potential hydraulic conductance. However, Ks did
not differ for PIN_PON and PIN_ARI when coincident, while K8 was positively

correlated with elevation. Maherali et al. (2002) also found Ks to be

216

phenotypically plastic in ponderosa pine across a climatic gradient. The results
from this experiment indicate that K8 is similar for PIN_PON and PIN_ARI, that
the ensemble of traits contributing to K8 is sufficiently plastic to acclimate across
a steep climatic gradient within a species, and that drought stress is negatively

associated with elevation.

Implications of drought tolerance to distribution

The balance of evidence from this study, composed of four experiments,
suggests that PIN_PON is more drought tolerant than PIN_ARI. With only the
exception of PIN_ARl trees at LIZ during the monsoon season, PIN_PON trees
had less negative ‘41,, at all periods of the day and across major seasons (Figure
5-6). PIN_PON trees exhibited greater variation in A during the arid foresummer
than did PIN_ARI trees (Figure 5-2); this result could be interpreted as PIN_PON
stomata responding more closely to fluctuations in soil W or as PIN_PON trees
being closer to the drought threshold at that time of year. However, the less
negative “1,, for PIN_PON trees suggests the former interpretation. Neither
species appears to be water-limited during the monsoon season; rather light is
limiting to A (Figure 5-3). When exposed to severe drought (i.e., dehydration),
PIN_PON needles retain a higher level photosynthetic function than do PIN_ARI
needles (Figure 5-16). Although not significant, PIN_PON trees had higher %N,
which is correlated with maximum A and therefore growth potential. Conversely,
PIN_PON needles contain a relatively higher concentration of 15N, suggesting a

larger amount of fractionation (or processing) occurs before N reaches the leaf;

217

this could indicate drought stress or, alternatively, different access to soil N pools
than experienced by PIN_ARI trees. However, in sum, the results from this study
suggest that PIN_PON trees are more drought tolerant than PIN_ARl trees.

lf drought tolerance is not limiting the lower elevational distribution of
PIN_PON, then what factors are limiting its range? This study did not examine
potential competition nor the role of drought tolerance on seedlings, which are
more susceptible to low soil water availability due to their developing root
systems. In controlled greenhouse conditions as well as reciprocal in situ
plantings, Barton and Teeri (1993) investigated drought tolerance of
Southwestern conifers, excluding Arizona pine, and found that lower elevational
distributions were determined by drought tolerance of seedlings. A similar
approach with the taxa from this study would allow for standardized comparisons
among these and other Southwestern species. Likewise, Daubenmire (1943)
saw soil water availability as the limiting factor to Rocky Mountain conifers.
Grasses and forbs are significant competitors against ponderosa pine seedlings
for belowground resources, especially water and available nitrogen (White 1985,
Elliott and White 1987, Riegel et al. 1992, and Kolb and Robberecht 1996).
Seedling mortality due to moisture stress, in part from competition for water by all
plants, can be over 90% within the first year (Elliott and White 1987). Relative
differences in seedling root growth rates significantly affect survival (Kolb and
Robberecht 1996). Although seedlings in the field were preferred subjects for
this study, extensive recent ground fires, especially through the transition zone in

the Santa Catalina Mountains, eliminated all small diameter seedlings and many

218

saplings, but the fires also created more habitat for grass, forb, and pine
regeneration (personal observation).

Stomatal control of water relations appear key to separating ponderosa
pine from other conifers of the Rocky Mountains. Given the purported warm,
humid origins of Arizona pine (Millar 1993) relative to the cooler, drier
evolutionary origin for ponderosa pine (Axelrod and Raven 1985, Millar 1993),
investigations into differences in stomatal control may shed insight on how the
two species control water loss, especially during the predictable arid foresummer.
Since roots directly acquire water and since ponderosa pine are known for
extensive root systems, differential rooting architecture, water uptake, and
nutrient absorption may be important in segregating species distribution across
the shallow soils of the Santa Catalina Mountains. The intriguing nitrogen
isotope data suggest that differential mycorrhizal relationships may influence
nitrogen uptake (Perry et al. 1989) and consequent photosynthetic capacity and
WUE. The value of the elevational gradient and transition zone in this region
should not be overlooked when examining distributions of related species. As
indicated by this study, a number of responses to drought can be integrated to
contribute to an understanding of species distributions; for example, spatial
analysis of indicators, including integrative measures like 613C and tree-rings,

can yield models to predict expected changes in distribution from climate change.

219

CHAPTER 6
GENERAL CONCLUSIONS

The objectives for this dissertation research were to characterize the
spatial distribution and examine ecophysiological responses that could affect the
respective distributions of the Ponderosae in the Santa Catalina Mountains of
southern Arizona. A priori expectations were that Southwestern ponderosa pine
were distributed higher in elevation than the evolutionarily close and spatially
overlapping Arizona pine because the former species is less drought tolerant and
the latter taxon is less cold tolerant. Differences in distribution and cold tolerance
met these expectations, while results from the drought tolerance work
demonstrated the opposite expectation. In general, Southwestern ponderosa
pine was better able to maintain positive net photosynthesis and water balance
during periods of low soil water availability, both during winter and the arid
foresummer.

Most Southwestern ponderosa pine were indeed located at higher
elevation than Arizona pine trees, with a few low-elevation occurrences by the
former species. As expected, spatial modeling of sampled trees by species, or
morphotype to avoid taxonomic debate, suggested that Arizona pine and the
Mixed morphotype are limited in upper and lower range by cold temperatures
and climatic variables contributing to drought during the arid foresummer,
respectively. Because the upper elevational range was not reached by

Southwestern ponderosa pine in the Santa Catalina Mountains, its distribution

220

was understandably limited most by arid foresummer conditions. However,
Southwestern ponderosa pine and the Mixed morphotype had higher probability
of occurrence at higher elevation as compared to the distribution predicted for
Arizona pine, while both Arizona pine and the Mixed morphotype had higher
probability of occurrence at lower elevation as compared to the distribution
predicted for Southwestern ponderosa pine. Since lower elevational limits were
based on the same climatic factors, the higher placement of Southwestern
ponderosa pine suggests a decreased ability to establish at its lower margin,
where drought is more severe and Arizona pine is sympatric. Furthermore, the
presence of the Mixed morphotype above the upper margin for Arizona pine and
below the lower margin for Southwestern ponderosa pine suggests that this
group possesses unique genetic adaptations to a broader climatic range than the
other taxa.

The ecophysiological experiments indicate that Southwestern ponderosa
pine is undoubtedly more cold tolerant than Arizona pine. Arizona pine seed had
much lower germination rate, especially at high elevation, while Southwestern
ponderosa pine seed germinated equally well across elevations. Survival of
germinants at both sites through the first full arid foresummer (2007) has yet to
be determined. Sapling Southwestern ponderosa pine had higher photosynthetic
function and rate and less negative leaf xylem pressure potential during the
winter than did Arizona pine saplings, demonstrating less cold tolerance of
Arizona pine trees; in one experiment, seedlings of the Mixed morphotype in

frozen soil water exhibited photosynthetic function intermediate to Southwestern

221

ponderosa and Arizona pine seedlings. Additionally, fully hardened Arizona pine
seedlings had higher threshold freezing temperatures than did similarly hardened
Southwestern ponderosa pine seedlings, thereby further supporting the
conclusion of greater genetic cold tolerance by Southwestern ponderosa pine.

