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METEOROLOGICAL FACTORS AFFECTING THE SUCCESS OF
THE GYPSY MOTH FUNGAL PATHOGEN Entomophaga maimaiga
(ZYGOMYCETES: ENTOMOPHTHORALES) IN MICHIGAN

By

Nathan Wade Siegert

A DISSERTATION

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

DOCTOR OF PHILOSOPHY

Department of Entomology
and
Program in Ecology, Evolutionary Biology & Behavior

2004

ABSTRACT
METEOROLOGICAL FACTORS AFFECTING THE SUCCESS OF
THE GYPSY MOTH FUNGAL PATHOGEN Entomophaga maimaiga
(ZYGOMYCETES: ENTOMOPHTHORALES) IN MICHIGAN
By

Nathan Wade Siegert

The fungal pathogen Entomophaga maimaiga (Zygomycetes:
Entomophthorales) has been responsible for significant declines in gypsy moth
[(Lymantria dispar L.) (Lepidoptera: Lymantriidae)] population density in the
northeastern U.S. since 1989. In Michigan, however, the pattern of E. maimaiga
epizootics has been less consistent since its introduction in 1991. Although E.
maimaiga is established throughout Michigan, high-density gypsy moth
populations and severe defoliation have continued to occur. As the gypsy moth
fungus is highly sensitive to variations in temperature and moisture, more
information is needed concerning E. maimaiga infection rates in relation to
climate in the North Central region of the United States. Meteorological factors
affecting the success of E. maimaiga were examined using large-scale climate-
matching analyses, and laboratory and field bioassays between 1999 and 2002
that compared E. maimaiga infection rates under optimal versus naturally-
occurring conditions. Additionally, E. maimaiga and nuclear polyhedrosis virus
infections of gypsy moth larvae during primary transmission were evaluated in
an oak-dominated Michigan forest with low-density gypsy moth populations.

Infection rates during 4-d intervals were related to microclimatic variables

occurring over a 6-wk period of gypsy moth larval development.

A relatively small area in the southern Great Lakes region was
determined to be highly similar in long-term climatic patterns to the climatic
conditions in regions of the US. where large-scale E. maimaiga epizootics have
been documented. A high degree of climatic variability, however, occurs
annually in portions of the North Central region. The number of years between
1971 and 2000, in which weather may have been favorable for the development
of E. maimaiga epizootics in the North Central region, were estimated.

Bioassays using laboratory-reared 4th-instar gypsy moths were
conducted to evaluate E. maimaiga infection rates in oak-dominated forests in
Michigan. ln field bioassays, infection rates of E. maimaiga were significantly
lower under naturally-occurring conditions in Michigan than under laboratory
conditions that were optimal for fungal germination. Increased levels of E.
maimaiga infection in field bioassays were associated with June temperature
and precipitation levels which were significantly greater than 30-year average
conditions. Dynamics of the gypsy moth fungal pathogen E. maimaiga
throughout much of the North Central region appear to be primarily limited by
weather, specifically levels of June precipitation. The role of climatic variability
in the success of E. maimaiga in the North Central region are discussed.
Implications of this research for developing improved methods and
recommendations to incorporate the biological control agent E. maimaiga into
an integrated pest management system for the effective control of gypsy moth in

forest ecosystems in the North Central region are presented.

ACKNOWLEDGEMENTS

I would like to express my sincere gratitude to the numerous folks that
have had some influence in the development, progression or completion of this
research over the past few years. Some have been more influential than others,
as is usually the case in graduate programs, but each has affected this research
in some integral manner, much to my appreciation.

I thank my friend and advisor, Deborah G. McCullough, for providing me
with the opportunity to develop and explore my interest in forest entomological
research. During my apprenticeship, Deb’s enthusiasm and diligence as a
mentor and a researcher has inspired and motivated me to strive for excellence,
originally and balance in my research. I would also like to thank Jeff Andresen,
Therese Poland, David Rothstein and Suzanne Thiem for their counsel and
valuable advisement during this project. I gratefully acknowledge their support
and assistance, which has greatly fostered my professional development.

Numerous folks were involved in collaborations which made the
completion of this research possible. I thank Ann Hajek and Mike Wheeler, of
Cornell University, for conducting the laboratory bioassays and their counsel
during this research. I also thank Rob Venette, of the University of Minnesota,
for his support and insightful advisement concerning the climate-matching
analyses. Deb Grooms, Bill Kauffman and associates at the USDA APHIS PPQ
Biological Control Laboratory have absolutely been of inestimable value during

this project. Their assistance with rearing, transport and dissection of larvae is

gratefully acknowledged. The magnitude of this research would not have been
possible without their consistently good-natured help.

Additionally, a number of other integral folks helped to make the
completion of this research possible. I gratefully acknowledge Matt Davenport,
Melanie DePoy, Rachael Harris, Anne Henderson and Abigail Sommers, all of
Michigan State University, for their assistance with numerous tasks, including
the transport of thousands of gypsy moth larvae to timely rendezvous' with me
throughout the state during the field bioassays each year. I thank Tom Ellis, of
Michigan State University, for similar assistance and additional thoughtfulness.
After a particularly hot, humid and hectic couple of weeks or so during the 1999
field bioassays, during which I had donated copious amounts of blood to
countless biting flies throughout the state and was running particularly ragged,
Tom was thoughtful enough to include a six-pack of Miller on ice along with my
larvae. I swear that the High Life never tasted so damn good! Tom is, indeed, a
really good guy.

I also thank Lyle Buss, of the University of Florida, and Bob Heyd, Roger
Mech and Frank Sapio, all of the Michigan Department of Natural Resources,
who provided much assistance locating field sites throughout Michigan
previously inoculated with the gypsy moth fungus, Entomophaga maimaiga. For
reawakening the mathematical chunk of my brain, I thank Dan Hayes, of
Michigan State University. I also extend my sincere gratitude to Aaron Pollyea,
of Michigan State University, for supplying much of the North Central Climate

Data, and George Bird, Haddish Melakeberhan and Jim Miller, all of Michigan

State University, for their provisions of additional laboratory space and
equipment over the course of this research.

My interactions with, and the support of my fellow graduate student
colleagues throughout my graduate experience are greatly appreciated. I also
acknowledge my good friends, David Benac, John Hood, Jeff Remick, and my
brother Todd Siegert, who have supported and assisted me at various times,
mostly just for the hell of it. They have happily provided hours of free labor, all
the while expecting nothing in return. Their camaraderie, along with that of a
few others, I hold dear.

Last, but certainly not least, I would be sorely remiss if I did not express
my sincere gratitude and appreciation for my wife Piera, whose love,
encouragement and support has been the one constant throughout my graduate
programs at Michigan State University. Piera has been willing to graciously
provide assistance, or otherwise support me in one form or another, during the
course of this research and has given up innumerable weekends, holidays and
other events, mostly just to spend time with me while I’ve been in the woods. I
thank her for her love and understanding, which has, and continues to provide
balance and enrichment in my life outside of academia.

This research was funded by the Special Technology Development
Program, State and Private Forestry, USDA Forest Service, and the Michigan
Department of Agriculture. A Dissertation Completion Fellowship from the
College of Agriculture and Natural Resources at Michigan State University

provided financial support during the final semester of my graduate program.

vi

Additional funding was provided through the Graduate School at Michigan State
University and the Ray and Bernice Hutson Endowment from the Department of

Entomology for partial support of travel expenses to several regional, national

and international entomological meetings.

vii

TABLE OF CONTENTS

LIST OF TABLES ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, xii
LIST OF FIGURES .................................................................................................. xiv
LIST OF SYMBOLS, ABBREVIATIONS AND NOMENCLATURE ,,,,,,,,,,,,,,,,,, xix
INTRODUCTION _____________________________________________________________________________________________________ 1
Gypsy moth in North America .................................................................... 2
Entomophaga maimaiga in North America ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 5
Scope of the present study ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 7
Dissertation organization ............................................................................ 9
CHAPTER 1
Field and laboratory evaluation of gypsy moth infection
by Entomophaga maimaiga: Potential influence of large-
scale meteorological events ___________________________________________________________________________________ 13
Introduction ................................................................................................... 13
Materials and Methods ............................................................................... 16
Study sites & field measurements _________________________________________________ 16
Field bioassays ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 18
Laboratory bioassays ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 20
Infection of forest-collected gypsy moth larvae ,,,,,,,,,,,,,,,,,,,,,,,,,,, 21
Precipitation & temperature departures from
30'W averages ..................................................................... 22
Statistical analysis ........................................................................... 22
Results ........................................................................................................... 24
Resting spore density in soil ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 24
Field bioassays ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 24
Laboratory bioassays ...................................................................... 27
Infection of forest-collected gypsy moth larvae ,,,,,,,,,,,,,,,,,,,,,,,,,,, 28
Precipitation & temperature departures from
30-yr averages ..................................................................... 28
Discussion ..................................................................................................... 31
Literature Cited ............................................................................................. 37
CHAPTER 2
Fungal and viral infections of gypsy moth (Lepidoptera:
Lymantriidae) larvae and effects of microclimatic conditions
in the initial development of epizootics ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 54
llltl'OdUCthTl ................................................................................................... 54
Materials and Methods _______________________________________________________________________________ 58
Study sites & field measurements ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 58

viii

Field bioassays ................................................................................ 59

Microclimatic data ........................................................................... 62
Results ........................................................................................................... 65
Field bioassays ................................................................................ 65
Infection dynamics ........................................................................... 65
Microclimatic conditions ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 67
Discussion ..................................................................................................... 69
Literature Cited ............................................................................................. 75
CHAPTER 3

Assessing the climatic potential for epizootics of the gypsy
moth fungal pathogen Entomophaga maimaiga in the North

Central United States .............................................................................................. 93
Introduction ................................................................................................... 93
Materials and Methods _______________________________________________________________________________ 97

Documented epizootics ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 97
Climate comparisons ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 97
Climatic deviations from 30-yr averages ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 100
Favorable years for epizootics in the North
Central region, 1971-2000 _________________________________________________ 101
RSSUItS ........................................................................................................... 103
Documented epizootics ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 103
Climate comparisons ....................................................................... 103
Climatic deviations from 30-yr averages ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 104
Favorable years for epizootics in the North
Central region, 1971-2000 ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 105
Discussion ..................................................................................................... 108
Literature Cited _____________________________________________________________________________________________ 114

MANAGEMENT IMPLICATIONS ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 132

APPENDICES .......................................................................................................... 136

APPENDIX A: VOUCHER SPECIMENS ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 137
Record Deposition of Voucher Specimens ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 138
Voucher Specimen Data ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 139

APPENDIX B: MICHIGAN CLIMATE 158

Figure B1. Michigan average daily temperature (°C)

January through December, 1961-1990 (adapted

from MRCC 2000). Temperature gradient varies by

month, with areas covered by darker shades of gray

indicating higher average daily temperatures than

areas covered by lighter shades of gray. White dots

indicate locations of Entomophaga maimaiga field

bioassays in Michigan, 1999-2002. .............................................. 159

Figure 82. Michigan average daily maximum temperature
(°C) January through December, 1961-1990 (adapted
from MRCC 2000). Temperature gradient varies by
month, with areas covered by darker shades of gray
indicating higher average daily maximum temperatures
than areas covered by lighter shades of gray. White
dots indicate locations of Entomophaga maimaiga field
bioassays in Michigan, 1999-2002. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 160

Figure B3. Michigan average daily minimum temperature

(°C) January through December, 1961-1990 (adapted

from MRCC 2000). Temperature gradient varies by

month, with areas covered by darker shades of gray

indicating higher average daily minimum temperatures

than areas covered by lighter shades of gray. White

dots indicate locations of Entomophaga maimaiga field

bioassays in Michigan, 1999-2002. ______________________________________________ 161

Figure B4. Michigan average monthly precipitation (mm)
January through December, 1961-1990 (adapted
from MRCC 2000). Precipitation gradient varies by
month, with areas covered by darker shades of gray
indicating higher average monthly precipitation than
areas covered by lighter shades of gray. White dots
indicate locations of Entomophaga maimaiga field
bioassays in Michigan, 1999-2002. ______________________________________________ 162

APPENDIX C: CLIMEX LOCATION DATA ......................................................... 163
Table C1. North American locations used in the CLIMEX
climate-matching analyses (n = 1132 locations).
Additional locations added to the CLIMEX meteorological
database to better represent climatic variability in the
North Central region (n = 832 locations) are listed in

upper-case characters. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 164

APPENDIX D: FIELD SITE VEGETATION DATA ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 189
Table D1. Summary of understory vegetation present along

a single, randomly-selected 100 m x 1 m transect in 32

of the 33 Michigan field sites used for the Entomophaga

maimaiga bioassays. Included in the summary is a list

of species present, the percentage of field sites that

species were present along transects, the overall mean

(iSEM) number of specimens (per m2) across field sites,

and the range in the number of specimens (per m2)

across field sites. 190

ooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo

Table D2. Summary of ground flora (<1 m in height) present
in four 1 m2 quadrants along a single, randomly-selected
100 m x 1 m transect in 32 of the 33 Michigan field sites
used for the Entomophaga maimaiga bioassays. Included
in the summary is a list of species present, the percentage
of field sites that species were present in quadrants, the
overall mean (:tSEM) number of specimens (per m2)
across field sites, and the range in the number of
specimens (per m2) across field sites. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 192

LITERATURE CITED 194

xi

LIST OF TABLES

CHAPTER 1.

Table 1.1. Location and characteristics of oak stands in Michigan

used for Entomophaga maimaiga field sites from 1999 to

2001. ..............................................................................................................
Table 1.2. Number of gypsy moth cadavers examined, mean

(:I: SEM) percentage of gypsy moth larvae infected by

Entomophaga maimaiga in field and laboratory bioassays

in 1999, 2000 and 2001, and mean (:I: SEM) number of

E. maimaiga resting spores (No./g dry soil) per aspect in

1999 and 2000. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
Table 1.3. Percentage of gypsy moth cadavers from wild

populations collected at field sites that were infected with

Entomophaga maimaiga and NPV present at field sites.

Up to 40 gypsy moth cadavers were collected from under

burlap bands on four sample trees at each site. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

Table 1.4. Mean (i SEM) 30-yr averages of precipitation (mm)
and temperature (°C) for April, May and June and
departures from the 30-yr averages during 1999, 2000
and 2001 in northern lower Michigan where 30 of the 33
field sites were located. Climatic parameters that were
significantly different from the 30-yr average (P < 0.05)
are designated with a double asterisk (**) following the
value.

CHAPTER 2.

Table 2.1. Location and characteristics of oak stands in

Newaygo Co., Michigan, used for 6-wk bioassay field

sites from 2001 to 2002. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,
Table 2.2. Summary of regression equations for predicting

Entomophaga maimaiga and NPV infections during

primary transmission in the initial development of

epizootics. Predictor variables (excluding intercept)

are listed in order of contribution to r2.

xii

43

45

46

47

82

83

CHAPTER 3.

Table 3.1. Summary of Entomophaga maimaiga epizootic locations
used in the CLIMEX climate-matching analyses. Following
each location are the observed conditions, 30-yr averages
and departures from 30-yr averages of precipitation (mm)
and air temperature (°C) from the nearest available weather
station for April, May and June. For all sites combined,
climatic parameters that were significantly different from
30-yr averages (P < 0.05) are designated with a double
asterisk (**) following the value. ________________________________________________________________ 121

xiii

LIST OF FIGURES

INTRODUCTION.

Figure 1. Polyhedral inclusion bodies of the nuclear polyhedrosis
virus (NPV) (A), and resting spores (i.e. azygospores) (B)
and a conidium (C) of the gypsy moth fungal pathogen
Entomophaga maimaiga. Viral particles range in size from
1 - 10 pm. Pear-shaped conidia of E. maimaiga are
approximately 20 x 25 pm in size and resting spores are
approximately 30 pm in diameter. Images in this figure are

presented in color. _______________________________________________________________________________________

CHAPTER 1.

Figure 1.1. Numbers (1 — 32) mark the locations of Entomophaga
maimaiga field sites in Michigan, 1999 to 2001. Site no. 33
was used as a field site in 2000 and 2001 after an E.

maimaiga epizootic occurred there in 1999. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

Figure 1.2. Field infection (°/o) of gypsy moth larvae by Entomophaga
maimaiga in relation to canopy cover (°/o) in (A) 1999, (B)
2000, and (C) 2001. There was not a significant trend in

E. maimaiga field infection by canopy cover in 2000 or 2001. ,,,,,,,,,,,,,

Figure 1.3. Field infection (°/o) of gypsy moth larvae by Entomophaga
maimaiga in relation to the number of resting spores per gram
of dry soil in (A) 1999 and (8)2000. There was not a significant
trend in E. maimaiga field infection by the number of resting

spores ill the SO” in 1999- ...........................................................................

Figure 1.4. Laboratory infection (°/o) of gypsy moth larvae by
Entomophaga maimaiga in relation to the number of resting

spores per gram of dry soil in (A) 1999 and (B) 2000. ,,,,,,,,,,,,,,,,,,,,,,,,,,,

Figure 1.5. Laboratory infection (°/o) of gypsy moth larvae by
Entomophaga maimaiga in relation to soil pH in (A) 1999,
(B) 2000, and (C) 2001. There was not a significant trend

in E. maimaiga laboratory infection by soil pH in 2001. .........................

xiv

12

48

49

50

51

52

Figure 1.6. Laboratory infection (%) of gypsy moth larvae by
Entomophaga maimaiga in relation to E. maimaiga field
infections (%) in (A) 1999, (B) 2000, and (C) 2001. There
was not a significant trend in E. maimaiga laboratory
infection by E. maimaiga field infection in 1999 or 2000. ______________________

CHAPTER 2.

Figure 2.1. Cumulative percentage fungal and viral infections
of 4th-instar gypsy moth larvae during 4-d bioassays
conducted over a 6-wk period in 2001 at A) Bitely, B)
Jackson Corners, and C) Lilley field sites. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

Figure 2.2. Cumulative percentage fungal and viral infections
of 4th-instar gypsy moth larvae during 4-d bioassays
conducted over a 6-wk period in 2002 at A) Bitely, B)
Jackson Corners, and C) Lilley field sites. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

Figure 2.3. Microclimatic conditions that occurred at the Bitely
site during the 6-wk field bioassays in 2001. Microclimatic
variables included A) hourly air and soil temperatures (°C),
B) soil moisture (%), and C) actual water vapor pressure
(kPa). Soil temperatures and moistures shown were collected
from southern aspects. Northern aspects were slightly lower
in temperature (northern aspect soil temperature = 0.964 x
(southern aspect soil temperature) + 0.299; r2 = 0.99) and
higher in moisture (northern aspect soil moisture = 1.069 x
(southern aspect soil moisture) + 0.001; r2 = 0.99). _______________________________

Figure 2.4. Microclimatic conditions that occurred at the Jackson
Corners site during the 6-wk field bioassays in 2001.
Microclimatic variables included A) hourly air and soil
temperatures (°C), B) soil moisture (%) and precipitation
(mm), and C) actual water vapor pressure (kPa). Soil
temperatures and moistures shown were collected from
southern aspects. Northern aspects were slightly lower in
temperature (northern aspect soil temperature = 0.894 x
(southern aspect soil temperature) + 1.264; r2 = 0.96) and
higher in moisture (northern aspect soil moisture = 1.116 x
(southern aspect soil moisture) - 0.005; r2 = 0.97). ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,

XV

53

85

86

87

88

Figure 2.5. Microclimatic conditions that occurred at the Lilley
site during the 6-wk field bioassays in 2001. Microclimatic
variables included A) hourly air and soil temperatures (°C),
B) soil moisture (%), and C) actual water vapor pressure
(kPa). Soil temperatures and moistures shown were collected
from southern aspects. Northern aspects were slightly lower
in temperature (northern aspect soil temperature = 0.858 x
(southern aspect soil temperature) + 1.553; r2 = 0.98) and
higher in moisture (northern aspect soil moisture = 1.016 x
(southern aspect soil moisture) - 0.004; r2 = 0.95). ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 89

Figure 2.6. Microclimatic conditions that occurred at the Bitely
site during the 6-wk field bioassays in 2002. Microclimatic
variables included A) hourly air and soil temperatures (°C),
B) soil moisture (%), and C) actual water vapor pressure
(kPa). Soil temperatures and moistures shown were
collected from southern aspects. Northern aspects were
slightly lower in temperature (northern aspect soil temperature
= 0.832 x (southern aspect soil temperature) + 2.583; r2 =
0.85) and higher in moisture (northern aspect soil moisture
= 1.134 x (southern aspect soil moisture) + 0.009; r2 = 0.90). ,,,,,,,,,,,, 90

Figure 2.7. Microclimatic conditions that occurred at the Jackson
Corners site during the 6-wk field bioassays in 2002.
Microclimatic variables included A) hourly air and soil
temperatures (°C), B) soil moisture (%) and precipitation
(mm), and C) actual water vapor pressure (kPa). Soil
temperatures and moistures shown were collected from
southern aspects. Northern aspects were slightly lower
in temperature (northern aspect soil temperature = 0.705
x (southern aspect soil temperature) + 3.871; r2 = 0.85)
and higher in moisture (northern aspect soil moisture =
1.558 x (southern aspect soil moisture) - 0.044; r2 = 0.84). ,,,,,,,,,,,,,,,,, 91

Figure 2.8. Microclimatic conditions that occurred at the Lilley
site during the 6-wk field bioassays in 2002. Microclimatic
variables included A) hourly air and soil temperatures (°C),
B) soil moisture (%), and C) actual water vapor pressure
(kPa). Soil temperatures and moistures shown were
collected from southern aspects. Northern aspects were
slightly lower in temperature (northern aspect soil
temperature = 0.787 x (southern aspect soil temperature)
+ 2.521; r2 = 0.91) and higher in moisture (northern aspect
soil moisture = 1.175 x (southern aspect soil moisture) -
0.005; I2 = 0.90). _________________________________________________________________________________________ 92

xvi

CHAPTER 3.

Figure 3.1. Symbols (0) mark the North American locations used in
the CLIMEX climate-matching analyses (n = 1132 locations)
in (A) North America. Additional locations were added to the
CLIMEX database to better represent climatic variability in
(B) the North Central region in the United States (n = 832
locations). ...................................................................................................... 124

Figure 3.2. Climate divisions used to evaluate variation in
precipitation and temperature, as per absolute deviation
summaries, 1971-2000, in the North Central states of
Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota,
Missouri, Ohio and Wisconsin (adapted from MRCC 2002). ,,,,,,,,,,,,,,,, 125

Figure 3.3. Maximum similarity to any one of 11 locations where
a documented Entomophaga maimaiga epizootic occurred,
based on overall climatic similarity (i.e. Match Index), using
(A) spring and (8) annual climate data. Images in this figure
are presented in color. ________________________________________________________________________________ 126

Figure 3.4. North Central region absolute departures from 30—yr
averages, 1971-2000, of (A) spring and (B) annual
precipitation (cm) (adapted from MRCC 2002). Areas
covered by darker shades of blue indicate greater absolute
departures from 30-yr averages (i.e. 30-yr sum of absolute
departures) for precipitation than areas covered by lighter
shades of blue for the North Central region. Images in this
figure are presented in color. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 127

Figure 3.5. North Central region absolute departures from 30-yr
averages, 1971-2000, of (A) spring and (B) annual
temperature (°C) (adapted from MRCC 2002). Areas
covered by darker shades of red indicate greater absolute
departures from 30-yr averages (i.e. 30-yr sum of absolute
departures) for temperature than areas covered by lighter
shades of red for the North Central region. Images in this
figure are presented in color. ..................................................................... 128

xvii

Figure 3.6. Number of years from 1971 to 2000 in the North
Central region with precipitation similar to several
documented Entomophaga maimaiga epizootics in April,
May and June. A year was considered favorable for an
epizootic if the precipitation during a given time period
met or exceeded (A - C) the average precipitation or (D
- F) the average precipitation minus the standard deviation
(i.e. best case scenario) that occurred in areas with
documented epizootics. Images in this figure are
presented in color. ....................................................................................... 129

Figure 3.7. Number of years from 1971 to 2000 in the North
Central region with temperature similar to several
documented Entomophaga maimaiga epizootics in April,
May and June. A year was considered favorable for an
epizootic if the temperature during a given time period
met or exceeded (A - C) the average temperature or (D
- F) the average temperature minus the standard deviation
(i.e. best case scenario) that occurred in areas with
documented epizootics. Images in this figure are
presented in color. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 130

Figure 3.8. Number of years from 1971 to 2000 in the North
Central region with precipitation and temperature similar
to several documented Entomophaga maimaiga epizootics
in April, May and June. A year was considered favorable
for an epizootic if both the precipitation and temperature
during a given time period met or exceeded (A - C) the
average conditions or (D - F) the average conditions minus
the respective standard deviations (i.e. best case scenario)
that occurred in areas with documented epizootics. Images
in this figure are presented in color. ,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,,, 131

xviii

LIST OF SYMBOLS, ABBREVIATIONS AND NOMENCLATURE

°C

pm

APHIS

ARSEF

BA
ca
Cl
Co.

CSIRO

ha

degrees Celsius

micrometer

total precipitation constant (CLIMEX)
Animal and Plant Health Inspection Service

Agricultural Research Service Collection of
Entomopathogenic Fungal Cultures

basal area

circa

confidence interval
county

Commonwealth Scientific & Industrial Research
Organization (Australia)

day(s)

diameter at breast height

actual atmospheric water vapor pressure
saturation water vapor pressure
Environmental Systems Research Institute
inverse of the natural logarithm of a number
fecal bovine serum

gram(s)

hour(s)

hectare(s)

xix

Irpaf
’rtof
It
,tmax

Itmin

IPM

kPa

kR

kr

LAI

MI

MI DNR
MRCC
NAPIS
NCAA
NPV

ppo

RM

precipitation pattern index (CLIMEX)

total precipitation index (CLIMEX)

average monthly temperature (CLIMEX)

maximum temperature index (CLIMEX)

minimum temperature index (CLIMEX)

integrated pest management

kilo-Pascal

precipitation pattern constant (CLIMEX)

total precipitation constant (CLIMEX)

temperature constant (CLIMEX)

leaf area index

Match Index (CLIMEX)

Michigan Department of Natural Resources

Midwestern Regional Climate Center

National Agricultural Pest Information Service

National Oceanic and Atmospheric Administration

nucleopolyhedrosis (nuclear polyhedrosis) virus

Plant Protection and Quarantine

correlation coefficient

coefficient of determination

average absolute difference between monthly
precipitation of target and matching

locations (CLIMEX)

annual precipitation at matching location (CLIMEX)

XX

RH
SEM

SI

Tdmax

Tdmin

USDA

wk

yr

annual precipitation at target location (CLIMEX)
relative humidity

standard error of the mean

sheindex

air temperature

average absolute difference in maximum
temperature between two locations (CLIMEX)

average absolute difference in minimum
temperature between two locations (CLIMEX)

United States Department of Agriculture
week(s)

year(s)

xxi

INTRODUCTION

The distribution of the world’s biota has historically been restricted by
geographic and ecological barriers, such as oceans and mountain ranges.
However, there has been a dramatic increase in the number of introductions of
new species, as a result of increased international commerce, travel and
I ecosystem disturbance (Liebhold et al. 1995, Niemela and Mattson 1996).
Successful establishment of non-indigenous species in new geographic ranges
may be facilitated by arriving without their native biotic constraints on growth,
survival and reproduction (NRC 2002). Additionally, without their native
complexes of predators, parasites or pathogens, many of these foreign species
become important pests, causing substantial disturbance to forest ecosystems
and often significant socioeconomic impacts (Liebhold et al. 1995).

Biological control of forest insects is an important technology for
successfully managing economic pests. The principle strategy behind biological
control is to use populations of other organisms (e.g. natural enemies) to limit
the density and growth of an insect pest population (Van Driesche and Bellows
1996). Barbosa and Wagner (1989) identified 22 programs that utilized
biological control in forest pest management globally, 18 of which were
described as successful. Several non-indigenous forest pests, such as
European pine shoot moth (Rhyacionia buoliana [Denis and Schiffermijller])
(Lepidoptera: Tortricidae), European spruce sawfly (Gilipinia hercyniae [Hartig])

(Hymenoptera: Diprionidae), larch casebearer (Coleophora Iaricella [H0bner])

(Lepidoptera: Coleophoridae) and winter moth (Operophtera brumata L.)
(Lepidoptera: Geometridae) (Craighead 1950, Embrée 1966, Embrée and Otvos
1984, Dahlsten 1986, Long 1988), have been successfully managed using
classical biological control strategies, which involve the importation and
establishment of control agents from a pest’s native natural enemy complex.
Van Driesche et al. (1996) reviewed 28 exotic insect pest species in the United
States and judged that 26 of them provided opportunities for their control via
natural enemy introductions. The ecological and environmental threats posed
by biological invasions necessitates continued efforts to examine classical
biological control agents and evaluate their potential role in the effective control

and management of exotic forest pests in North America.