The studies examining indicators of drought tolerance offered no clear
sign of Arizona pine being more drought tolerant than Southwestern ponderosa
pine; in fact, most evidence suggested the opposite trend. Southwestern
ponderosa pine had less negative leaf xylem pressure potentials across all times
of clay and seasons, lower vulnerability to xylem cavitation in branches, higher
immediate response to variations in climatic conditions during the arid
foresummer, and higher photosynthetic function during dehydration. These
indications suggest that Southwestern ponderosa pine is more drought tolerant
than Arizona pine. Although not statistically significant, Southwestern ponderosa
pine had higher foliar nitrogen across all drought and non-drought years, possibly
indicating a higher maximum photosynthetic capacity. No difference in integrated
water-use efficiency, as measured by carbon isotopic discrimination, was found
between. sympatric species; however, sample sizes were small, and application
of this technique across the elevational gradient may detect species x site
differences. Differential leaf nitrogen isotope composition suggests that these
species differ in their nitrogen uptake pathway, and, although nitrogen dynamics
are highly complex, combining this type of analysis with studies of rooting

architecture and mycorrhizal associations may contribute more to understanding

222

lower elevational distribution of these taxa in the nitrogen-poor granitic soils of
the Santa Catalina Mountains.

The rapidly developing field of species distribution modeling offers tools
for inferring evolutionary processes, tracing population migration, and predicting
responses to climate change. Heretofore unseen in the literature, a thorough
spatial analysis of genetically linked morphological traits and combined
cytoplasmic and nuclear DNA of the Ponderosae would contribute to an
understanding of the systematics and evolution of this complex in the Southwest.
In this dissertation, the unique spatial and modeled niche of trees producing
intermediate needle numbers (“Mixed”) relative to Southwestern ponderosa and
Arizona pine do not support the Taxon X hypothesis; instead, these results
support the accepted taxonomy of the parent species (Conkle and Critchfield
1988, Price et al. 1998) and putative introgression (Peloquin 1984, Rehfeldt et al.
1996, Epperson et al. 2001). Further, the high intratree variance in the highly
heritable morphologic feature of number of needles per fascicle (Rehfeldt et al.
1996) draws attention to intermediate, or putative hybrid, morphotypes.
Population migration can be traced, for example, by back-calibrating models
without the outlying low-elevation 3-needled trees to determine the likelihood of
their being part of, or date of last contact with, the current Southwestern
ponderosa pine population. Potential low-elevation areas containing “old” trees
could be identified for dendrochronological research, or, in longer time periods,
the progression of ponderosa pine through the region could be modeled and

cross-calibrated with packrat midden data. Conversely, output from predictive

223

models can be projected to new scenarios, whether they be other mountain
islands in the Southwest or altered climate, to generate probabilities of suitable
habitat for the modeled species. Using ecophysiological methods to confirm the
importance of climatic and other variables (e.g, mycorrhizal associations) can
only improve variable selection and directions for continued research on dynamic

species distributions.

224

APPENDIX

225

Table A-1. Geographic coordinates and mean needle number for trees from the
distribution study ("SCAT"). Universal Transverse Mercator (UTM) coordinates are for
Zone 128 projected to World Geodetic System 1984. Elevation was interpolated from
Topozone.com. Morphotype is based on Peloquin (1984): Pinus ponderosa var.
scopulorum (PIN_PON), P. arizonica (PIN_ARI), and putative hybrid (Mixed).

 

 

 

226

Mean

Elevation needle
WP UTM-X UTM—Y (m) number Morphotype
1 518198 3588043 2598.4 3.05 PIN_PON
1 518198 3588043 2598.4 3.63 Mixed
1 518198 3588043 2598.4 3.40 Mixed
1 518198 3588043 2598.4 3.61 Mixed
2 517666 3588386 2484.1 4.99 PIN_ARI
2 517666 3588386 2484.1 5.01 PIN_ARI
2 517666 3588386 2484.1 4.92 PIN_ARI
2 517666 3588386 2484.1 5.00 PIN_ARI
3 517260 3588735 2430.2 4.96 PIN_ARI
3 517260 3588735 2430.2 4.53 Mixed
3 517260 3588735 2430.2 4.88 PIN_ARI
3 517260 3588735 2430.2 5.05 PIN_ARI
4 516955 3589032 2346.0 4.86 PIN_ARI
4 516955 3589032 2346.0 5.00 PIN_ARI
4 516955 3589032 2346.0 4.89 PIN_ARI
4 516955 3589032 2346.0 4.91 PIN_ARI
5 516823 3589263 2368.8 5.00 PIN_ARI
5 516823 3589263 2368.8 4.98 PIN_ARI
5 516823 3589263 2368.8 4.65 PIN_ARI
5 516823 3589263 2368.8 5.00 Pl N_ARI
6 517210 3590125 2193.1 4.97 PIN_ARI
6 517210 3590125 2193.1 4.78 PIN_ARI
6 517210 3590125 2193.1 4.93 PIN_ARI
6 517210 3590125 2193.1 4.75 PIN_ARI
7 517328 3590761 2266.3 4.80 PIN_ARI
7 517328 3590761 2266.3 4.82 PIN_ARI
7 517328 3590761 2266.3 4.78 PIN_ARI
7 517328 3590761 2266.3 4.88 PIN_ARI
7 517328 3590761 2266.3 4.86 PIN_ARI
8 517298 3590996 2252.1 4.95 PIN_ARI
8 517298 3590996 2252.1 4.08 Mixed
8 517298 3590996 2252.1 4.03 Mixed
8 517298 3590996 2252.1 4.97 PIN_ARI
9 517636 3592413 2221.3 4.90 PIN_ARI
9 517636 3592413 2221.3 3.93 Mixed
9 517636 3592413 2221 .3 4.47 Mixed
9 517636 3592413 2221.3 4.59 Mixed
10 517417 3593325 2200.2 4.96 PIN_ARI
10 517417 3593325 2200.2 4.37 Mixed
10 517417 3593325 2200.2 4.74 PIN_ARI
10 517417 3593325 2200.2 4.54 Mixed
1 1 517429 3593667 2172.7 3.00 PIN_PON
__11 51329 J 3593667 2172.7 3.00 PIN_:PQN

Table A-1. Continued.

 

 

 

 

227

Mean

Elevation needle
WP UTM-X UTM-Y (m) number Morphotype
11 517429 3593667 2172.7 3.02 PIN_PON
11 517429 3593667 2172.7 3.14 PIN_PON
17 525172 3587947 2251.3 4.99 PIN_ARI
17 525172 3587947 2251.3 4.71 PIN_ARI
18 525234 3588017 2246.9 4.53 Mixed
18 525234 3588017 2246.9 4.90 PIN_ARI
18 525234 3588017 2246.9 5.01 PIN_ARI
18 525234 3588017 2246.9 4.85 PIN_ARI
19 525617 3588455 2110.2 4.51 Mixed
19 525617 3588455 2110.2 4.80 PIN_ARI
19 525617 3588455 2110.2 4.37 Mixed
19 525617 3588455 2110.2 4.24 Mixed
20 525467 3588808 2060. 1 4. 88 PIN_ARI
20 525467 3588808 2060. 1 4.24 Mixed
20 525467 3588808 2060.1 4.98 PIN_ARI
20 525467 3588808 2060.1 4.60 Mixed
21 524432 3588773 2080.4 4. 81 PIN_ARI
21 524432 3588773 2080.4 4.77 PIN_ARI
21 524432 3588773 2080.4 4.55 Mixed
21 524432 3588773 2080.4 4.78 PIN_ARI
22 524420 3590284 2083.4 4.72 PIN_ARI
22 524420 3590284 2083.4 4.66 PIN_ARI
22 524420 3590284 2083.4 5. 00 PIN_ARl
22 524420 3590284 2083.4 4.91 PIN_ARI
23 524703 3590316 2083.6 4.80 PIN_ARI
23 524703 3590316 2083.6 4.70 PIN_ARI
23 524703 3590316 2083.6 4.98 PIN_ARI
23 524703 3590316 2083.6 4.94 PIN_ARI
24 524236 3590600 2134.2 4.72 PIN_ARI
24 524236 3590600 2134.2 4.65 PIN_ARI
24 524236 3590600 2134.2 4.69 PIN_ARI
24 524236 3590600 2134.2 4.99 PIN_ARI
25 523595 3590662 2256.7 4.60 Mixed
25 523595 3590662 2256.7 4.85 PIN_ARI
25 523595 3590662 2256.7 4.94 PIN_ARI
25 523595 3590662 2256.7 4. 79 PIN_ARI
26 523343 3590272 2328.0 5.00 PIN_ARI
26 523343 3590272 2328.0 4.99 PIN_ARI
26 523343 3590272 2328.0 4.47 Mixed
26 523343 3590272 2328.0 4.86 PIN_ARI
28 528669 3582880 2133.7 4.91 PIN_ARI
28 528669 3582880 2133.7 4. 70 PIN_ARI
28 528669 3582880 2133.7 4.54 Mixed
28 528669 3582880 2133.7 4.78 PIN_ARI
29 528529 3582872 2192.9 3.74 Mixed
29 528529 3582872 2192.9 4.97 PIN_ARI
_29 528529 3582872 2192.9 4.84 . __ _ PINARI .