Gypsy moth in North America

European gypsy moth, Lymantria dispar L. (Lepidoptera: Lymantriidae) is
an exotic Iepidopteran forest herbivore that was accidentally introduced to North
America in the late 1860’s by an entrepreneurial amateur entomologist in
Massachusetts who was attempting to cross gypsy moth with native North
American silkworms (Forbush and Fernald 1896, Liebhold et al. 1989). Despite
great efforts to reduce the spread of this notorious forest defoliator, gypsy moth
has continued to expand its geographic range. As of 2004, gypsy moth is
currently known to be established in the southern portions of the Canadian
provinces of Ontario and Quebec (CFIA 2004), throughout the northeastern

states, and some southeastern states, Michigan and portions of the adjacent

North Central states of Illinois, Indiana, Ohio and Wisconsin (NAPIS 2004).
The life cycle of the gypsy moth was previously described by Forbush
and Fernald (1896) and Leonard (1981 ). Gypsy moth spends the majority of its
univoltine life cycle (i.e. one generation per year) as an egg, clustered in egg
masses of 100 - 600 or more that are deposited on the undersides of tree
branches, on tree trunks, buildings, fences or other suitable locations. In mid-
spring, typically late April to early May, larvae hatch from eggs and begin to feed
on tree foliage. Larvae are highly seteous (i.e. hairy) and first-instars typically
disperse by spinning a strand of silk and “ballooning" via wind currents to new
locations. Young larvae are dark-colored, while later instars develop prominent
blue spots on their dorsal anterior body segments and red spots on their dorsal
posterior body segments. Late-instar gypsy moths have voracious appetites
and feed on hundreds of species of trees and shrubs, though oaks (Quercus
spp.) are highly preferred. Late-instar larvae move down from the tree canopy
at dawn and remain amongst the leaf litter or in cryptic locations on the holes of
trees throughout the day (Forbush and Fernald 1896, Leonard 1981). At dusk,
larvae ascend into the canopy again to feed. Feeding is completed in
approximately seven weeks, at which point larvae find a sheltered location and
pupate in a brownish-black pupal case. Adults eclose from pupal cases in
approximately mid-July, with males typically eclosing several days earlier than
females. Adult wingspans are about 50 mm and adult males are dark brown in
color, while females are white with dark bands across their forewings. Female

European gypsy moths do not fly. However, females of the closely-related

Asian gypsy moth, which is not known to currently be established in North
America, do have the ability to fly. Female gypsy moths emit a sex pheromone
that volatizes in the air and is highly attractive to male gypsy moths. Males
follow the pheromone plume to females and mating occurs. Females cover egg
masses with buff-colored setae from their bodies, which gives them the
appearance of a sponge fungus—hence, its German name, the sponge fungus
moth (Stanek 1969). Adults do not feed and soon die after mating and
depositing eggs.

A large number of natural enemies of the gypsy moth, including
parasitoids, predators and pathogens, have been studied and evaluated since
its introduction to North America in an effort to successfully suppress this
invasive pest. Reardon (1981), Griffiths and Quednau (1984), Van Driesche et
al. (1996) and Nealis et al. (2002) provide thorough summaries of the history of
the century-long effort to acquire a natural enemy complex for gypsy moth.
Fuxa et al. (1998) provide a thorough summary of gypsy moth pathogens.

While several pathogens have been evaluated, a nuclear polyhedrosis
virus (NPV) (Figure 1A) was detected in North America in the early 1900’s
(Glaser 1915) and has frequently been found to cause epizootics in high-density
gypsy moth populations (Doane 1970, Leonard 1981, Woods and Elkinton
1987). Gypsy moth typically become infected with NPV by ingesting the virus
(Murray and Elkinton 1989), though other modes of infection, such as
transovum transmission (Doane 1969), are possible. Infected early-instar gypsy

moth move to the ends of branches or the tops of trees (usually some elevated

position) where they die. As these dead larvae deteriorate, they contaminate
the foliage and provide viral inoculum for infection of late-instar larvae (Woods
and Elkinton 1987). Other biotic (e.g. other caterpillars and insects, parasitoids,
birds, mammals) and abiotic factors (e.g. wind, rain) serve to further facilitate
the spread and dispersal of the virus (Podgwaite et al. 1981 ). Until 1989, NPV

remained the dominant gypsy moth pathogen in North America.

Entomophaga maimaiga in North America

Since 1989, the fungus Entomophaga maimaiga (Zygomycetes:
Entomophthorales) (Figure 18, C) has become an important pathogen of gypsy
moth in the northeastern U.S. (Andreadis and Weseloh 1990a, 1990b, Hajek et
al. 1995b, Reardon and Hajek 1998, Hajek 1999). Entomophaga maimaiga is a
desirable biological control agent because it is highly synchronized with gypsy
moth larval development, has relatively few negative effects on non-target
species (Soper et al. 1988, Vandenberg 1990, Hajek et al. 1995a, 19963,
1996b, 2000), and is compatible with other natural enemies, including NPV
(Andreadis and Weseloh 1990a, Hajek and Roberts 1992, Weseloh and
Andreadis 1992b).

The source of E. maimaiga and the reason for it’s appearance in North
America in 1989 remain unknown (Hajek et al. 1995b, Hajek 1999). By the
early 1900’s, researchers in North America had learned of a fungal pathogen
affecting gypsy moth in Japan and attempted to release it in the Boston area in

1910 and 1911 (Hajek 1999). There was no evidence, however, of successful

transmission of the fungus and local gypsy moth populations were substantially
reduced by a viral epizootic in 1911, so the program was discontinued (Hajek
1999). Numerous surveys of gypsy moth populations for pathogens were
conducted following the early release attempts (Campbell and Podgwaite 1971,
Podgwaite 1981), however, the presence of entomophthoralean spores were
not detected in larvae. Efforts to introduce E. maimaiga to North American
gypsy moth populations were renewed in the mid-1980’s, following damaging
gypsy moth outbreaks (Hajek et al. 1995b). As had happened with the earlier
release attempts, there was little to no evidence of successful transmission of E.
maimaiga to the native gypsy moth populations at the experimental sites in New
York and Virginia (Hajek et al. 1995b). Entomophaga maimaiga was absent in
follow-up surveys at these sites in 1987 and 1989 to 1991, so the releases were
considered to have failed (Hajek et al. 1995b). Unexpectedly, E. maimaiga was
discovered causing epizootics in southwestern Connecticut in June 1989 and
subsequent surveys during 1989 found that E. maimaiga was present in seven
northeastern states (Andreadis and Weseloh 1990a, 1990b, Hajek et al. 1995b).
Hajek et al. (1995b) and Weseloh (1998) discuss several hypotheses regarding
the recent origin of E. maimaiga in North America.

Entomophaga maimaiga produces two types of spores, both of which
may infect gypsy moth larvae (Reardon and Hajek 1998). Resting spores (i.e.
azygospores) of E. maimaiga overwinter in the soil (Figure 18), with the highest
levels occurring in the organic layer of soil at the base of trees (Hajek et al.

1998a). Late-instar larval behavior of climbing down from the canopy to rest in

the leaf litter during the day increases the risk of fungal infection (Hajek 2001).
A portion of these resting spores germinate in the spring when environmental
conditions are suitable (i.e. primary transmission) (Hajek 1997b, Hajek and
Humber 1997, Weseloh and Andreadis 1997). Gypsy moth larvae become
infected when E. maimaiga spores adhere to the cuticle, and then gain entry to
the host using a combination of mechanical pressure and enzymatic
degradation (Hajek 1999). Early-instar larvae become infected and die. These
infected cadavers externally produce E. maimaiga conidiophores that discharge
conidia (Figure 1C) which may infect mid- to late-instar gypsy moth (i.e.
secondary transmission). Late-instar larval cadavers principally produce resting
spores and are usually found attached to lower tree trunks by their prolegs with
their heads oriented downwards (Hajek and Soper 1991, Hajek et al. 1998b).
Cadavers drop to the soil, decompose and resting spores remain dormant in the

soil until the following spring.

Scope of the present study

Microclimatic conditions directly affect the transmissibility, germination
and infection of many entomopathogenic fungal pathogens (Andreadis 1987).
Temperature and forms of environmental moisture, such as humidity, dew, and
free water tend to be particularly important factors (McCoy et al. 1988, Tanada
and Kaya 1993, Burges 1998). Due to this dependency on climate, the success
of fungal pathogens is invariably associated with climatic variability when viable

host and pathogen populations are present.

Although E. maimaiga epizootics have effectively regulated gypsy moth
populations in areas of the northeastern United States since its discovery in
1989 (Hajek et al. 1995b, 1996b, Hunter and Elkinton 1999), this fungal
pathogen has been less consistent in other states, such as Michigan (Smitley et
al. 1995). Entomophaga maimaiga was first introduced into Michigan in 1991
(Smitley et al. 1995), and additional introductions have been made subsequently
(Buss 1997, L.J. Buss and DC. McCullough unpubl. data, Michigan Department
of Natural Resources [MDNR] and Michigan Department of Agriculture [MDA]
unpubl. data). Despite these widespread introductions and establishment of E.
maimaiga in Michigan, the development of epizootics has been inconsistent in
suppressing gypsy moth populations. Entomophaga maimaiga appeared to
contribute to a population collapse in 1993 and localized epizootics were
frequently observed to effectively control gypsy moth populations in 1996 (Bauer
and Smitley 1996). Both of these years had springs with above average
precipitation (MRCC 2002). Although variable, substantial gypsy moth
defoliation has continued to occur since 1996, with 242,361.2 ha of Michigan
forests sustaining moderate to heavy defoliation between 1997 and 2003
(USDA-FS 2004).

Gypsy moth populations continue to expand into the North Central
region, recently becoming established in Illinois, Indiana, Ohio and Wisconsin
(NAPIS 2004). Much of the North Central region that contains extensive areas
of highly susceptible forests (Liebhold et al. 1997a, 1997b). The costs of

suppressing gypsy moth are high, ranging from $1 - 3.6 million per year in

Michigan alone between 1990 and 1998 (USDA-FS 2004), and resource
managers in the North Central region are very interested in incorporating E.
maimaiga into their gypsy moth management strategies. However, if the
development of epizootics are regulated by specific climatic conditions, then E.
maimaiga may not consistently suppress gypsy moth populations.

Many questions remain regarding the effectiveness of E. maimaiga as a
successful biological control agent in the management of gypsy moth in the
North Central region of the United States. More knowledge is still needed,
including an understanding of climatic variability in the North Central region, its
potential impact on the development of E. maimaiga epizootics and the role of
weather in the infection dynamics of E. maimaiga in North American forests.
The present study addresses associations between meteorological factors and
the success of the gypsy moth fungal pathogen E. maimaiga in Michigan and

the North Central region.

Dissertation organization

Results from extensive field bioassays and corresponding laboratory
bioassays that were conducted from 1999 to 2001 to evaluate E. maimaiga
infection of gypsy moth larvae are presented in Chapter 1. Pathogen infection
rates under field conditions in Michigan versus controlled laboratory conditions,
known to be optimal for fungal germination, were compared. Numerous climatic
and site-related factors that may affect E. maimaiga infection rates, including E.

maimaiga resting spore inoculum densities, were quantified. The proportion of

late-instar gypsy moth mortality in natural populations that was attributable to E.
maimaiga versus other pathogens, such as NPV, was surveyed at the field sites
and results are included in Chapter 1.

In Chapter 2, intensive 6-wk field bioassays that were conducted to
evaluate E. maimaiga and NPV infections of 4th-instar gypsy moth larvae, in
relation to hourly microclimatic conditions, are presented. Infection dynamics of
both pathogens, during the initial phase in the development of epizootics (i.e.
primary transmission), were evaluated at three field sites in Michigan over the
course of gypsy moth larval development from late May to early July in 2001
and 2002.

In Chapter 3, the climatic conditions in regions of the United States where
large-scale E. maimaiga epizootics have been documented, were compared to
the climate of North America overall and, most rigorously, to the North Central
region of the United States. I conjectured that regions with greater climatic
similarity to epizootic-specific environmental conditions may be more likely to
develop extensive E. maimaiga epizootics than regions that were less similar in
climate. The climatological software CLIMEX (Sutherst and Maywald 1985,
Sutherst et al. 1999) was used to compare epizootic-specific environmental
conditions to average climatic conditions, based on 30-yr average maximum
temperature, minimum temperature, total precipitation and precipitation pattern,
at sites throughout North America. Annual departures from the average climatic
condition, which may create ephemerally conducive conditions for E. maimaiga

epizootics in an otherwise, generally non-conducive region, or vice versa, were

10

also examined for the North Central region.

A concise section on the possible implications of this research is
presented in conclusion. Additionally, several appendices are included that are
pertinent to this research. Appendix A contains a deposition record of voucher
specimens and voucher specimen data. Michigan climate, including average
daily temperature (°C), average daily maximum temperature (°C), average daily
minimum temperature (°C), and average monthly precipitation (mm) for January
through December, in relation to locations of E. maimaiga field bioassays is
included in Appendix B. North American locations used in the CLIMEX climate-
matching analyses are listed in Appendix C and summaries of the understory
vegetation and ground flora at the E. maimaiga field bioassay sites are provided

in Appendix D.

11

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CHAPTER 1

FIELD AND LABORATORY EVALUATION OF GYPSY MOTH INFECTION BY
Entomophaga maimaiga: POTENTIAL INFLUENCE OF LARGE-SCALE

METEOROLOGICAL EVENTS

INTRODUCTION

The fungal pathogen Entomophaga maimaiga Humber, Shimazu and
Soper (Zygomycetes: Entomophthorales) has been responsible for significant
declines in gypsy moth Lymantria dispar L. (Lepidoptera: Lymantriidae)
defoliation in the northeastern US. since 1989 (Andreadis 3nd Weseloh 19903,
1990b, Hajek et al. 1990b, Hajek 1999). Entomophaga maimaiga is 3 desirable
biological control agent because it affects few non-target species (Soper et al.
1988, Vandenberg 1990, Hajek et al. 19953, 19963, 1996b, 2000) and is
compatible with other natural enemies, including the gypsy moth
nucleopolyhederosis virus (NPV) (Andreadis 3nd Weseloh 19903, Hajek and
Roberts 1992, Weseloh and Andreadis 1992b).

Gypsy moth larvae become infected when E. maimaiga spores adhere to
the cuticle, then gain entry to the host using 3 combination of mechanical
pressure and enzymatic degradation (Hajek 1999). Entomophaga maimaiga
resting spores overwinter in the soil (Hajek et al. 19983), with the highest levels

of resting spores occur in the organic layer of soil at the base of trees (Hajek et

13

al. 19983). Gypsy moth larval behavior, as they move up and down trees,
increases the risk of fungal infection (Hajek 2001). Depending on ambient
environmental conditions, 3 portion of these resting spores germinate in the
spring (Hajek 1997b, Hajek and Humber 1997, Weseloh and Andreadis 1997)
and infect early-instar gypsy moth larvae (i.e. primary transmission) (Hajek
2001). When these infected larvae die, E. maimaiga conidiophores that are
produced externally on the cadavers discharge conidia and infect mid- to late-
instar gypsy moth (i.e. secondary transmission). Late-instar cadavers principally
produce resting spores and usually are found attached to lower tree trunks by
their prolegs with their heads oriented downwards (Hajek and Soper 1991,
Hajek et al. 1998b). These cadavers drop to the soil at the base of the tree,
decompose and resting spores remain in the soil until the following spring.