Table A-1. Continued.

 

 

Mean
Elevation needle

WP UTM-X UTM-Y (m) number Morghotype
29 528529 3582872 2192.9 4.77 PIN_ARI
30 528850 3582967 2135.3 4.93 PIN_ARI
30 528850 3582967 2135.3 4.77 PIN_ARI
30 528850 3582967 2135.3 4.32 Mixed

30 528850 3582967 2135.3 4.97 PIN_ARI
31 528339 3583418 2111.6 4.62 PIN_ARI
31 528339 3583418 2111.6 4.90 PIN_ARI
31 528339 3583418 2111.6 4.91 PIN_ARI
31 528339 3583418 2111.6 4.66 PIN_ARI
32 528636 3584094 2195.4 4.94 PIN_ARI
32 528636 3584094 2195.4 4.95 PIN_ARI
32 528636 3584094 2195.4 4.71 PIN_ARI
32 528636 3584094 2195.4 4.99 PIN_ARI
33 527366 3583739 2131.1 5.39 PIN_ARI
34 527280 3583339 2125.7 3.64 Mixed

34 527280 3583339 2125.7 5.04 PIN_ARI
34 527280 3583339 2125.7 4.72 PIN_ARI
34 527280 3583339 2125.7 4.95 PIN_ARI
35 525564 3586049 2504.2 3.56 Mixed

35 525564 3586049 2504.2 3.00 PIN_PON
35 525564 3586049 2504.2 3.02 PIN_PON
35 525564 3586049 2504.2 3.22 Mixed

36 525571 3585999 2492.3 3.07 PIN_PON
36 525571 3585999 2492.3 3.00 PIN_PON
37 526461 3585865 2400.7 5.15 PIN_ARI
37 526461 3585865 2400.7 5.04 PIN_ARI
37 526461 3585865 2400.7 4.61 PIN_ARI
37 526461 3585865 2400.7 4.99 PIN_ARI
37 526461 3585865 2400.7 5.01 PIN_ARI
38 527040 3585860 2428.2 4.97 PIN_ARI
38 527040 3585860 2428.2 4.79 PIN_ARI
38 527040 3585860 2428.2 4.96 PIN_ARI
38 527040 3585860 2428.2 4.99 PIN_ARI
39 526097 3586082 2487.4 3.46 Mixed

39 526097 3586082 2487.4 3.05 PIN_PON
39 526097 3586082 2487.4 4.91 PIN_ARI
39 526097 3586082 2487.4 3.06 PIN_PON
40 526177 3586133 2526.3 3.13 PIN_PON
40 526177 3586133 2526.3 3.38 Mixed

40 526177 3586133 2526.3 4.07 Mixed

40 526177 3586133 2526.3 3.01 PIN_PON
41 526231 3586172 2546.1 3.24 Mixed

41 526231 3586172 2546.1 3.02 PIN_PON
41 526231 3586172 2546.1 3.91 Mixed

41 526231 3586172 2546.1 3.37 Mixed

42 526206 3586225 2572.3 3.70 Mixed

42 526206 _3586225 2572.3 3.52_____ y Mixed ..

 

 

228

 

Table A-1. Continued.

 

 

Mean

Elevation needle
WP UTM-X UTM-Y (m) number Morphotype
42 526206 3586225 2572. 3 3. 00 PIN_PON
42 526206 3586225 2572.3 3.00 PIN_PON
43 524405 3588019 2335.8 3.21 Mixed
43 524405 3588019 2335.8 3.05 PIN_PON
43 524405 3588019 2335.8 3.29 Mixed
43 524405 3588019 2335.8 2.99 PIN_PON
44 524395 3587937 2342. 5 5.01 PIN_ARI
44 524395 3587937 2342. 5 3.24 Mixed
44 524395 3587937 2342.5 4. 99 PIN_ARI
44 524395 3587937 2342. 5 3.00 PIN_PON
47 525002 3587025 2475.0 3.00 PIN_PON
47 525002 3587025 2475.0 3.36 Mixed
47 525002 3587025 2475.0 3.02 PIN_PON
47 525002 3587025 2475.0 3.01 PIN_PON
48 524884 3586716 2499.4 3.60 Mixed
48 524884 3586716 2499.4 3.23 Mixed
48 524884 3586716 2499.4 3.03 PIN_PON
48 524884 3586716 2499.4 3.08 PIN_PON
49 525728 3586553 2547.8 3.26 Mixed
49 525728 3586553 2547.8 3.00 PIN_PON
49 525728 3586553 2547.8 3.01 PIN_PON
49 525728 3586553 2547.8 3.02 PIN_PON
50 526064 3586493 2541 .3 3.34 Mixed
50 526064 3586493 2541 .3 3.03 PIN_PON
50 526064 3586493 2541.3 4.13 Mixed
50 526064 3586493 2541 .3 4.08 Mixed
50 526064 3586493 2541 .3 4. 34 Mixed
51 526931 3586640 2581 .4 3.03 PIN_PON
51 526931 3586640 2581.4 3.04 PIN_PON
51 526931 3586640 2581.4 3.01 PIN_PON
51 526931 3586640 2581.4 3.01 PIN_PON
52 529524 3584657 2198.4 4.95 PIN_ARI
52 529524 3584657 2198.4 4.98 PIN_ARI
52 529524 3584657 2198.4 4.95 PIN_ARI
52 529524 3584657 2198.4 5.00 PIN_ARI
53 529829 3584400 2193.1 5.03 PIN_ARI
53 529829 3584400 2193.1 4.99 PIN_ARI
53 529829 3584400 2193.1 4.92 PIN_ARI
53 529829 3584400 2193.1 4.79 PIN_ARI
54 530378 358451 1 2010.5 4.64 PIN_ARI
54 530378 358451 1 2010.5 4.95 PIN_ARI
54 530378 3584511 2010.5 4.64 PIN_ARI
54 530378 358451 1 2010.5 4.94 PIN_ARI
55 530828 3584110 2110.0 5.08 PIN_ARI
55 530828 3584110 2110.0 4.82 PIN_ARI
55 530828 3584110 2110.0 4.97 PIN_ARI
55 530828 35841107, g1 10 0 _4_.81 _ _ PIN:ARI_ _ _

 

Table A-1. Continued.