The overall goal of this study was to evaluate the influence of climatic
factors on E. maimaiga infection rates. Epizootics of E. maimaiga continue to
occur frequently in much of the northeastern United States (Hajek et al. 1995b,
1996b, Hunter and Elkinton 1999). This fungal pathogen, however, has been
less consistent in other states, such as Michigan (Smitley et al. 1995).
Entomophaga maimaiga was first introduced into Michigan in 1991 and spread
across much of the state by 1995 (Smitley et al. 1995). Substantial gypsy moth
defoliation has continued to occur however since 1996, with 242,361 ha of
Michigan forests sustaining moderate to heavy defoliation between 1997 and
2003 (USDA-F8 2004).

The primary objective of this project was to compare E. maimaiga

14

infection rates of gypsy moth larvae under field conditions with infection rates
under laboratory conditions that were optimal for fungal germination. Field
bioassays to assess infection of 4"“-instar larvae were conducted from 1999 to
2001 in oak-dominated forests. Laboratory bioassays were conducted with
larvae exposed to soil collected from field sites.

Germination of E. maimaiga resting spores is sensitive to moisture and
temperature (Hajek et al. 1993, Weseloh et al. 1993), so it is likely that
successful infection will vary annually depending on environmental conditions.
My second objective was to compare regional monthly precipitation and
temperature in northern Michigan in 1999 - 2001 with 30-yr averages to assess
variability in precipitation and temperature and relate potential correlations to

observed E. maimaiga infection rates.

15

MATERIALS AND METHODS

Study sites 8. field measurements

Thirty-two oak-dominated stands, each at least 10 ha in size, were
selected in Michigan in 1999 (Figure 1.1). An additional stand that experienced
an E. maimaiga epizootic in 1999 (NW. Siegert, unpubl. data, site no. 33, Clare
Co.) was included in the study in 2000 and 2001. All stands had at least one
documented E. maimaiga epizootic between 1993 and 1998 (Buss 1997, L.J.
Buss and 0G. McCullough unpubl. data, Michigan Department of Natural
Resources [MDNR] and Michigan Department of Agriculture [MDA] unpubl.
data). Stands were located on public land (Huron-Manistee National Forest,
Michigan State University’s WK. Kellogg Experimental Forest, and several
MDNR-managed state forests, including the Au Sable and Pere Marquette State
Forests) and were separated by at least 5 km. Density of gypsy moth
populations in each stand were quantified annually by averaging counts of egg
masses in two to four 0.01 ha fixed-radius plots (Kolodny—Hirsch 1986).

Stands were characterized by 3 dominant mixed oak (Quercus spp.)
overstory with ca 90% canopy closure (Table 1.1) and 3 sparse to moderately
dense understory, which consisted of mixed oak, witch-hazel (Hamamelis
virginiana L.), red maple (Acer rubrum L.), white pine (Pinus strobus L.), black
cherry (Prunus serotina Ehrh.), sassafras (Sassafras albidum (Nutt.) Nees), and
serviceberry (Amelanchier arborea (Michaux f.) Fern.) (Appendix D1). Ground

flora tended to be moderately dense with bracken fern (Pteridium aquilinum (L.)

16

Kuhn), grasses and sedges (Gramineae and Cyperaceae), red maple and
mixed oak regeneration, and low sweet blueberry (Vaccinium angustifolium
Alton) being the most common species (Appendix D2). Within each stand, I
established a plot center and selected dominant oak trees at 10, 25 and 50 m
along transects in each cardinal direction from the plot center (12 sample trees
per stand). Sample trees averaged 38.4 i 1.2 cm in diameter at breast height
(DBH). Percentage canopy cover was measured in the four cardinal directions
at each plot center with a concave spherical densiometer (Lemmon Forest
Densiometers, Bartlesville, OK) and averaged (Table 1.1). Basal area was
estimated with a 10-factor wedge prism at the plot center of each stand (Table
1.1). Soil in the selected stands was typically well-drained with a thin organic
layer and a pH of ca 4.6 (Table 1.1). Soil pH was measured with a hand-held
pH meter (WTW Measurement Systems, Inc., Ft. Myers, FL) from homogenized
soil collected from the northern and southern aspects at the base of 12 sample
trees in each stand.

To estimate the amount of fungal inoculum present at each stand, I
quantified E. maimaiga resting spores in the soil. A soil sample, ca 85 cm3, was
collected at the base of each sample tree, where the highest levels of resting
spores occur (Hajek et al. 19983), using a modified bulb planter. All soil
samples were collected at the beginning of the field bioassays, when cages of
gypsy moth larvae were placed in the stands (see below). To avoid inadvertent
transportation of E. maimaiga resting spores between field sites, disposable

non-latex gloves (Medline Industries, Inc., Mundelein, IL) and boot covers

17

(McKesson General Medical Corporation, Richmond, VA) were used and
disposed of following visits to each site. All equipment used in the stands was
sterilized with 95% ethanol and thoroughly rinsed with distilled water.
Equipment used to collect soil samples from each aspect of 3 sample tree was
also sterilized and rinsed between each sample. In 1999, soil samples were
composited by cardinal direction for each stand (e.g. four composite soil
samples per stand) and securely stored in plastic resealable bags. In 2000 and
2001, soil samples were collected only from northern and southern aspects at
the base of each sample tree and composited by aspect (e.g. two composite soil
samples per stand). The homogenized soil samples were transported from the
field to the laboratory in coolers with ice packs and stored at 5 °C to inhibit
fungal germination prior to resting spore quantification.

Wet-sieving of the soil, followed by density-gradient centrifugation using
Percoll (Sigma-Aldrich Company, St. Louis, MO), and microscopy was used to
quantify E. maimaiga resting spores (number per gram of dry soil) for each
composite sample in each stand in 1999 and 2000 (MacDonald and Spokes
1981, Li et al. 1988, Hajek and Wheeler 1994). Absolute counts of E. maimaiga
resting spores in soil were not conducted in 2001 because of limited resources

to complete the labor intensive sampling.
Field bioassays

To assess E. maimaiga infection rates under field conditions, I conducted

4-d field bioassays with freshly-molted 4‘“-instar gypsy moth larvae. Gypsy

18

moth egg masses were obtained from USDA APHIS, Otis Air National Guard
Base, MA, and larvae were reared in early June on artificial diet (O’Dell et al.
1985) at the USDA APHIS PPQ Biological Control Laboratory, Niles, MI. Each
morning, 4‘“-instar gypsy moth larvae that had molted in the previous 24 hr were
collected for field bioassays. Field bioassays corresponded to the occurrence of
4‘“-instar larvae of the wild gypsy moth populations (typically early to mid-June
in Michigan) to simulate the timing of naturally-occurring E. maimaiga infections.
Larval development was staggered so that sufficient numbers of freshly molted
4th-instar larvae (approximately 4000 larvae per day in 1999 and approximately
2000 per day in 2000 3nd 2001) were available each morning for the duration of
the field bioassays.

Field bioassays were conducted at each stand by placing 20 of the 4‘“-
instar larvae in 15 x 20 cm cages made of 6 x 7 mesh/cm2 aluminum screening
(Hajek and Humber 1997). Cages used for field bioassays were sterilized
annually with 95% ethanol. Two ca 15 9 pieces of high wheat germ artificial diet
(O’Dell et al. 1985), sufficient to last the duration of the field bioassay, were
placed in each cage. One cage was placed on the soil surface at the base of
each sample tree In each cardinal direction in each stand and collected four
days later. After four days in the field, cages of larvae were collected,
individually stored in plastic bags to prevent contamination during transport, and
returned to the USDA APHIS PPQ Biological Control Laboratory. Larvae were
reared individually in 50 mL cups on artificial diet following standard protocols

for assessing fungal infections (Papierok and Hajek 1997). Larvae were reared

19

3t 20 °C and 14:10 h (lightzdark photoperiod) for 10 d, then placed in the dark
for 3 d at 20 °C. After 3 d, the cadavers were checked for presence or absence
of E. maimaiga conidia. If conidia were present, then cadavers were transferred
to cold storage (4 °C and dark). If conidia were not present, then larvae were
kept at 20 °C in the dark for an additional 7 d before being transferred to cold
storage. Gypsy moth cadavers were dissected and examined with 3
microscope to determine whether E. maimaiga resting spores or NPV was
present. Nearly 300 gypsy moth larvae were reared in the laboratory to check
for possible laboratory contamination with E. maimaiga or NPV and 100% of the
larvae survived to pupation.

Infection rates for E. maimaiga for the 4-day field and laboratory
bioassays were calculated as the percentage of larvae infected by E. maimaiga
out of the total number of larvae examined (i.e. total number of cadavers
processed plus the number of larvae that survived to pupation). Because of the
more rapid pathogenesis from E. maimaiga than NPV after simultaneous
infection (Hajek 1997a, Malakar et al. 19993, 1999b), larval cadavers found to
be co-infected with E. maimaiga and NPV were assumed to have died from E.

maimaiga infection.

Laboratory bioassays
Soil collected at the beginning of the field bioassays from the base of the
sample trees (as described above) was used for laboratory bioassays, as well

as resting spore analysis. The homogenized soil samples were shipped

20

overnight to Cornell University in coolers with ice packs to keep the E. maimaiga
resting spores from germinating. At Cornell University, freshly-molted 4'“-instar
gypsy moth larvae (reared on artificial diet from egg masses obtained from
USDA APHIS, Otis Air National Guard Base, MA) were placed on 35 g of soil
from the field bioassay stands in polypropylene containers with lids (4.5 cm in
height and 10.5 cm in diameter) at standardized moisture levels of 100% and
reared at 15 °C and 14:10 h (lightzdark photoperiod) for 4 d (Hajek et al. 2004).
These conditions are optimal for E. maimaiga resting spore germination
(Shimazu and Soper 1986, Shimazu 1987, Hajek et al. 1990b, Hajek 1997b).
Thirty gypsy moth larvae per aspect were exposed to soil from each field
bioassay stand in 1999 (120 larvae per stand; 3840 total larvae). In 2000 and
2001, 40 larvae per aspect were exposed to soil from each field bioassay stand
(80 larvae per stand each year; 2640 total larvae each year). Larvae were then

reared to detect fungal infections as described above for the field bioassays.

Infections in forest-collected gypsy moth larvae

Wild gypsy moth populations present at field sites were surveyed each
year to examine prevalence of E. maimaiga and NPV mortality in late-instar
larvae. Late-instar larval cadavers from wild gypsy moth populations present at
field sites were collected as larvae start to pupate between late June and early
July. Burlap bands (40 x 80 cm) were placed at breast height on the 25 m
sample trees when cages were retrieved at the end of the field bioassays. Up to

40 gypsy moth cadavers per stand were collected from the burlap bands and

21

placed in individual containers. Cadavers were dissected and examined with a
microscope to determine if E. maimaiga or NPV was responsible for mortality of

the wild cadavers.

Precipitation & temperature departures from 30-yr averages

Since E. maimaiga resting spore germination is sensitive to moisture and
temperature (Hajek et al. 1993, Weseloh et al. 1993), it is likely that successful
infection will vary annually depending on environmental conditions. Weather
data from the National Oceanic and Atmospheric Administration (NOAA) for
climate divisions across northern lower Michigan, which encompassed the
majority of the field sites, were used to approximate area-wide departures from
the 30-yr averages of monthly air temperature and precipitation for 1999, 2000
and 2001 (MRCC 2002, NOAA 20023, 2002b). A climate division is a region
within a state that is as climatically homogeneous as possible (NOAA 20023,
2002b). Climate divisions are often used for various research applications by
climatologists to assess regional climatic trends over time (e.g. 30 year periods)
(NOAA 20023, 2002b). Departures were determined by calculating the
differences between actual climatic conditions (MRCC 2002) and monthly 30-yr
average conditions for the four climate divisions across northern lower Michigan

for April, May and June between 1999 and 2001 (NOAA 20023, 2002b).

Statistical analysis

Simple linear regression analyses were used to analyze relationships

22

between E. maimaiga resting spore counts and soil pH on E. maimaiga infection
rates in field and laboratory bioassays (SYSTAT 2000). Overall differences in
E. maimaiga resting spore counts and infection rates in field and laboratory
bioassays among aspects were analyzed for each year using analysis of
variance (ANOVA) (SYSTAT 2000). Entomophaga maimaiga infection rates in
laboratory and field bioassays were not normally distributed among sites, so the
nonparametric Wilcoxon signed-rank test (Sokal and Rohlf 1995) was used to
test for differences in infection levels between laboratory and field bioassays
each year (SYSTAT 2000).

Monthly precipitation and temperature values for climate divisions in
northern lower Michigan were tested for differences from 30-yr averages using
two-tailed t-tests, with critical values of tIO-S. 6) = 2.447 and t<o.5. 3) = 3.182 (Sokal
and Rohlf 1995) for temperature and precipitation differences, respectively. The
precipitation data were heteroscedastic, so the degrees of freedom for
precipitation t-tests were reduced, as recommended by Sokal and Rohlf (1995),

when the sizes of the two samples were equal.

23

RESULTS

Resting spore density in soil

The overall quantity of E. maimaiga resting spores in the soil at each site
averaged 154 i 12 and 232 i 30 spores/g dry soil in 1999 and 2000,
respectively (Table 1.2). Density of resting spores was highly variable in each
year, ranging from 21 to 623 spores/g dry soil in 1999 and 33 to 932 spores/g
dry soil in 2000. Differences in the number of E. maimaiga resting spores in the
soil between aspects were not significant in 1999 (P > 0.05) or 2000 (P > 0.05)
(Table 1.2).

Entomophaga maimaiga resting spore density in the soil was examined
in relation to gypsy moth egg mass densities at the field sites. Gypsy moth egg
mass densities in the field sites generally decreased from 1999 to 2001 (Table
1.1), with gypsy moth populations in many of the stands remaining at low levels
through the duration of this project. The change in the number of resting spores
was not significantly associated with the change in gypsy moth population
densities between 1999 and 2000 (3 = 0.01, P = 0.60). Interestingly, three
stands that had the largest decreases in gypsy moth egg mass densities from
1999 to 2000 (sites 5, 11, and 31) did not exhibit any change in E. maimaiga

resting spore density.

Field bioassays

A total of 62,400 laboratory-reared gypsy moth larvae were used in the

24

field bioassays from 1999 to 2001, including 30,720 larvae in 1999, and 15,840
larvae in 2000 and in 2001. A portion of the larvae did not survive the 4-day
exposure to field conditions each year, typically because of predation by ants or
insectivorous rodents. In 1999, 21,769 gypsy moth larvae were returned intact
from the 4-day field exposure, while 11,885 and 15,811 larvae were returned in
2000 and 2001, respectively.

In 1999, 3,953 gypsy moth larvae (18.2%) survived to pupation and a
total of 10,436 larvae that died before pupating were processed. These larval
cadavers were processed to determine whether E. maimaiga was the pathogen
responsible for mortality. Infection by E. maimaiga killed 29.8 :I: 2.3% of all
larvae that were evaluated in the 1999 field bioassay (i.e. number of gypsy moth
larvae that survived plus number of larval cadavers processed) (Table 1.2). The
rest of the larval cadavers that were processed were determined to have been
killed by NPV (43.4%).

In 2000, 2,204 gypsy moth larvae (18.5%) survived to pupation out of the
11,885 larvae that were returned from the 4-day field exposure. A total of 6,761
larval cadavers were processed. Percentage of infection by E. maimaiga
dropped to less than half of that observed during the previous year’s field
bioassay. Entomophaga maimaiga infection was responsible for mortality of
10.8 1 1.6% of the larvae that were evaluated (Table 1.2). The remaining larval
cadavers that were processed were determined to have been killed by NPV
(65.2%).

In 2001, nearly a third of the gypsy moth larvae (32.7%) that were

25

returned intact from the field exposure survived to pupation (5,173 out of 15,811
larvae). Infection by E. maimaiga was even lower In 2001. A total of 8,306
larval cadavers were processed and less than 4% of the larvae (3.4 i 0.7%)
were infected with E. maimaiga (Table 1.2). As in 1999 and 2000, the rest of
the larval cadavers that were processed were determined to have been killed by
NPV (55.2%).

Entomophaga maimaiga field infections were examined in relation to
several site-related factors, including canopy cover, aspect, E. maimaiga resting
spore density in the soil, and soil pH. Canopy cover was generally not
associated with differences in E. maimaiga field infection rates (Figure 1.2). In
1999, however, the relationship between canopy cover and E. maimaiga field
infection rates was marginally significant (r2 = 0.12, P = 0.051) (Figure 1.2A).
Differences in E. maimaiga field infection rates between aspects were not
significant (P > 0.05) in 1999, 2000, or 2001 (Table 1.2). Infection of gypsy
moth larvae under field conditions by E. maimaiga was not significantly
correlated with the number of E. maimaiga resting spores in the soil in 1999 (r2
= 0.04, P = 0.27) (Figure 1.3A). In 2000, however, increases in E. maimaiga
field infection of gypsy moth was significantly correlated with increases in the
quantity of fungal inoculum (Figure 1.38). Entomophaga maimaiga infection of
gypsy moth larvae in field bioassays was not significantly associated with soil

pH between 1999 and 2001 (P > 0.05).

26

Laboratory bioassays

A total of 9,120 gypsy moth larvae were used in the laboratory bioassays
from 1999 to 2001 (3,840 in 1999; 2,640 in 2000 and 2001). In 1999, infection
of gypsy moth larvae by E. maimaiga was 20.9 t 4.1% in the laboratory
bioassays and was significantly lower than the level of E. maimaiga infection
observed in the field bioassays in 1999 (Wilcoxon’s Z = -2.116; P = 0.034)
(Table 1.2). In 2000 and 2001, however, this trend was reversed and infection
rates were significantly higher in laboratory bioassays versus field bioassays.
Entomophaga maimaiga infected 43.7 :1: 4.4% (Wilcoxon’s Z = 4.672; P <
0.0005) and 59.7 :l: 4.5% (Wilcoxon’s Z = 4.994; P < 0.0005) of the gypsy moth
larvae in laboratory bioassays in 2000 and 2001, respectively (Table 1.2).
Infection of gypsy moth larvae by other pathogens, such as NPV, was extremely
rare (< 0.5%) in laboratory bioassays.

Differences in E. maimaiga laboratory infection rates between aspects
were not significant in 1999 (P > 0.05) or 2000 (P > 0.05). In 2001, however, E.
maimaiga laboratory infection rates between aspects were significantly different
(P < 0.05), with infection rates on northern aspects being greater than on
southern aspects (Table 1.2). Laboratory infection of gypsy moth larvae by E.
maimaiga increased linearly as the quantity of fungal inoculum (i.e. resting
spore density) increased in 1999 and 2000 (Figure 1.4A, 8). As few as 235
spores/g dry soil and 112 spores/g dry soil caused 260% infection of larvae
reared under optimal laboratory conditions in 1999 and 2000, respectively

(Figure 1.4A, 8). Entomophaga maimaiga infection of gypsy moth larvae in

27

laboratory bioassays increased linearly in association with soil pH in 1999
(Table 1.1; Figure 15A). The linear relationship, however, was only marginally
significant in 2000 (r2 = 0.12, p = 0.052) (Figure 1.53) and not significant in
2001 (r2 = 0.04, P = 0.25) (Figure 1.58). Infections of gypsy moth larvae by E.
maimaiga in field and laboratory bioassays were not significantly associated
with each other in 1999 (I2 = 0.001, P = 0.91) and 2000 (r2 = 0.003, P = 0.76),
but were significantly associated with each other in 2001 (r2 = 0.14, P < 0.05)
(Figure 1.6).

Infections in forest-collected gypsy moth larvae

Up to 40 cadavers of late-instar gypsy moth larvae from wild populations
present in field sites were collected from under burlap bands each year in early
July from 1999 to 2000 (total of 917 larval cadavers). The number of field sites
where gypsy moth cadavers were present ranged from 11 to 29 (Table 1.3).
Entomophaga maimaiga was the dominant pathogen in the late-instar cadaver
collections each year. Infections in forest-collected gypsy moth by E. maimaiga
ranged from 76.6 to 90.8%, while NPV infections ranged from 9.2 to 29%

between 1999 and 2001 (Table 1.3).

Precipitation & temperature departures from 30-yr averages
Long-term comparisons of climatic data are typically conducted with
monthly 30-yr averages for variables such as precipitation and temperature

(NOAA 20023, 2002b). Precipitation and temperature for April, May and June

28

were evaluated during this study because that is the time period that
germination of E. maimaiga resting spores and infection of gypsy moth larvae
may occur (Hajek and Roberts 1991). Compared to 30-yr averages, June
weather was significantly warmer and wetter in northern lower Michigan when
field bioassays were conducted in 1999. June precipitation was 32.7 :t 12.5 mm
greater than the 30-yr average (t = 5.495, P < 0.05). The average temperature
was 1.9 :t 0.2 °C higher than the 30-yr average (t = 5.822, P < 0.05) for the
region in 1999 (Table 1.4). April and May temperatures were also significantly
higher than the 30-yr average (t = 6.155, P < 0.05; and t = 5.547, P < 0.05,
respectively), but precipitation in those months did not significantly differ from
the 30-yr average (t = 0.244, P > 0.05; and t = 1.286, P > 0.05, respectively) for
the region in 1999 (Table 1.4).

The weather was much closer to average across the region in 2000 and
June weather was not significantly different from normal conditions. June
precipitation was only 4.5 :I: 5.5 mm greater (t = 1.499, P > 0.05) and average
temperature was only 0.2 :I: 0.2 °C greater (t = 0.623, P > 0.05) than the
respective 30-yr average for each variable (Table 1.4). Likewise, April
precipitation and temperature was not significantly different from 30-yr averages
(t = 0.555, P > 0.05; and t = 0.000, P > 0.05, respectively). May, however, was
significantly warmer (t = 3.579, P < 0.05) and wetter than average (t = 6.262, P
< 0.05) (Table 1.4).

In 2001, May was again significantly warmer (t = 5.226, P < 0.05) and

wetter than average (t = 8.284, P < 0.05), but June weather was significantly

29

drier than normal compared to 30—yr averages (t = 4.407, P < 0.05) , with 10.6 i
5.0 mm less precipitation than the 30-yr average (Table 1.4). Additionally, the
average temperature in June of 2001 was only 0.5 :I: 0.3 °C greater than the 30-
yr average for northern lower Michigan and not significantly different (t = 1.602,
P > 0.05). April temperature was significantly higher than the 30-yr average (t =
5.905, P < 0.05), however, April precipitation, though greater than the 30-yr
average, was not significantly different from normal conditions (t = 3.154, P >

0.05) (Table 1.4).

30

DISCUSSION

Entomophaga maimaiga germination and infection rates have often been
correlated with environmental moisture (Shimazu 1987, Hajek et al. 19903,
Hajek 1999). In a study examining the dynamics of resting spore development
and germination, Hajek and Humber (1997) found that E. maimaiga infection
increased with greater soil moisture. I hypothesized that E. maimaiga infection
levels would be lower in field versus laboratory conditions because of E.
maimaiga’s sensitivity to moisture and temperature (Hajek et al. 1993, Weseloh
et al. 1993) and the considerable, inherent variability of climatic conditions in the
field. Several manipulative experiments have demonstrated increased E.
maimaiga infection with the application of water (Weseloh and Andreadis 19923,
1992b, Hajek and Roberts 1991, Hajek et al. 1996b) and extensive E. maimaiga
epizootics have also been associated with above average rainfall (Andreadis
and Weseloh 19903, 1990b, Hajek 1999, Webb et al. 1999). Weseloh and
Andreadis (19923) found that infection at ten locations in Connecticut was
positively associated with June precipitation, but not May precipitation, despite
abundant May rainfall. In contrast, Hajek et al. (1996b) found that precipitation
in May was significantly correlated with infection levels, but June precipitation
was not, at plots in Maryland, Pennsylvania, Virginia and West Virginia. Five of
the seven experimental plots in 1992 (Hajek et al. 1996b), though, had greater
than 60% E. maimaiga infection when more than 55 mm of precipitation fell in

both May and June. At three locations in Michigan over the course of three

31

years, Smitley et al. (1995) found that infection was positively correlated with
precipitation during the two week period in June prior to sampling. My results
demonstrate that substantial annual variation in E. maimaiga infection levels
may occur at individual sites and that increased levels of field infection were
positively associated with abundant June precipitation. Similar to results of
Weseloh and Andreadis (19923), abundant May precipitation did not appear to
positively influence E. maimaiga infections during this study in 2000 and 2001.
Pathogen-related mortality of gypsy moth larvae in the field bioassays
was dominated by NPV, which was the major gypsy moth pathogen in North
America (Doane 1970, Campbell and Podgwaite 1971) before E. maimaiga was
discovered causing epizootics in the northeastern United States. Epizootics
caused by NPV are generally considered to function as a density-dependent
mortality factor (Doane 1970, Woods and Elkinton 1987) and sufficient NPV
inoculum needs to accumulate before a viral epizootic may occur. Gypsy moth
populations at the field sites were generally at high densities in 1999, but tended
to decrease to low densities in 2000 and 2001. NPV was the dominant
pathogen during this study from 1999 to 2001 and suggests that NPV inoculum
at the sites was in large enough titers to potentially initiate a viral epizootic had
high densities of gypsy moth been present. Viral infections in my laboratory
bioassays, however, were extremely rare. The high levels of NPV infection
could alternatively be explained by latent viral infections in larvae used in field
bioassays that became activated during the 4-d field exposures. Stressful

physical, chemical or physiological conditions have been suggested to induce

32

latent viral infections in some insects (Troitskaya and Chichigina 1980, Petre
and Fuhrmann 1981, Hughes et al. 1993, Stoltz and Makkay 2003). In a closely
related Lymantria spp., latent NPV infections were apparently activated by
reductions in temperature (Bakhvalov et al. 1979). Whether or not latency may
have been responsible for the high levels of viral infection observed in this study
remains to be tested.

Results from this study demonstrate that the gypsy moth fungal
pathogen, E. maimaiga, is capable of high levels of infection under favorable
conditions, but may be limited by weather in some regions. Only a portion of the
E. maimaiga resting spores in the soil may germinate annually depending on
ambient conditions, which enables E. maimaiga to persist in the environment
when gypsy moth populations are not present (Hajek and Humber 1997, Hajek
1999). In my field bioassays, E. maimaiga infection of gypsy moth larvae
decreased from 1999 to 2001, while E. maimaiga infections in laboratory
bioassays, using soil from the field bioassay sites, increased during that period.
This pattern may reflect weather conditions in May and June. Interpretation,
however, should be approached with caution because monthly-based
meteorological factors are coarse descriptors of climate and may not accurately
reflect site-specific environmental conditions during the phenology of gypsy
moth larvae and the development of E. maimaiga epizootics. Despite these
short comings, calendar months are the most available units for long-term
comparisons of meteorological data and this approach may be insightful for

initial exploration of E. maimaiga’s potential dependency on weather-related

33

factors. In 1999, sites experienced significantly wetter and warmer weather
than normal in June, presumably more E. maimaiga resting spores germinated
prior to the start of the bioassays, and infection levels were relatively high in the
field bioassay. In 2000 and 2001, June weather was close to normal and drier
than normal, respectively, and low levels of E. maimaiga infection were
observed in the field. In laboratory bioassays, however, levels of E. maimaiga
infection were much higher. This could be explained by fewer resting spores
being available in soil samples for germination in the laboratory bioassays when
the environmental conditions were favorable, such as 1999, compared to when
the environmental conditions less than favorable, such as 2000 and 2001. If
true, an inverse relationship should exist between field and laboratory infection
rates. However, such a relationship is not supported by the current study.

An alternative explanation for the observed difference between field and
laboratory infection levels could be that specific environmental cues that initiate
germination of E. maimaiga resting spores vary in relation to the length of time
that they have persisted in the soil. Variation in E. maimaiga germination in
relation to resting spore age, however, remains to be tested. Another
explanation could simply be that larval infection during 4-d field bioassays is
strongly affected by variations in microclimate. Entomophaga maimaiga
germination rates may be lower because favorable environmental conditions do
not exist for long enough durations because of the diurnal periodicity of climatic
conditions (e.g. air temperatures typically highest in the mid-afternoon and

lowest at sunrise). Germination of E. maimaiga is known to vary in relation to

34

the duration of time under given conditions (Hajek and Humber 1997).
Individual 4-d bioassays may be a good method for evaluating transmission of
E. maimaiga during the four day time period, but should be used cautiously in
assessing potential gypsy moth infections. Consecutive bioassays over longer
periods of time during gypsy moth larval development, however, may be useful
in evaluating E. maimaiga infections under varying conditions. Examination of
forest-collected Iate-instar larvae may provide an estimate of E. maimaiga and
NPV prevalence in wild gypsy moth populations.

Entomophaga maimaiga inoculum in the soil varied considerably among
field sites. Several factors may account for this variation, such as the frequency
of climatic conditions favorable for E. maimaiga germination, the history of
gypsy moth population levels, and differential rates of germination in relation to
length of persistence of resting spores in the environment. Previous field
studies have shown that E. maimaiga infection levels of 80% or more may occur
in gypsy moth larvae with as few as 255 resting spores per gram of dry soil
present (Hajek and Roberts 1991). Similarly, results from this research confirm
that relatively low levels of fungal inoculum (235 and 112 E. maimaiga spores
per gram of dry soil in 1999 and 2000, respectively) in the soil were associated
with high levels of E. maimaiga infection in the laboratory when environmental
conditions were favorable for germination.