 

 

 

 

 

230

Mean

Elevation needle
WP UTM-X UTM-Y (m) number Mclphotype
57 531064 3583808 2162.7 4.96 PIN_ARI
57 531064 3583808 2162.7 4.92 PIN_ARI
57 531064 3583808 2162.7 4.42 Mixed
57 531064 3583808 2162.7 4.99 PIN_ARI
58 531692 3583274 2212.7 4.03 Mixed
58 531692 3583274 2212.7 4.11 Mixed
58 531692 3583274 2212.7 4.38 Mixed
58 531692 3583274 2212.7 3.81 Mixed
59 532261 3583539 2171.8 4.20 Mixed
59 532261 3583539 2171.8 4.98 PIN_ARI
59 532261 3583539 2171.8 5.00 PIN_ARI
59 532261 3583539 2171.8 4.46 Mixed
60 530739 3583800 2073.1 4.98 PIN_ARI
60 530739 3583800 2073.1 4.99 PIN_ARI
60 530739 3583800 2073.1 4.80 PIN_ARI
60 530739 3583800 2073.1 4.82 PIN_ARI
61 530500 3583244 2031.5 4.52 Mixed
61 530500 3583244 2031.5 4.96 PIN_ARI
61 530500 3583244 2031.5 4.96 PIN_ARI
61 530500 3583244 2031.5 4.87 PIN_ARI
62 530500 3583244 2031 .5 4.82 PIN_ARI
62 530500 3583244 2031 .5 4.96 PIN_ARI
62 530500 3583244 2031 .5 4.95 PIN_ARI
62 530500 3583244 2031 .5 5.00 PIN_ARI
65 523114 3590540 2378.6 4.86 PIN_ARI
65 523114 3590540 2378.6 4.99 PIN_ARI
65 523114 3590540 2378.6 4.84 PIN_ARI
65 523114 3590540 2378.6 3.44 Mixed
66 523015 3590116 2413.9 3.74 Mixed
66 523015 3590116 2413.9 3.40 Mixed
66 523015 3590116 2413.9 3.01 PIN_PON
67 522749 3589887 2406.1 3.23 Mixed
67 522749 3589887 2406.1 3.05 PIN_PON
67 522749 3589887 2406.1 3.00 PIN_PON
67 522749 3589887 2406.1 3.08 PIN_PON
68 523072 3589232 2438.0 4.91 PIN_ARI
68 523072 3589232 2438.0 3.00 PIN_PON
68 523072 3589232 2438.0 3.33 Mixed
68 523072 3589232 2438.0 3.00 PIN_PON
68 523072 3589232 2438.0 4.99 PIN_ARI
69 523212 3589207 2436.1 6.50 PIN_ARI
69 523212 3589207 2436.1 3.00 PIN_PON
69 523212 3589207 2436.1 3.33 Mixed
69 523212 3589207 2436.1 3.03 PIN_PON
70 521773 3590551 2398.3 5.00 PIN_ARI
70 521773 3590551 2398.3 4.99 PIN_ARI
‘70 521773 3590551 2398.3 5.23 “__PlNgARLH

Table A-1. Continued.

 

 

Mean

Elevation needle
WP UTM-X UTM-Y (m) number Morphotype
70 521773 3590551 2398.3 5.02 PIN_ARI
71 521997 3592420 2069.5 4.87 PIN_ARI
71 521997 3592420 2069.5 4.65 PIN_ARI
71 521997 3592420 2069.5 4.88 PIN_ARI
71 521997 3592420 2069.5 4.88 PIN_ARI
72 522002 3593674 1745.0 3.00 PIN_PON
72 522002 3593674 1745.0 3.00 PIN_PON
72 522002 3593674 1745.0 3.00 PIN_PON
72 522002 3593674 1745.0 3.00 PIN_PON
73 523628 3593676 2127.0 3.1 1 PIN_PON
73 523628 3593676 2127.0 3.07 PIN_PON
73 523628 3593676 2127.0 2.98 PIN_PON
73 523628 3593676 2127.0 3.02 PIN_PON
74 524046 3592746 2295.7 3.23 Mixed
74 524046 3592746 2295.7 3.04 PIN_PON
74 524046 3592746 2295.7 3.01 PIN_PON
74 524046 3592746 2295.7 3.00 PIN_PON
75 523059 3587849 2268.4 3.05 PIN_PON
75 523059 3587849 2268.4 2.99 PIN_PON
75 523059 3587849 2268.4 3.00 PIN_PON
75 523059 3587849 2268.4 3.00 PIN_PON
76 522747 3588693 2321 .7 3.49 Mixed
76 522747 3588693 2321 .7 3.29 Mixed
76 522747 3588693 2321 .7 3.24 Mixed
76 522747 3588693 2321 .7 3.05 PIN_PON
77 522552 3589178 2344.7 3.01 PIN_PON
77 522552 3589178 2344.7 3.00 PIN_PON
77 522552 3589178 2344.7 3.02 PIN_PON
77 522552 3589178 2344.7 3.00 PIN_PON
78 521964 3590069 2422.0 3.03 PIN_PON
78 521964 3590069 2422.0 3.00 PIN_PON
78 521964 3590069 2422.0 3.06 PIN_PON
78 521964 3590069 2422.0 3.35 Mixed
79 521731 3590186 2465.4 3.22 Mixed
79 521731 3590186 2465.4 3.03 PIN_PON
79 521731 3590186 2465.4 3.01 PIN_PON
79 521731 3590186 2465.4 2.99 PIN_PON
80 520297 3590584 2572.5 3.00 PIN_PON
80 520297 3590584 2572.5 4.31 Mixed
80 520297 3590584 2572.5 3.25 Mixed
80 520297 3590584 2572.5 3.06 PIN_PON
81 520952 3589507 2680.4 3.13 PIN_PON
81 520952 3589507 2680.4 3.1 1 PIN_PON
81 520952 3589507 2680.4 4.99 PIN_ARI
81 520952 3589507 2680.4 3.99 Mixed
82 520361 3589357 2756.9 4.02 Mixed
82 529361 3589357 2756.79 3.64 _Mixed __

231

Table A-1. Continued.

 

 

Mean

Elevation needle
WP UTM-X UTM-Y (m) number Morphotype
82 520361 3589357 2756.9 3.06 PIN_PON
82 520361 3589357 2756.9 3.68 Mixed
83 521521 3589530 2625.6 3.00 PIN_PON
85 523068 3587618 2314.2 4.85 PIN_ARI
85 523068 3587618 2314.2 4.97 PIN_ARI
85 523068 3587618 2314.2 4.98 PIN_ARI
85 523068 3587618 2314.2 4.98 PIN_ARI
86 522678 3587265 2410.5 5.00 PIN_ARI
86 522678 3587265 2410.5 5.07 PIN_ARI
86 522678 3587265 2410.5 5.10 PIN_ARI
86 522678 3587265 2410.5 5.00 PIN_ARI
87 521895 3587265 2472.8 4.64 PIN_ARI
87 521895 3587265 2472.8 5.00 PIN_ARI
87 521895 3587265 2472.8 4.86 PIN_ARI
87 521895 3587265 2472.8 4.89 PIN_ARI
88 521510 3587614 2478.7 4.99 PIN_ARI
88 521510 3587614 2478.7 5.00 PIN_ARI
88 521510 3587614 2478.7 4.98 PIN_ARI
88 521510 3587614 2478.7 5.00 PIN_ARI
89 521313 3588163 2434.8 4.90 PIN_ARI
89 521313 3588163 2434.8 4.52 Mixed
89 521313 3588163 2434.8 4.97 PIN_ARI
89 521313 3588163 2434.8 4.64 PIN_ARI
90 521837 3588153 2363.9 3.00 PIN_PON
90 521837 3588153 2363.9 3.04 PIN_PON
90 521837 3588153 2363.9 3.06 PIN_PON
90 521837 3588153 2363.9 3.00 PIN_PON
91 521198 3589037 2613.4 3.01 PIN_PON
91 521198 3589037 2613.4 3.02 PIN_PON
91 521198 3589037 2613.4 3.20 PIN_PON
91 521198 3589037 2613.4 3.70 Mixed
92 521161 3588666 2481.9 4.95 PIN_ARI
92 521161 3588666 2481.9 5.40 PIN_ARI
92 521161 3588666 2481.9 3.00 PIN_PON
92 521161 3588666 2481.9 4.60 PIN_ARI
93 520867 3588430 2372.2 4.98 PIN_ARI
93 520867 3588430 2372.2 4.95 PIN_ARI
93 520867 3588430 2372.2 3.51 Mixed
93 520867 3588430 2372.2 4.97 PIN_ARI
94 522536 3588172 2417.8 4.97 PIN_ARI
94 522536 3588172 2417.8 5.01 PIN_ARI
94 522536 3588172 2417.8 4.92 PIN_ARI
94 522536 3588172 2417.8 4.81 PIN_ARI
95 522408 3587860 2331 .1 5.00 PIN_ARI
95 522408 3587860 2331.1 3.00 PIN_PON
95 522408 3587860 2331.1 3.05 PIN_PON
95 522408 3587860 2331.1 5.00 _PIN ARI _ __ _

 

 

 

232

Table A-1. Continued.

 

 

Mean
Elevation needle

WP UTM-X UTM-Y Jim number Morphotype
97 519443 3589532 2714.6 3.11 PIN_PON
97 519443 3589532 2714.6 3.00 PIN_PON
97 519443 3589532 2714.6 3.00 PIN_PON
97 519443 3589532 2714.6 3.14 PIN_PON
98 525491 3584718 2305.2 4.92 PIN_ARI
98 525491 3584718 2305.2 4.94 PIN_ARI
98 525491 3584718 2305.2 4.86 PIN_ARI
98 525491 3584718 2305.2 4.95 PIN_ARI
99 525777 3584681 2317.9 4.96 PIN_ARI
99 525777 3584681 2317.9 4.89 PIN_ARI
99 525777 3584681 2317.9 5.01 PIN_ARl
99 525777 3584681 2317.9 5.00 PIN_ARI
100 525551 3584070 2291.8 4.93 PIN_ARI
100 525551 3584070 2291.8 4.98 PIN_ARI
100 525551 3584070 2291 .8 4.44 Mixed
100 525551 3584070 2291.8 4.94 PIN_ARI
101 526357 3585193 2365.6 4.98 PIN_ARI
101 526357 3585193 2365.6 4.41 Mixed

101 526357 3585193 2365.6 5.1 1 PIN_ARI
101 526357 3585193 2365.6 5.02 PIN_ARI
102 526350 3585532 2395.7 4.87 PIN_ARI
102 526350 3585532 2395.7 4.95 PIN_ARI
102 526350 3585532 2395. 7 4.97 PIN_ARI
102 526350 3585532 2395.7 5.00 PIN_ARI
103 525110 3585720 2478.0 4.60 PIN_ARI
103 5251 10 3585720 2478.0 4.55 Mixed
103 5251 10 3585720 2478.0 4.84 PIN_ARl
103 5251 10 3585720 2478.0 4.97 PIN_ARI
104 525208 3586246 2489.2 3.10 PIN_PON
104 525208 3586246 2489.2 3.97 Mixed
104 525208 3586246 2489.2 3.40 Mixed
104 525208 3586246 2489.2 3.59 Mixed
105 528133 3585823 2482.1 4.40 Mixed
105 528133 3585823 2482.1 4.78 PIN_ARI
105 528133 3585823 2482.1 4.98 PIN_ARI
106 528204 3586238 2313.3 4.90 PIN_ARI
106 528204 3586238 2313.3 4.96 PIN_ARI
106 528204 3586238 2313.3 4.95 PIN_ARI
107 527998 3586344 2340.9 4.93 PIN_ARI
107 527998 3586344 2340.9 4.98 PIN_ARI
107 527998 3586344 2340.9 4.38 Mixed
107 527998 3586344 2340.9 4.85 PIN_ARI
108 527884 3586038 2467.4 5.1 1 PIN_ARI
108 527884 3586038 2467.4 5.45 PIN_ARI
108 527884 3586038 2467.4 4.57 Mixed
108 527884 3586038 2467.4 4.92 PIN_ARI
#109 524358 3585856 2355.0 4.99 WPIN_ARI

 

233

 

Table A-1. Continued.

 

 

Mean

Elevation needle
WP UTM-X UTM-Y (m) number Morphotype
109 524358 3585856 2355.0 4.95 PI N_ARI
109 524358 3585856 2355.0 4.97 Pl N_ARI
109 524358 3585856 2355.0 4.89 Pl N_ARI
1 10 524331 3586423 2379.9 4.99 PIN_ARI
1 10 524331 3586423 2379.9 4.79 PIN_ARI
1 10 524331 3586423 2379.9 5.00 PIN_ARI
1 10 524331 3586423 2379.9 4.83 PIN_ARI
1 11 524607 3586710 2419.0 4.90 PIN_ARI
1 11 524607 3586710 2419.0 4.88 PIN_ARI
1 1 1 524607 3586710 2419.0 3.72 Mixed
1 11 524607 3586710 2419.0 4.78 PIN_ARI
1 12 524527 3586971 2451.1 4.94 PIN_ARI
1 12 524527 3586971 2451 .1 3.00 PIN_PON
1 12 524527 3586971 2451.1 2.99 PIN_PON
1 12 524527 3586971 2451.1 4.49 Mixed
1 13 524083 3587031 2490.7 5.06 PIN_ARI
1 13 524083 3587031 2490.7 4.95 PIN_ARl
1 13 524083 3587031 2490.7 5.18 PIN_ARI
1 13 524083 3587031 2490.7 4.87 PIN_ARI
1 14 526916 3587500 2338.0 4.86 PIN_ARI
1 14 526916 3587500 2338.0 4.52 Mixed
1 14 526916 3587500 2338.0 4.98 PIN_ARI
1 15 527095 3587904 2309.0 4.65 PIN_ARI
1 15 527095 3587904 2309.0 4.58 Mixed
1 15 527095 3587904 2309.0 3.07 PIN_PON
1 15 527095 3587904 2309.0 4.85 PIN_ARl
1 16 527123 3588052 2232.9 4.90 PIN_ARI
1 16 527123 3588052 2232.9 4.95 PIN_ARI
1 16 527123 3588052 2232.9 4.74 PIN_ARI
1 17 526723 3588045 2172.7 4.97 PIN_ARI
1 17 526723 3588045 2172.7 4.76 PIN_ARI
117 526723 3588045 2172.7 5.13 PIN_ARI
1 18 526559 3587741 2108.5 5.00 PIN_ARI
1 18 526559 3587741 2108.5 4.66 PIN_ARI
1 18 526559 3587741 2108.5 4.88 PIN_ARI
1 19 525808 3587904 2142.4 4.99 PIN_ARI
1 19 525808 3587904 2142.4 5.02 PIN_ARI
1 19 525808 3587904 2142.4 4.80 PIN_ARI
120 527424 3586021 2419.8 4.81 PIN_ARI
120 527424 3586021 2419.8 4.99 PIN_ARI
120 527424 3586021 2419.8 4.97 PIN_ARI
120 527424 3586021 2419.8 4.98 PIN_ARI
121 527600 3586131 2451.6 4.96 PIN_ARI
121 527600 3586131 2451.6 4.95 PIN_ARI
121 527600 3586131 2451.6 5.00 PIN_ARI
121 527600 3586131 2451.6 4.75 PIN_ARI
122‘ _ 527450 J586192 __247_0_.8 4.94 PIN_ARI

 

234

Table A-1. Continued.