Soil pH may be an important factor in E. maimaiga resting spore
germination and disease transmission. Valovage and Kosaraju (1992) found

that the highest levels of Entomophaga calopteni resting spore germination

35

occurred in the pH range of 6 - 8. Results from this study suggest that soil pH
may also affect the germination of E. maimaiga resting spores. More research
is needed, however, to conclusively evaluate the effect of soil pH on resting
spore germination and persistence. Future studies should address the role of
soil pH in affecting E. maimaiga resting spore germination and persistence.
Entomophaga maimaiga is highly synchronized with gypsy moth
phenology (Hajek et al. 19953, Hajek and Humber 1997) and variations in
environmental conditions, primarily moisture relations (i.e. precipitation) during
the ca 2 months of larval development may play a critical role in the
development, persistence and frequency of E. maimaiga epizootics. In regions
where long-term, average environmental conditions are not favorable for E.
maimaiga epizootics, highly variable areas are more likely to occasionally
experience conditions necessary for epizootics than areas of low variability.
Alternatively, in regions where long-term, average environmental conditions
tend to be favorable for E. maimaiga epizootics, highly variable areas are more
likely to experience adverse conditions and fewer epizootics than areas of low
variability. Additional research involving landscape-level studies and long-term
monitoring will be needed to fully assess the role of climatic variability in the
success of E. maimaiga as an effective biological control agent of gypsy moth,

as the range of gypsy moth continues to expand in North America.

36

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38

Hajek, A.E., L. Butler, and MM. Wheeler. 1995a. Laboratory bioassays testing
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39

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40

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41

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of Invertebrate Pathology. 50: 151-157.

42

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Figure 1.1. Numbers (1 - 32) mark the locations of Entomophaga maimaiga
field sites in Michigan, 1999 to 2001. Site no. 33 was used as a field site in
2000 and 2001 after an E. maimaiga epizootic occurred there in 1999.

48

 

100---,” ~«w— ——— «amen—“fl- has“-..
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E. maimaiga field infection (%)

 

 

 

Figure 1.2. Field infection (%) of gypsy moth larvae by Entomophaga maimaiga
in relation to canopy cover (%) in (A) 1999, (B) 2000, and (C) 2001. There was
not a significant trend in E. maimaiga field infection by canopy cover in 2000 or
2001.

49

100

 

 

 

 

 

 

 

 

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Figure 1.3. Field infection (%) of gypsy moth larvae by Entomophaga maimaiga
in relation to the number of resting spores per gram of dry soil in (A) 1999 and
(8)2000. There was not a significant trend in E. maimaiga field infection by the
number of resting spores in the soil in 1999.

50

 

301

E. maimaiga laboratory infection (%)

 

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10-

E. maimaiga laboratory infection (%)

 

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200 400 600 800 1000
E. maimaiga resting spores (No.19 dry soil)

Figure 1.4. Laboratory infection (%) of gypsy moth larvae by Entomophaga
maimaiga in relation to the number of resting spores per gram of dry soil in (A)

1999 and (B) 2000.

51

.L
O
O

90 1 A. 1999
80 1

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3.0 3.5 4.0 4.5 5.0 5.5 6.0

100 1mm — ~- » w—a eke—m.
904 C. 2001 o ‘ 0 o g l
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50 1 O

40 1 . . .
30 . .0 I
20 ‘ '
1o « 0 I

E. maimaiga laboratory infection (%)

 

 

3.0 3.5 4.0 4.5 5.0 5.5 6.0
Soil pH

Figure 1.5. Laboratory infection (%) of gypsy moth larvae by Entomophaga
maimaiga in relation to soil pH in (A) 1999, (B) 2000, and (C) 2001. There was
not a significant trend in E. maimaiga laboratory infection by soil pH in 2001.

52

 

 

 

 

 

 

 

 

§ 100 - -— __ ——~—————— — - - —— —~—-.
= 90 « A. 1999
_O
8 80 '
a . 12 = 0.001
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l
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uj 0 4—O—r——-——." . 1* 4r 1? J
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2 . . 0 , r2 = 0.003
E 70 ' P = 0.76 -
O
5‘ 60
«I O
3 50 ’.o '
g 40 . o ‘ l
3 30 I ' o ’ o l
9 o l
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to l
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E. maimaiga field infection (%)

Figure 1.6. Laboratory infection (%) of gypsy moth larvae by Entomophaga
maimaiga in relation to E. maimaiga field infections (%) in (A) 1999, (B) 2000,
and (C) 2001. There was not a significant trend in E. maimaiga laboratory
infection by E. maimaiga field infection in 1999 or 2000.

53

CHAPTER 2

FUNGAL & VIRAL INFECTIONS OF GYPSY MOTH (LEPIDOPTERA:
LYMANTRIIDAE) LARVAE 8: EFFECTS OF MICROCLIMATIC CONDITIONS

IN THE INITIAL DEVELOPMENT OF EPIZOOTICS

INTRODUCTION

Entomopathogens are capable of causing a rapid change in their
prevalence over a short time period that results in a large-scale mortality event
within a host population, known as an epizootic (Fuxa and Tanada 1987).
Factors that initiate and affect the development of epizootics play a critical role
in regulating insect pathogen dynamics, but are generally poorly understood.
While host density is typically important for the amplification and intensity of
epizootics (Watanabe 1987), environmental conditions strongly influence
pathogen activity and are integral in the initial development of epizootics
(Andreadis 1987, Benz 1987). Because of the inherent variability of
environmental conditions in natural systems, the occurrence and intensity of
entomopathogenic epizootics are usually difficult to accurately predict.

The gypsy moth nucleopolyhederosis virus (NPV) was first detected in
North America in the early 1900’s (Glaser 1915). It can cause dramatic
epizootics in gypsy moth [(Lymantria dispar L.) (Lepidoptera: Lymantriidae)]

populations, though it typically becomes abundant only when gypsy moth

54

population densities are high (Doane 1970, Leonard 1981, Woods and Elkinton
1987). Typically, larvae become infected with NPV by ingesting foliage
contaminated with the virus (Murray and Elkinton 1989), though other modes of
infection, such as transovum transmission (Doane 1969), are possible. Infected
early-instar larvae move to an elevated position, such as the ends of branches
or the tops of trees, where they die. As these dead larvae deteriorate, they
recontaminate the foliage and bark and provide viral inoculum for infection of
late-instar larvae (Woods and Elkinton 1987). Other biotic (e.g. other
caterpillars and insects, parasitoids, birds, mammals) and abiotic factors (e.g.
wind, rain) serve to further facilitate the spread and dispersal of the virus
(Podgwaite et al. 1981). This viral pathogen has often been responsible for
reducing outbreak gypsy moth populations to low densities and, until 1989, was
the dominant gypsy moth pathogen in North America.

Since 1989, the fungus Entomophaga maimaiga (Zygomycetes:
Entomophthorales) has become an important pathogen of gypsy moth in the
northeastern United States (Andreadis and Weseloh 1990a, 1990b, Hajek et al.
1995b, Hajek 1999). It is highly synchronized with gypsy moth larval
development, has relatively few negative effects on non-target species (Soper et
al. 1988, Vandenberg 1990, Hajek et al. 1995a, 1996a, 1996b, 2000), and is
compatible with other natural enemies, including NPV (Andreadis and Weseloh
1990a, Hajek and Roberts 1992, Weseloh and Andreadis 1992a), making E.
maimaiga a desirable biological control agent.

Entomophaga maimaiga produces two types of spores, both of which

55

may infect gypsy moth larvae (Hajek 1999). Resting spores of E. maimaiga
overwinter in the soil, with the highest densities of spores occurring in the
organic layer of soil at the base of trees (Hajek et al. 1998a). Behavior of late-
instar gypsy moth larvae, such as diurnal movement up and down from the tree
canopy (Forbush and Fernald 1896, Leonard 1981), increases the risk of fungal
infection by putting larvae in contact with the spore-bearing soil (Hajek 2001). A
portion of the E. maimaiga resting spores germinate in the spring depending on
environmental conditions (Hajek 1997b, Hajek and Humber1997,Weseloh and
Andreadis 1997). These spores infect and kill early-instar gypsy moth larvae (i.
e. primary transmission). Early-instar cadavers produce E. maimaiga
conidiophores externally that discharge conidia to infect mid- to late-instar gypsy
moth (i.e. secondary transmission). Late-instar larval cadavers principally
produce resting spores and are usually found attached to lower tree trunks by
their prolegs with their heads oriented downwards (Hajek and Soper 1991,
Hajek et al. 1998b). Cadavers drop to the soil, decompose and resting spores
remain dormant in the soil until the following spring.

Gypsy moth density and other host-associated factors do not appear to
influence primary transmission of E. maimaiga (Hajek and Eastburn 2001),
suggesting that environmental conditions are integral in the initial development
of epizootics. Infection by resting spores, the primary transmission of E.
maimaiga, was evaluated under field conditions using laboratory-reared 4th-
instar gypsy moth in 2001 and 2002 in Michigan oak-dominated forests. The

overall goal of this research was to acquire a better understanding of role of

56

microclimate in the initial development of E. maimaiga epizootics. The specific
objective of this project was to evaluate the relative infection rates of gypsy
moth larvae by the E. maimaiga and NPV pathogens in the field. Infection rates
during 4-d intervals were related to site-specific microclimatic variables

occurring over a 6-wk period of gypsy moth larval development.

57

MATERIALS AND METHODS

Study sites & field measurements

Three oak-dominated stands (Bitely, Jackson Corners, and Lilley) were
selected in the Huron-Manistee National Forest in Newaygo County, Michigan,
in 2001 (Table 2.1 ). Selected stands were at least 10 ha in size, known to have
experienced at least one E. maimaiga epizootic in the past (Buss 1997, L.J.
Buss and D.G. McCullough unpubl. data, Michigan Department of Natural
Resources [MDNR] unpubl. data) and were within 9.5 km of one another.
Additionally, stands had been utilized for related research the two years before
the current study (N.W. Siegert chapter one). Density of gypsy moth
populations in each stand were quantified annually by averaging counts of egg
masses in two 0.01 ha fixed-radius plots (Kolodny—Hirsch 1986). Gypsy moth
population densities were high in 1999 at these sites, but had decreased to low
densities by 2000 and remained at low densities during this study in 2001 and
2002 (Table 2.1 ).

Stands were characterized by a dominant mixed oak (Quercus spp.)
overstory with ca 90% canopy closure (Table 2.1) and a moderately dense
understory, which consisted primarily of sassafras (Sassafras albidum (Nutt.)
Nees) and witch-hazel (Hamamelis virginiana L.). Also lightly distributed in the
understory was some mixed oak, red maple (Acer rubrum L.), white pine (Pinus
strobus L.) and red pine (Pinus resinosa Aiton). Ground flora tended to be

moderately dense with bracken fern (Pteridium aquilinum (L.) Kuhn), low sweet

58

blueberry (Vaccinium angustifolium Alton), and red maple, mixed oak, sassafras
and witch-hazel regeneration being the most common species.

A plot center was established in each stand. Dominant oak trees at 25 m
along transects in each cardinal direction from the plot center were selected and
tagged (4 trees per stand). Sample trees at the sites ranged from 36.6 to 41.0
cm and averaged 38.7 i 1.3 cm in diameter at breast height (DBH). Soil at the
sites was well-drained and consisted primarily of Coloma-Spinks-Metea sandy
material (USDA-SCS 1995) with a thin organic layer and a pH of 4.4 :l: 0.2 (N.W.
Siegert chapter one). Percentage canopy cover was measured in the cardinal
directions at each plot center with a concave spherical densiometer (Lemmon
Forest Densiometers, Bartlesville, OK) and averaged 90.7 i 1.3% (Table 2.1).
Basal area was measured with a 10-factor wedge prism at the plot center of
each stand and averaged 23.7 :1: 1.3 m2/ha (Table 2.1).

Disposable non-latex gloves (Medline Industries, Inc., Mundelein, IL) and
boot covers (McKesson General Medical Corporation, Richmond, VA) were
worn and disposed of following visits to each site to avoid inadvertent
transportation of E. maimaiga between field sites. All equipment used in the
stands was sterilized with 95% ethanol and thoroughly rinsed with distilled water
between samples. Cages used for field bioassays were sterilized with 95%

ethanol after each use.

Field bioassays

Gypsy moth egg masses were obtained from USDA APHIS, Otis Air

59

National Guard Base, Massachusetts. Larvae were reared on artificial diet
(O’Dell et al. 1985) at the USDA APHIS PPQ Biological Control Laboratory,
Niles, Ml. Fourth-instar gypsy moth larvae that had molted within the previous
24 hr were selected daily for field bioassays. Larval development was
staggered so that sufficient numbers of freshly-molted larvae (approximately
500 larvae per day in 2001 and 2002) were available each morning for the
duration of the field bioassays.

Field bioassays were conducted at each stand by placing 20 of the 4‘"-
instar larvae in 15 x 20 cm cages made of 6 x 7 mesh/cm2 aluminum screening
(Hajek and Humber 1997). Two ca 15 9 pieces of high wheat germ artificial diet
(O’Dell et al. 1985), which was sufficient enough to last the duration of the field
bioassay, were placed in each cage. One cage was placed on the soil surface
on the northern and southern aspects at the base of each sample tree in each
stand and collected four days later (total of 8 cages per stand).

After four days in the field, cages of larvae were collected, individually
stored in plastic bags to prevent contamination during transport, and returned to
the USDA APHIS PPQ Biological Control Laboratory. Cages of larvae that had
been in the field for 4-d were replaced with cages of fresh larvae. Field
bioassays using 4‘“-instar larvae were continuously conducted for a 6-wk period,
corresponding with gypsy moth larval development in wild populations, from 25
May to 4 July in 2001 and from 24 May to 3 July in 2002.

After their 4-d exposure period, larvae were reared individually in 50 mL

cups on artificial diet following standard protocols for assessing fungal infections

60

(Papierok and Hajek 1997). Larvae were reared at 20 °C and 14:10 h (light:
dark photoperiod) for 10 d, then placed in the dark for 3 d at 20 °C. After 3 d,
the cadavers were checked for presence or absence of E. maimaiga conidia. lf
conidia were present, then cadavers were transferred to cold storage (4 °C and
dark). If conidia were not present, then larvae were kept at 20 °C in the dark for
an additional 7 d before being transferred to cold storage. Gypsy moth
cadavers were dissected and examined with microscopy to determine whether
E. maimaiga was present. To evaluate whether or not the laboratory was
contaminated with E. maimaiga or NPV, 293 gypsy moth larvae were reared in
the laboratory without undergoing field exposures and 100% of the larvae
survived to pupation.

Infection rates for E. maimaiga for the 4-d field bioassays were calculated
as the percentage of larvae in which E. maimaiga was found out of the total
larvae examined (i.e. total number of cadavers processed plus the number of
larvae that survived to pupation) for each 4-d period. Larval cadavers found to
be co-infected with E. maimaiga and NPV were counted as mortality caused by
E. maimaiga because of the more rapid pathogenesis from E. maimaiga than
NPV (Hajek 1997a, Malakar et al. 1999a, 1999b). Co-infection with NPV
occurred in 92.2 and 44.6% of E. maimaiga-killed cadavers in 2001 and 2002,
respectively. Differences in overall infection levels between northern and
southern aspects for all sites combined were analyzed using two-sample t-tests

(SYSTAT 2000).

61

Microclimatic data

Several microclimatic variables at each site were collected every hour
with on-site weather collection equipment, including air and soil temperatures,
relative humidity, soil moisture and precipitation (Campbell Scientific, lnc.,
Logan, Utah). Weather collection equipment was positioned 40 - 50 m from the
plot center at the base of a representative dominant oak tree. Air temperature
and relative humidity data were collected with a temperature and relative
humidity probe (Campbell Scientific, lnc., Logan, Utah) in a solar radiation shield
positioned 1.5 m above the ground surface on a rebar pole. Soil temperatures
and moisture levels were collected with temperature probes and water content
reflectometers (Campbell Scientific, lnc., Logan, Utah), respectively.
Temperature probes and water content reflectometers were positioned within
the upper 3 to 4 cm of soil where the highest densities of E. maimaiga resting
spores occur (Hajek et al. 19983), to record relevant microclimatic conditions
experienced by the fungus. Hourly precipitation measurements used in
analyses were collected with a data-logging, tip-bucket rain gauge (Onset
Computer Corporation, Bourne, Massachusetts) each year at the Jackson
Corners site. Total precipitation during the 4-d bioassay periods, recorded using
rain gauges (All-Weather Rain Gauge, Productive Alternatives, lnc., Fergus
Falls, Minnesota) at each site, were similar over the study area.

Relative humidity was not used in analyses because of its nonlinear
dependence on atmospheric temperature (Rosenberg et al. 1983). However,

actual atmospheric water vapor pressure at each site was used in analyses and

62

was calculated using the respective on-site air temperature and relative humidity
data, as:
95, = RH x es/ 100,

where 98 = actual atmospheric water vapor pressure (kPa), RH = relative
humidity (%), and eS = saturation water vapor pressure (Rosenberg et al. 1983).
Saturation water vapor pressure was calculated as:

198 = 0.61078 exp [(17.269 x T) / (T+ 23730)],
where T = air temperature (°C) (Rosenberg et al. 1983).

Weather data were collected throughout the 6-wk period of gypsy moth
field bioassays. In 2001, weather data were recorded for six weeks during
gypsy moth larval development from 1200 hrs, 25 May to 1200 hrs, 4 July. In
2002, weather data were recorded from 1200 hrs, 24 May to 1200 hrs, 3 July, at
each site. Microclimatic measurements were collected every hour for the
duration of the study period.

In 2002, weather data was collected at only two of the sites due to an
equipment malfunction at the Bitely site. Simple linear regression analyses
were conducted using microclimatic variables in 2001 to develop equations to
estimate microclimatic conditions for the Bitely site in 2002. Relationships
between the three field sites and an independent weather station (Freemont
station; MAWN 2003) were evaluated to determine which site most closely
approximated microclimatic conditions at the Bitely field site. The equations
used to approximate microclimatic conditions (followed the coefficient of

determination) at Bitely in 2002 were: air temperature = 0.994 x (Freemont air

63

temperature) - 0.834 (r2 = 0.96), relative humidity = 1.004 x (Freemont relative
humidity ) + 4.372 (r2 = 0.87), northern aspect soil temperature = 1.083 x
(Jackson Corner northern aspect soil temperature) - 0.616 (r2 = 0.99), southern
aspect soil temperature = 0.999 x (Jackson Corner southern aspect soil
temperature) - 0.533 (r2 = 0.96), northern aspect soil moisture = 1.078 x (Lilley
northern aspect soil moisture) - 0.049 (r2 = 0.78), and southern aspect soil
moisture = 1.117 x (Lilley southern aspect soil moisture) - 0.030 (r2 = 0.89). All
linear regression relationships were significant at P < 0.001.

A backward-stepping multiple regression analysis was used to analyze
effects of microclimatic variables on the levels of E. maimaiga and NPV infection
for all sites combined each year (SYSTAT 2000). Because germination of E.
maimaiga resting spores is greatest from 15 to 25 °C (Shimazu and Soper
1986, Hajek et al. 1990a, Hajek and Shimazu 1996), the sum of the hours that
air and soil temperatures were between 15 and 25 °C were used in analyses.
Other microclimatic variables used in analyses included the sum of the hours
that volumetric soil moisture levels exceeded 10%, sum of the hours that
precipitation occurred, total precipitation, and average atmospheric water vapor
pressure over a given 4-d bioassay period. To reduce effects of
multicollinearity, values for soil temperature and soil moisture were averaged
between northern and southern aspects for each site over a given 4-d bioassay

penod.

64

RESULTS

Field bioassays

In 2001 and 2002, 4,800 laboratory-reared gypsy moth larvae were used
in the field bioassays (total of 9,600 larvae). A portion of the larvae did not
survive the 4-day exposure to field conditions each year (21.4% in 2001 and
12.1% in 2002), typically due to predation by ants or insectivorous rodents.

Overall, pathogen infection levels on northern and southern aspects were
not significantly different in either year. In 2001, total E. maimaiga infection
levels were 2.9 4.- 0.5% on northern aspects and 2.6 :l: 0.6% on southern aspects
(t = 0.273, df = 18, P = 0.79), while total NPV infection levels were 41.7 :t 6.8%
and 42.4 :l: 6.3% on northern and southern aspects, respectively (t = -0.082, df =
18, P = 0.94). Similarly, in 2002, total E. maimaiga infection levels were 5.1 1:
1.9% on northern aspects and 3.9 i 1.4% on southern aspects (t = 0.501, df =
18, P = 0.62), compared with total NPV infection levels of 14.3 :l: 2.1% and 15.0
:1: 3.0% on northern and southern aspects, respectively (t = -0.195, df = 18, P =

0.85).

Infection dynamics

In 2001, a total of 3,775 larvae were returned from the 4-d field
exposures and reared in the laboratory until death or pupation. For all sites, the
percentage of gypsy moth larvae infected with NPV was much greater than the

percentage infected with E. maimaiga, regardless of the sample period (Figure

65

2.1).

Cumulatively, fungal and viral infections were responsible for mortality of
1,686 larvae (44.7%) in 2001. NPV was the dominant pathogen present in
1,584 cadavers (42.0% of the total larvae processed; 94.0% of the pathogen-
killed cadavers). Infection by E. maimaiga was responsible for mortality of only
102 larvae (2.7% of the total larvae processed; 6.0% of the pathogen-killed
cadavers). There were 195 larval cadavers (5.1%) in which neither NPV or E.
maimaiga was present. Of the 3,775 larvae that were returned from the field,
1,894 gypsy moth larvae (52.2%) survived to pupation.

In 2002, 4,221 of the 4,800 larvae were returned from the 4-d field
exposures and reared in the laboratory until death or pupation. In general, the
percentage of gypsy moth larvae infected with NPV was again greater than the
percentage infected with E. maimaiga, regardless of the sample period (Figure
2.2). Two exceptions occurred; at the Bitely site, during the 5 June and 13 June
sample periods, the E. maimaiga infection rate was slightly greater than NPV
infections.

Fungal and viral infection rates were much lower in 2002 than 2001, with
815 total larvae (19.3%) succumbing to either NPV or E. maimaiga. While NPV
was again the dominant pathogen and was present in 622 cadavers (14.7% of
the total larvae processed; 76.3% of the pathogen-killed cadavers), infection by
E. maimaiga nearly doubled and was present in 193 cadavers (4.6% of the total
larvae processed; 23.7% of the pathogen-killed cadavers). There were 88 larval

cadavers (2.1%) in 2002 in which neither pathogen was present. Of the 4,221

66

larvae that were returned from the field, 3,318 gypsy moth larvae (78.6%)

survived to pupation.

Microclimatic conditions

Several microclimatic variables suspected to influence primary
transmission of E. maimaiga, including the sum of the hours that air and soil
temperatures were between 15 and 25 °C, the sum of the hours that volumetric
soil moisture levels were greater than 10%, sum of the hours that precipitation
occurred, total precipitation, and average atmospheric water vapor pressure
over a given 4-d bioassay period, were regressed on the percentage of gypsy
moth larvae infected with E. maimaiga and the percentage of gypsy moth larvae
infected with NPV in 2001 and 2002. While microclimatic conditions were
relatively similar among sites each year, differences in environmental conditions
between 4-d bioassay periods were considerable in 2001 (Figures 2.3 - 2.5) and
2002 (Figure 2.6 - 2.8).

In 2001, regression analysis of microclimatic variables on the percentage
of gypsy moth larvae infected with E. maimaiga was not significant (P = 0.091)
and the amount of variation explained was 22% (Table 2.2). However,
regression analysis of microclimate on the percentage of gypsy moth larvae
infected with NPV was significant (P < 0.05) and the amount of variation
explained was 50%. Important predictors of NPV infection included the sum of
the hours that air and soil temperatures were between 15 and 25 °C, total

precipitation, and average atmospheric water vapor pressure over a given 4-d

67

bioassay period. Predictor coefficients for air temperature and atmospheric
water vapor pressure were positive, indicating that NPV infection rates were
higher during sample periods that had more hours with the air temperature
between 15 and 25 °C and greater atmospheric water vapor pressure (Table
2.2).

In 2002, regression analysis of microclimatic variables on the percentage
of gypsy moth larvae infected with E. maimaiga was significant (P < 0.05) and
the amount of variation explained was 31% (Table 2.2). The sum of the hours
that precipitation occurred over a given 4-d bioassay period was an important
predictor of E. maimaiga infection. The predictor coefficient was positive,
indicating that E. maimaiga infection rates were higher during sample periods
that had precipitation occur over a longer period of time. Regression analysis of
microclimate on the percentage of gypsy moth larvae infected with NPV was
also significant (P < 0.05) and the amount of variation explained was 22%. The
sum of the hours that precipitation occurred and average atmospheric water
vapor pressure over a given 4-d bioassay period were important predictors of
NPV infection. Predictor coefficients were again positive, indicating that NPV
infections occurred more frequently when precipitation occurred for more hours

and there was greater atmospheric water vapor pressure (Table 2.2).

68

DISCUSSION

This study provided a unique opportunity to examine the activity of two
dominant gypsy moth pathogens, E. maimaiga and NPV, during primary
transmission in the development of disease epizootics under field conditions.
While the dynamics of these two pathogens are not identical, they do share
some common characteristics. Specifically, primary transmission of the
pathogen to early-instar hosts is the initial step in the development of an
epizootic. The next step in the development of an epizootic involves secondary
transmission of the pathogen from these infected early-instar hosts to later-
instar hosts. It is during secondary transmission when amplification of disease
takes place and the development of a large-scale epizootic may be realized
(Hajek and Roberts 1991, Weseloh and Andreadis 1992a, 1992b, Hajek et al.
1993, Hajek 1997a). Gypsy moth populations at the three field sites were low
since 1999 (NW. Siegert chapter one) and wild gypsy moth larvae were rarely
observed in these sites during this study in 2001 and 2002. This reduced the
possibility of secondary transmission occurring, enabling me to focus on primary
transmission of these pathogens over the course of gypsy moth larval
development under field conditions.

Both E. maimaiga and NPV were common mortality agents in 2001 and
2002, though pathogen-related mortality of gypsy moth larvae was dominated
by NPV. Until 1989, when E. maimaiga was discovered to be causing

epizootics in the northeastern United States (Andreadis and Weseloh 1990a,

69

1990b, Hajek et al. 1990b), the major gypsy moth pathogen in North America
was NPV (Doane 1970, Campbell and Podgwaite 1971). NPV remained the
dominant gypsy moth pathogen in Michigan until E. maimaiga was introduced in
the early to mid-1990’s (Smitley et al. 1995, Buss 1997). Although NPV
epizootics can be variable in nature, NPV is generally considered to function as
a density-dependent mortality factor (Doane 1970, Woods and Elkinton 1987).
Sufficient NPV inoculum generally must accumulate before a viral epizootic may
occur. Since gypsy moth populations at the field sites were low in 2000 and
2001, I expected that infection rates by NPV during field bioassays would be
low. However, NPV was the dominant pathogen during the 6-wk bioassays in
both years, suggesting that NPV inoculum at the sites had persisted since 1999
in the soil and remained in large enough titers to potentially initiate a viral
epizootic had high densities of gypsy moth been present. NPV of European
pine sawfly (Neodiprion sertifer Geoffroy; Hymenoptera: Diprionidae) is known
to persist in the soil at least 13 years (Olofsson 1988). Another possible
explanation for the high levels of NPV infection, though, may be that the
laboratory-reared larvae had latent viral infections that became activated during
the 4-d field exposures. Previous research has suggested that stressful
physical, chemical or physiological conditions may induce latent viral infections
in some insects (Troitskaya and Chichigina 1980, Petre and Fuhrmann 1981,
Hughes et al. 1993, Stoltz and Makkay 2003), but not others (Olofsson 1989).
Reduction in temperature alone activated latent NPV infections in a Lymantria

spp. closely related to gypsy moth (Bakhvalov et al. 1979). Further studies are

70

needed, however, to elucidate whether or not latency may have been
responsible for the high levels of viral infection observed in this study.

Although several factors have been suggested to affect secondary
transmission of NPV, and therefore the ultimate development of a viral epizootic
(see D’Amico et al. 1996), precipitation or other forms of environmental moisture
most likely drive viral infections, through contamination of food resources during
primary transmission (Podgwaite et al. 1979, D’Amico and Elkinton 1995). In
this study, infections of gypsy moth larvae with NPV were often associated with
precipitation-related factors. Whether these factors are casual agents of viral
infection or are merely correlated with precipitation-based contamination of food
resources, however, remains to be tested.

Entomophaga maimaiga activity has often been associated with moisture
(see Hajek 1999 and references therein), but few studies have evaluated the
germination of resting spores during primary transmission. In a study of a
related entomopathogen, Perry and Latge (1982) found that free water was
required for the germination of Conidiobolus obscurus (Petch) Hall & Dunn
resting spores. Results from this study appear to further verify that infection by
E. maimaiga is associated with environmental moisture. However, results were
inconsistent between years and, in 2001, results of the multiple regression were
not significant. Whether this was due to some unmeasured abiotic or biotic
factor that affects E. maimaiga infection dynamics or is an artifact of
stochasticity in resting spore germination between years, remains to be

determined. In 2002, however, the sum of the hours that precipitation occurred

71

over a given 4-d bioassay period was an important predictor of E. maimaiga
infection during primary transmission. This suggests that the primary
transmission of E. maimaiga, and therefore the initial step in the development of
epizootics, may be affected by the availability of free water.