 

 

Mean

Elevation needle
WP UTM-X UTM-Y (rm number Morphotype
122 527450 3586192 2470.8 4.94 PIN_ARI
122 527450 3586192 2470.8 4.97 PIN_ARI
122 527450 3586192 2470.8 4.70 PIN_ARI
123 527388 3586363 2554.3 3.16 PIN_PON
123 527388 3586363 2554.3 2.99 PIN_PON
124 527265 3586402 2551.7 3.02 PIN_PON
124 527265 3586402 2551.7 3.14 PIN_PON
124 527265 3586402 2551 .7 3.42 Mixed
124 527265 3586402 2551.7 3.00 PIN_PON
125 527129 3586406 2521.1 3.26 Mixed
125 527129 3586406 2521.1 3.03 PIN_PON
125 527129 3586406 2521.1 3.04 PIN_PON
125 527129 3586406 2521.1 3.01 PIN_PON
126 527059 3586353 2508.2 3.01 PIN_PON
126 527059 3586353 2508.2 4.94 PIN_ARI
126 527059 3586353 2508.2 4.98 PIN_ARI
126 527059 3586353 2508.2 4.45 Mixed
127 519668 3589289 2768.9 3.06 PIN_PON
127 519668 3589289 2768.9 3.52 Mixed
127 519668 3589289 2768.9 3.08 PIN_PON
128 519615 3589131 2767.8 3.70 Mixed
128 519615 3589131 2767.8 3.68 Mixed
128 519615 3589131 2767.8 3.05 PIN_PON
128 519615 3589131 2767.8 3.08 PIN_PON
129 519183 3588867 2700.6 3.38 Mixed
129 519183 3588867 2700.6 3.00 PIN_PON
129 519183 3588867 2700.6 3.03 PIN_PON
129 519183 3588867 2700.6 3.63 Mixed
130 519136 3588734 2682.0 3.00 PIN_PON
130 519136 3588734 2682.0 3.00 PIN_PON
130 519136 3588734 2682.0 3.37 Mixed
130 519136 3588734 2682.0 3.02 PIN_PON
131 518926 3588551 2677.5 3.07 PIN_PON
131 518926 3588551 2677.5 3.38 Mixed
131 518926 3588551 2677.5 3.03 PIN_PON
131 518926 3588551 2677.5 2.93 PIN_PON
132 519369 3588742 2689.8 3.52 Mixed
132 519369 3588742 2689.8 3.97 Mixed
132 519369 3588742 2689.8 3.56 Mixed
132 519369 3588742 2689.8 2.99 PIN_PON
132 519369 3588742 2689.8 3.28 Mixed
133 519716 3588880 2712.0 3.01 PIN_PON
133 519716 3588880 2712.0 3.01 PIN_PON
133 519716 3588880 2712.0 3.14 PIN_PON
133 519716 3588880 2712.0 3.00 PIN_PON
134 519878 3588961 2730.1 3.00 PIN_PON
134 519878 3588961 2739.1 3.38 Mixed 7 a.--

235

Table A-1. Continued.

 

 

236

Mean

Elevation needle
WP UTM-X UTM-Y (m) number Morphotype
134 519878 3588961 2730.1 3.09 PIN_PON
134 519878 3588961 2730.1 3.42 Mixed
135 529463 3584453 2334.9 4.84 PIN_ARI
135 529463 3584453 2334.9 4.75 PIN_ARI
135 529463 3584453 2334.9 4.42 Mixed
135 529463 3584453 2334.9 4.77 PIN_ARI
136 529496 3584309 2402.2 4.38 Mixed
1 36 529496 3584309 2402.2 5.22 PIN_ARI
136 529496 3584309 2402.2 4.68 PIN__ARI
136 529496 3584309 2402.2 4.32 Mixed
137 529406 3584161 2407.9 4.60 PIN_ARI
137 529406 3584161 2407.9 5.00 PIN_ARI
137 529406 3584161 2407.9 4.91 PIN_ARI
137 529406 3584161 2407.9 5.01 PIN_ARI
138 529469 3584041 2396.2 4.86 PIN_ARI
138 529469 3584041 2396.2 4.99 PIN_ARI
138 529469 3584041 2396.2 4.67 PIN_ARI
138 529469 3584041 2396.2 5.00 PIN_ARI
139 529396 3583820 2370.2 4.31 Mixed
139 529396 3583820 2370.2 4.99 PIN_ARI
139 529396 3583820 2370.2 5.00 PIN_ARI
139 529396 3583820 2370.2 5.00 PIN_ARI
140 529244 3583659 2319.5 5.02 PIN_ARI
140 529244 3583659 2319.5 4.95 PIN_ARI
140 529244 3583659 2319.5 5.00 PIN_ARI
140 529244 3583659 2319.5 5.25 PIN_ARI
141 528978 3583449 2228.5 5.00 PIN_ARI
141 528978 3583449 2228.5 5.10 PIN_ARI
141 528978 3583449 2228.5 4.80 PIN_ARI
141 528978 3583449 2228.5 4.95 PIN_ARI
142 528739 3583426 2173.4 4.94 PIN_ARI
142 528739 3583426 2173.4 4.89 PIN_ARI
142 528739 3583426 2173.4 4.89 PIN_ARI
142 528739 3583426 2173.4 4.88 PIN_ARI
143 528664 3584663 2190.9 4.96 PIN_ARI
143 528664 3584663 2190.9 4.51 Mixed
143 528664 3584663 2190.9 4.95 PIN_ARI
143 528664 3584663 2190.9 4.95 PIN_ARI
144 527887 3584178 2167.9 4.85 PIN_ARI
144 527887 3584178 2167.9 4.83 PIN_ARI
144 527887 3584178 2167.9 4.99 PIN_ARI
144 527887 3584178 2167.9 4.87 PIN_ARI
145 527156 3583655 2145.8 4.92 PIN_ARI
145 527156 3583655 2145.8 4.86 PIN_ARI
145 527156 3583655 2145.8 4.92 PIN_ARI
145 527156 3583655 2145.8 4.65 PIN_ARI
_1:l§__.53844§ 358§ZZ§._24§_9.§_- -4-68 Plus:

Table A-1. Continued.

 

 

Mean

Elevation needle
WP UTM-X UTM-Y (m) number Morphotype
146 528445 3585778 2439.8 4.98 PIN_ARI
146 528445 3585778 2439.8 5.03 PIN_ARI
146 528445 3585778 2439.8 4.92 PIN_ARI
147 528272 3585635 2397.0 4.70 PIN_ARI
147 528272 3585635 2397.0 4.85 PIN_ARI
147 528272 3585635 2397.0 5.04 PIN_ARI
147 528272 3585635 2397.0 4.81 PIN_ARI
148 528482 3585409 2376.9 4.97 PIN_ARI
148 528482 3585409 2376.9 4.97 PIN_ARI
148 528482 3585409 2376.9 4.71 PIN_ARI
148 528482 3585409 2376.9 4.97 PIN_ARI
149 528480 3585146 2409.1 5.00 PIN_ARI
149 528480 3585146 2409.1 4.93 PIN_ARI
149 528480 3585146 2409.1 5.22 PIN_ARI
149 528480 3585146 2409.1 5.02 PIN_ARI
150 528592 3585010 2341.7 4.97 PIN_ARI
150 528592 3585010 2341.7 5.13 PIN_ARI
150 528592 3585010 2341.7 4.76 PIN_ARI
150 528592 3585010 2341.7 4.98 PIN_ARI
151 528877 3585124 2293.6 4.89 PIN_ARI
151 528877 3585124 2293.6 5.37 PIN_ARI
151 528877 3585124 2293.6 4.99 PIN_ARI
151 528877 3585124 2293.6 4.78 PIN_ARI
152 529181 3584828 2244.9 5.12 PIN_ARI
152 529181 3584828 2244.9 4.73 PIN_ARI
152 529181 3584828 2244.9 4.89 PIN_ARI
152 529181 3584828 2244.9 4.94 PIN_ARI
153 527783 3584801 2268.5 4.97 PIN_ARI
153 527783 3584801 2268.5 5.20 PIN_ARI
153 527783 3584801 2268. 5 4.93 PIN_ARI
153 527783 3584801 2268.5 5.00 PIN_ARI
154 527563 3585623 2334.1 4.98 PIN_ARI
154 527563 3585623 2334.1 4.96 PIN_ARI
154 527563 3585623 2334.1 4.96 PIN_ARI
154 527563 3585623 2334.1 5.03 PIN_ARl
155 528120 3585438 2310.3 4.96 PIN_ARI
155 528120 3585438 2310.3 4.70 PIN_ARI
155 528120 3585438 2310.3 4.99 PIN_ARI
155 528120 3585438 2310.3 4.97 PIN_ARI
1 56 525955 3586763 2556.7 3.63 Mixed
156 525955 3586763 2556.7 3.07 PIN_PON
156 525955 3586763 2556.7 3.00 PIN_PON
1 56 525955 3586763 2556. 7 3.20 Mixed
157 525565 3586272 2595.4 3.06 PIN_PON
157 525565 3586272 2595.4 3.13 PIN_PON
1 57 525565 3586272 2595.4 3.94 Mixed
_1_ 57 525565 3586272 25954 _3.07___7___ _ __PflflgPQN __

 

 

237

Table A-1. Continued.

 

 

Mean
Elevation needle

WP UTM-X UTM-Y (m) number Morphotype
158 525782 3586502 2561.0 3.15 PIN_PON
158 525782 3586502 2561.0 3.44 Mixed
158 525782 3586502 2561.0 4.01 Mixed
158 525782 3586502 2561.0 3.17 PIN_PON
159 525333 3586998 2432.5 3.08 PIN_PON
159 525333 3586998 2432.5 3.03 PIN_PON
159 525333 3586998 2432.5 3.01 PIN_PON
159 525333 3586998 2432.5 3.00 PIN_PON
160 520065 3589172 2769.7 3.22 Mixed
160 520065 3589172 2769.7 3.01 PIN_PON
160 520065 3589172 2769.7 3.01 PIN_PON
160 520065 3589172 2769.7 3.40 Mixed
161 526520 3586370 2558.6 3.06 PIN_PON
161 526520 3586370 2558.6 3.01 PIN_PON
162 526504 3586244 2564.1 3.06 PIN_PON
162 526504 3586244 2564.1 3.35 Mixed
162 526504 3586244 2564.1 3.24 Mixed
162 526504 3586244 2564.1 4.51 Mixed
163 525218 3586605 2507.9 3.00 PIN_PON
163 525218 3586605 2507.9 3.00 PIN_PON
163 525218 3586605 2507.9 3.01 PIN_PON
163 525218 3586605 2507.9 3.02 PIN_PON
164 524790 3587570 2407.7 3.71 Mixed
164 524790 3587570 2407.7 4.86 PIN_ARI
164 524790 3587570 2407.7 3.00 PIN_PON
165 523505 3588328 2439.0 3.20 Mixed
165 523505 3588328 2439.0 5.10 PIN_ARI
165 523505 3588328 2439.0 4.98 PIN_ARI
165 523505 3588328 2439.0 4.02 Mixed
166 528831 3583112 2125.6 4.76 PIN_ARI
166 528831 3583112 2125.6 3.10 PIN_PON
166 528831 3583112 2125.6 4.99 PIN_ARI
166 528831 3583112 2125.6 3.39 Mixed
167 529014 3583212 2197.0 4.99 PIN_ARI
167 529014 3583212 2197.0 4.91 PIN_ARI
167 529014 3583212 2197.0 4.93 PIN_ARI
167 529014 3583212 2197.0 5.00 PIN_ARI
168 529195 3583607 2309.8 5.00 PIN_ARI
168 529195 3583607 2309.8 4.96 PIN_ARI
168 529195 3583607 2309.8 5.03 PIN_ARI
168 529195 3583607 2309.8 5.38 PIN_ARI
169 528976 3583398 2221.8 4.99 PIN_ARI
169 528976 3583398 2221.8 4.78 PIN_ARI
169 528976 3583398 2221.8 4.88 PIN_ARI
169 528976 3583398 2221.8 4.73 PIN_ARI
170 519676 3587162 2210.6 4.46 Mixed
_170 519676 3587162 2210.6 4.89 _PlNJflM

 

 

238

Table A-1. Continued.

 

 

Mean

Elevation needle
WP UTM-X UTM-Y (m) number Morphotype
170 519676 3587162 2210.6 4.47 Mixed
170 519676 3587162 2210.6 5.00 PIN_ARI
171 518348 3586331 2130.8 5.00 PIN_ARI
171 518348 3586331 2130.8 4.86 PIN_ARI
171 518348 3586331 2130.8 5.00 PIN_ARI
171 518348 3586331 2130.8 4.99 PIN_ARI
172 517083 3585705 2233.9 4.98 PIN_ARI
172 517083 3585705 2233.9 5.1 1 PIN_ARI
172 517083 . 3585705 2233.9 4.88 PIN_ARI
172 517083 3585705 2233.9 4.96 PIN_ARI
173 516251 3585092 2014.8 3.00 PIN_PON
174 516431 3585241 2066.6 3.58 Mixed
174 516431 3585241 2066.6 3.13 PIN_PON
174 516431 3585241 2066.6 3.01 PIN_PON
174 516431 3585241 2066.6 4.26 Mixed
175 516888 3585400 2215.3 4.97 PIN_ARI
175 516888 3585400 2215.3 4.10 Mixed
175 516888 3585400 2215.3 4.99 PIN_ARI
175 516888 3585400 2215.3 4.99 PIN_ARI
177 517261 3586397 2301 .9 4.56 Mixed
177 517261 3586397 2301 .9 4.99 PIN_ARI
177 517261 3586397 2301.9 4.84 PIN_ARI
177 517261 3586397 2301.9 4.98 PIN_ARI
178 517653 3587136 2330.9 4.95 PIN_ARI
178 517653 3587136 2330.9 4.99 PIN_ARI
178 517653 3587136 2330.9 4.96 PIN_ARI
178 517653 3587136 2330.9 4.96 PIN_ARI
179 517969 3587762 2493.1 3.01 PIN_PON
179 517969 3587762 2493.1 4.83 PIN_ARI
179 517969 3587762 2493.1 4.87 PIN_ARI
179 517969 3587762 2493.1 5.00 PIN_ARI
180 524999 3586496 2449.3 3.01 PIN_PON
180 524999 3586496 2449.3 4.98 PIN_ARI
180 524999 3586496 2449.3 4.90 PIN_ARI
180 524999 3586496 2449.3 3.13 PIN_PON
181 524595 3587330 2458.1 4.42 Mixed
181 524595 3587330 2458.1 3.41 Mixed
181 524595 3587330 2458.1 4.96 PIN_ARI
181 524595 3587330 2458.1 4.78 PIN_ARI
182 523842 3587780 2354.8 4.50 Mixed
182 523842 3587780 2354.8 4.97 PIN_ARI
182 523842 3587780 2354.8 4.99 PIN_ARI
182 523842 3587780 2354.8 4.99 PIN_ARI
183 522592 3584399 2155.0 4.93 PIN_ARI
183 522592 3584399 2155.0 4.36 Mixed
183 522592 3584399 2155.0 3.99 Mixed
.183 32239.2 9.594399 215.5..9__411_ PIN_ABI-

239

Table A-1. Continued.

 

 

Mean
Elevation needle

WP UTM-X UTM-Y (m) number Morphotype
184 522830 3585258 2248.2 4.87 PIN_ARI
184 522830 3585258 2248.2 4.88 PIN_ARI
184 522830 3585258 2248.2 4.18 Mixed
184 522830 3585258 2248.2 4.86 PIN_ARI
185 523842 3585874 2413.9 4.97 PIN_ARI
185 523842 3585874 2413.9 4.98 PIN_ARI
185 523842 3585874 2413.9 4.64 PIN_ARI
185 523842 3585874 2413.9 5.01 PIN_ARI
186 529520 3582208 1814.2 4.94 PIN_ARI
186 529520 3582208 1814.2 4.83 PIN_ARI
186 529520 3582208 1814.2 4.81 PIN_ARI
186 529520 3582208 1814.2 4.99 PIN_ARI
187 528657 3581733 1782.6 4.50 Mixed
187 528657 3581733 1782.6 4.55 Mixed
187 528657 3581733 1782.6 3.98 Mixed
187 528657 3581733 1782.6 4.72 PIN_ARI
188 528111 3581568 1758.0 5.01 PIN_ARI

 

240

 

 

 

 

 

 

.. 00050.0 00050.0 00000.0 00000.0 00050.0 00000.0 00000.0 00000.0 0000.0 50000.0 00500.0 00550.0 000
00050.0 P 5 3000.0 50550.0 00000.0 50000.0 00500.0 00000.0 00500.0 00000.0 50000.0 00000.0 52
00050.0 5 5 3000.0 ..0550.0 00000.0 50000.0 00500.0 00000.0 00500.0 00000.0 50000.0 00000.0 80
00000.0 $000.0 $000.0 5 00000.0 00050.0 00500.0 05000.0 00000.0 00000.0 5000.0 .5030 00000.0 .000
00000.0 50550.0 50550.0 00000.0 5 00000.0 00000.0 00050.0 00000.0 00000.0 00050.0 00000.0 05000.0 0:0.
00050.0 00000.0 00000.0 00050.0 00000.0 5 05000.0 00000.0 00000.0 0000.0 0000.0 00000.0 00050.0 5.0..
00000.0 50000.0 50000.0 00500.0 00000.0 0.0000 F 50000.0 00000.0 0000.0 00000.0 00000.0 05050.0 000..
00000.0 00500.0 00500.0 05000.0 00050.0 00000.0 50000.0 5 00000.0 00500.0 00000.0 00000.0 00000.0 50.2
00000.0 00000.0 00000.0 00000.0 00000.0 00000.0 00000.0 00000.0 .. 00000.0 00000.0 300.0 5000.0 a<
0000.0 00500.0 00500.0 00000.0 00000.0 0000.0 0000.0 00500.0 00000.0 5 00000.0 00000.0 0.5000 00.2
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005000 ..00000 50000.0 55050.0 00000.0 00000.0 00000.0 00000.0 $00.0 00000.0 05000.0 5 00000.0 00..
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000 52 50 .000 004. 5.3.. 000.. 50.2 a... 00.2 00“. 00.. .0000... QE050§

5 05000.0 00000.0 00000.0 05000.0 50500.0 00050.0 00050.0 00050.0 00050.0 00050.0 55500.0 50050.0 000
05000.0 P 05000.0 05050.0 00000.0 50000.0 50050.0 00050.0 00000.0 $000.0 00000.0 50000.0 00050.0 52
00000.0 05000.0 5 50000.0 00500.0 00000.0 05000.0 00500.0 05000.0 0000.0 05050.0 00050.0 00000.0 80
00000.0 05050.0 50000.0 _. 50000.0 50000.0 0000.0 00005.0 00000.0 00500.0 50000.0 00050.0 00000.0 .000
05000.0 00000.0 00.00 50000.0 5 00050.0 00000.0 00550.0 55000.0 00500.0 50500.0 0005.0 0000.0 0:<
50500.0 50000.0 00000.0 50000.0 00050.0 5 0000.0 5055.0 05005.0 00005.0 000500 0.0050 50000.0 5.0..
00050.0 50050.0 050000 00000 00000.0 0000.0 5 00050.0 00500.0 00000.0 0000.0 50000.0 0000.0 00....
00050.0 00050.0 00500.0 0005.0 0030.0 5055.0 00050.0 5 05000.0 00000.0 00000.0 00050.0 00050.0 50.2
00050.0 00000.0 05000.0 00000.0 55000.0 05005.0 00500.0 05000.0 .. 50500.0 00000.0 0050.0 50000.0 5<
00050.0 3000.0 0000.0 00.000 00500.0 00005.0 00000.0 00000.0 50500.0 5 05500.0 0000.0 0000.0 .05.
00050.0 00000.0 0050.0 50000.0 50500.0 0000.0 0000.0 00000.0 00000.0 05500.0 5 50000.0 50050.0 00“.
55500.0 50000.0 00050.0 00050.0 0.0050 0.0050 50000.0 0000.0 0050.0 50000.0 50000.0 .. 00000.0 00...
50050.0 00050.0 00000.0 000000 0000.0 5000.0 0000.0 00050.0 50000.0 0000.0 50050.0 00000.0 5 .0005.

000 52 60 :00 W00. 5...... 000.. 50.2 .9. 00.2 00“. 00.. .00004. 0.0001
00.000

5030 0.0.0022 05.000 0.0000 05 .0. .003 0x00 .0E0...x0.2. 05.000900. E:E_x0E 000 .. Eouoc .0E0hc..20 05.000900.
05.0.0.0. .. no. .9005. 020000.00... 5.50oE 000 .00000 .0000 5.0.0.0 0000-550: 000E ..o. x500. 02.0.0000 .0-< 0.00...

241

 

 

5 50000.0 00000.0 05000.0 00000.0 00000.0 50000.0 500.0 0500.0 00000.0 00000.0 55500.0 00500.0 000
50000.0 5 00000.0 00000.0 50000.0 00500.0 00500.0 50000.0 00000.0 00500.0 50000.0 00500.0 50500.0 32
00000.0 00000.0 5 00000.0 00000.0 50500.0 05500.0 00000.0 00000.0 00000.0 00000.0 00500.0 00000.0 .00
05000.0 00000.0 00000.0 5 0000.0 50500.0 0000.0 00000.0 50000.0 05500.0 50000.0 00500.0 00000.0 .000
00000.0 50000.0 00000.0 0000.0 5 50000.0 00000.0 55000.0 50500.0 00000.0 00000.0 50000.0 00000.0 000.
00000.0 00500.0 50500.0 50500.0 50000.0 5 00000.0 00500.0 05000.0 00000.0 50000.0 05000.0 00000.0 5.0..
50000.0 00500.0 05500.0 0000.0 00000.0 00000.0 5 50000.0 00000.0 05000.0 05000.0 00500.0 50500.0 000..

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