While E. maimaiga infections were generally lower than NPV infections in
this study, the infection levels I observed are not unreasonably low for primary
transmission of this pathogen. Hajek et al. (1993) hypothesized that secondary
transmission of E. maimaiga in gypsy moth populations was more critical in the
ultimate development of epizootics. Indeed, several studies examining infection
by E. maimaiga during gypsy moth larval development have documented low
levels of infection during primary transmission of the pathogen comparable to
levels observed in this study, followed by a rapid increase in E. maimaiga
infection presumably due to secondary transmission (e.g. Weseloh and
Andreadis 1992b, Hajek et al. 1996a, Hajek and Webb 1999, Webb et al. 1999).
In a forest that had an E. maimaiga epizootic in the previous year in
Connecticut, Weseloh and Andreadis (1992b) reported that 5% or less of forest-
collected 1St to 3’d-instar gypsy moth larvae became infected in the first few
weeks of larval development. Additionally, 2nd to 4‘“-instar laboratory-reared
gypsy moth larvae that were caged and exposed 5 cm above the ground for 3-d
periods had even lower infection rates until late 4th and Sm-instar larvae were
present in the forest (Weseloh and Andreadis 1992b). In 5 out of 7 plots In
Virginia, Hajek et al. (19963) also found that 5% or less of forest-collected

larvae became infected with E. maimaiga during the first six weeks of larval

72

development followed by a rapid increase in E. maimaiga infection levels. The
remaining two plots exhibited a similar pattern of epizootic development, but E.
maimaiga infections during the first several weeks of larval development were
slightly greater (ca 5 to 15%) (Hajek et al. 1996a). Webb et al. (1999)
documented similar results in forest-collected larvae from five “higher-
population” woodlots in Virginia. It seems reasonable to suspect that the levels
of E. maimaiga infection I observed during primary transmission would have
been more than adequate for the development of a large-scale epizootic had
higher-density gypsy moth populations been present and microclimatic
conditions, such as environmental moisture (Hajek et al. 1999), been favorable
for efficient secondary transmission.

Entomophaga maimaiga resting spores were quantified at these sites the
two years before the current study in related research (N.W. Siegert chapter
one). These sites averaged ca 169 i 36 and 90 i 18 resting spores per gram of
dry soil in 1999 and 2000, respectively, but resting spore counts tended to be
highly variable (N.W. Siegert chapter one). In 1999 and 2000, E. maimaiga
resting spore counts at the Jackson Corners site were 235 :t 85 and 69 i 15
resting spores per gram of dry soil, respectively. Resting spore counts at the
Lilley site were 176 :t 58 and 108 :t 73 resting spores per gram of dry soil in
1999 and 2000, respectively. The Bitely site had the least variable counts with
97 :I: 11 and 95 :l: 15 resting spores per gram of dry soil in 1999 and 2000,
respectively. Although only 112 to 235 E. maimaiga resting spores per gram of

dry soil can cause more than 60% mortality in laboratory bioassays under

73

optimal conditions (N.W. Siegert chapter one), it is likely that greater fungal
inoculum in the soil would increase E. maimaiga infection levels during primary
transmission (Weseloh and Andreadis 1992a, Hajek and Webb 1999).
Prediction of naturally-occurring epizootics and their effects on gypsy
moth populations has become more complicated because both E. maimaiga
and NPV must be considered. Evaluation of hourly microclimatic conditions
during larval development in stands with varying gypsy moth population
densities would improve our understanding of the primary and secondary
transmission dynamics of E. maimaiga and NPV. This information, in addition to
a better understanding of the interaction of these two pathogens under varying
climatic conditions, could significantly aid in the development of our ability to

accurately predict epizootics in North American gypsy moth populations.

74

LITERATURE CITED

Andreadis, T.G. 1987. Transmission. Pages 159-176 in Epizootiology of
Insect Diseases, edited by JR. Fuxa and Y. Tanada. John Wiley 8
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Andreadis, T.G. and RM. Weseloh. 1990a. Discovery of Entomophaga
maimaiga in North American gypsy moth, Lymantria dispar.
Proceedings of the National Academy of Sciences. 87: 2461-2465.

Andreadis, T.G. and RM. Weseloh. 1990b. Fungus from Japan causes heavy
mortality of gypsy moth. Frontiers of Plant Science. Pp. 2-3.

Bakhvalov, S.A., G.V. Larionov, V.N. Zhimerikin, and CA. Chernyavskaya.
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Benz, G. 1987. Environment. Pages 177-214 in Epizootiology of Insect
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Buss, L.J. 1997. Evaluation of three egg mass survey methods and two
biological control agents for gypsy moth management. M.S. thesis,
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Campbell, R.W. and J .D. Podgwaite. 1971. The disease complex of the gypsy
moth. l. Major components. Journal of Invertebrate Pathology. 18:
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D’Amico, V. and J.S. Elkinton. 1995. Rainfall effects on transmission of gypsy
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Environmental Entomology. 24: 1144-1149.

D’Amico, V., J.S. Elkinton, G. Dwyer, J.P. Burand. and JP. Buonaccorsi. 1996.
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Doane, CC. 1970. Primary pathogens and their role in the development of an

epizootic in the gypsy moth. Journal of Invertebrate Pathology. 15:
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Doane, CC. 1969. Trans-ovum transmission of a nuclear-polyhedrosis virus in
the gypsy moth and inducement of virus susceptibility. Journal of
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Forbush, E.H. and CH. Fernald. 1896. The Gypsy Moth. Wright and Potter
Printing 00., Boston. 595 pp.

Fuxa, JR. and Y. Tanada. 1987. Epidemiological concepts applied to insect
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Glaser, R.W. 1915. Wilt of gypsy-moth caterpillars. Journal of Agricultural
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Hajek, A.E. 2001. Larval behavior in Lymantria dispar increases risk of fungal
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Hajek, A.E. 1999. Pathology and epizootiology of Entomophaga maimaiga
infections in forest Lepidoptera. Microbiology and Molecular Biology
Reviews. 63: 814-835.

Hajek, A.E. 1997a. Fungal and viral epizootics in gypsy moth (Lepidoptera:
Lymantriidae) populations in central New York. Biological Control. 10:
58-68.

Hajek, A.E. 1997b. Ecology of terrestrial fungal entomopathogens. Pages 193-
249 in Advances in Microbial Ecology, Volume 15, edited by J.
Gwynfryn Jones. Plenum Press, New York. 374 pp.

Hajek, A.E. and CC. Eastburn. 2001. Effect of host insects on activation of
Entomophaga maimaiga resting spores. Journal of Invertebrate
Pathology. 77: 290-291.

Hajek, A.E. and RE. Webb. 1999. lnoculative augmentation of the fungal
entomopathogen Entomophaga maimaiga as a homeowner tactic to

control gypsy moth (Lepidoptera: Lymantriidae). Biological Control.
14: 11-18.

Hajek, A.E. and RA. Humber. 1997. Formation and germination of

Entomophaga maimaiga azygospores. Canadian Journal of Botany.
75: 1739-1747.

Hajek, A.E. and M. Shimazu. 1996. Types of spores produced by

Entomophaga maimaiga infecting the gypsy moth L ymantria dispar.
Canadian Journal of Botany. 74: 708-715.

76

Hajek, A.E. and D.W. Roberts. 1992. Field diagnosis of gypsy moth
(Lepidoptera: Lymantriidae) larval mortality caused by Entomophaga
maimaiga and the gypsy moth nuclear polyhedrosis virus.
Environmental Entomology. 21: 706-713.

Hajek, A.E. and D.W. Roberts. 1991. Pathogen reservoirs as a biological
control resource: Introduction of Entomophaga maimaiga to North
American gypsy moth, Lymantria dispar, populations. Biological
Control. 1: 29-34.

Hajek, A.E. and RS. Soper. 1991. Within-tree location of gypsy moth,
Lymantria dispar, larvae killed by Entomophaga maimaiga (Zygomycetes:
Entomophthorales). Journal of Invertebrate Pathology. 58: 468-469.

Hajek, A.E., L. Butler, J.K. Liebherr, and MM. Wheeler. 2000. Risk of infection
by the fungal pathogen Entomophaga maimaiga among Lepidoptera on
the forest floor. Environmental Entomology. 29(3): 645-650.

Hajek, A.E., C.H. Olsen, and J.S. Elkinton. 1999. Dynamics of airborne conidia
of the gypsy moth (Lepidoptera: Lymantriidae) fungal pathogen
Entomophaga maimaiga (Zygomycetes: Entomophthorales). Biological
Control. 16: 111-117.

Hajek, A.E., L. Bauer, M.L. McManus, and MM. Wheeler. 1998a. Distribution
of resting spores of the Lymantria dispar pathogen Entomophaga
maimaiga in soil and on bark. BioControl. 43: 189-200.

Hajek, A.E., K.M. Tatman, P.H. Wanner, and MM. Wheeler. 1998b. Location
and persistence of cadavers of gypsy moth, Lymantria dispar, containing
Entomophaga maimaiga azygospores. Mycologia. 90: 754-760.

Hajek, A.E., L. Butler, S.R.A. Walsh, J.C. Silver, F.P. Hain, F.L. Hastings, T.M.
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(Lepidoptera: Lymantriidae) pathogen Entomophaga maimaiga
(Zygomycetes: Entomophthorales) in the field versus laboratory.
Environmental Entomology. 25: 709-721.

Hajek, A.E., J.S. Elkinton, and J.J. Witcosky. 1996b. Introduction and spread of
the fungal pathogen Entomophaga maimaiga (Zygomycetes:
Entomophthorales) along the leading edge of gypsy moth (Lepidoptera:
Lymantriidae) spread. Environmental Entomology. 25: 1235-1247.

Hajek, A.E., L. Butler, and MM. Wheeler. 1995a. Laboratory bioassays testing

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77

Hajek, A.E., R.A. Humber, and J.S. Elkinton. 1995b. Mysterious origin of

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Hajek, A.E., T.S. Larkin, R.l. Carruthers, and RS. Soper. 1993. Modeling the
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Hajek, A.E., R.A. Humber, J.S. Elkinton, R. May, S.R.A. Walsh, and J.C. Silver.
1990b. Allozyme and restriction fragment length polymorphism analyses
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78

MAWN (Michigan Automated Weather Network, Michigan State University).
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PD-

79

Shimazu, M. and RS. Soper. 1986. Pathogenicity and sporulation of
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Vandenberg, JD. 1990. Safety of four entomopathogens for caged adult honey
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Webb, R.E., G.B. White, K.W. Thorpe, and SE. Talley. 1999. Quantitative
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Weseloh, RM. and T.G. Andreadis. 1997. Persistence of resting spores of

Entomophaga maimaiga, a fungal pathogen of the gypsy moth, Lymantria
dispar. Journal of Invertebrate Pathology. 69: 195-196.

80

Weseloh, RM. and T.G. Andreadis. 1992a. Mechanisms of transmission of the
gypsy moth (Lepidoptera: Lymantriidae) fungus, Entomophaga maimaiga
(Zygomycetes: Entomophthoraceae) and effects of site conditions on its
prevalence. Environmental Entomology. 21: 901-906.

Weseloh, RM. and T.G. Andreadis. 1992b. Epizootiology of the fungus
Entomophaga maimaiga, and its impact on gypsy moth populations.
Journal of Invertebrate Pathology. 59: 133-141.

Woods, SA. and J.S. Elkinton. 1987. Bimodal patterns of mortality from

nuclear polyhedrosis virus in gypsy moth (Lymantria dispar) populations.
Journal of Invertebrate Pathology. 50: 151-157.

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Figure 2.5. Microclimatic conditions that occurred at the Lilley site during the 6-
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soil temperature = 0.832 x (southern aspect soil temperature) + 2.583; r2 =
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hourly air and soil temperatures (°C), B) soil moisture (%) and precipitation
(mm), and C) actual water vapor pressure (kPa). Soil temperatures and
moistures shown were collected from southern aspects. Northern aspects were
slightly lower in temperature (northern aspect soil temperature = 0.705 x
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(rglorthern aspect soil moisture = 1.558 x (southern aspect soil moisture) - 0.044;
= 0.84).

91

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Figure 2.8. Microclimatic conditions that occurred at the Lilley site during the 6-
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soil temperatures (°C), B) soil moisture (%), and C) actual water vapor pressure
(kPa). Soil temperatures and moistures shown were collected from southern
aspects. Northern aspects were slightly lower in temperature (northern aspect
soil temperature = 0.787 x (southern aspect soil temperature) + 2.521; r2 =
0.91) and higher in moisture (northern aspect soil moisture = 1.175 x (southern
aspect soil moisture) - 0.005; r2 = 0.90).

92

CHAPTER 3

ASSESSING THE CLIMATIC POTENTIAL FOR EPIZOOTICS OF THE
GYPSY MOTH FUNGAL PATHOGEN Entomophaga maimaiga

IN THE NORTH CENTRAL UNITED STATES

INTRODUCTION

Substantial decreases in gypsy moth Lymantria dispar L. (Lepidoptera:
Lymantriidae) defoliation in the northeastern United States in the last decade
have been largely attributed to the occurrence of epizootics of the fungal
pathogen Entomophaga maimaiga Humber, Shimazu et Soper (Zygomycetes:
Entomophthorales). Originally from Japan, E. maimaiga epizootics in North
America were first observed in 1989 (Andreadis and Weseloh 19903, 1990b,
Hajek et al. 1990b). Entomophaga maimaiga is a desirable biological control
agent for gypsy moth. It has few impacts on non-target organisms (Hajek et al.
1995a, 1996a, 1996b, 2000) and is compatible with other natural enemies and
pathogens, including a nuclear polyhedrosis virus (NPV) (Andreadis and
Weseloh 1990a, Hajek and Roberts 1992, Weseloh and Andreadis 1992b).
Unlike NPV (Doane 1970, Leonard 1981, Woods and Elkinton 1987), E.
maimaiga functions in a density-independent manner (Hajek et al. 1990b, Hajek
1997a), so it is not necessary for gypsy moth populations to build to damaging

levels before an epizootic may develop.

93

Entomophaga maimaiga produces two types of spores, both of which
may infect gypsy moth larvae (Hajek 1999). Soil-borne E. maimaiga resting
spores germinate in the spring when environmental conditions are suitable
(Hajek 1997b, Hajek and Humber 1997, Weseloh and Andreadis 1997). Early-
instar larvae become infected and die (Hajek et al. 1998b). These infected
cadavers externally produce E. maimaiga conidiophores that discharge conidia
which may infect mid- to Iate-instar gypsy moth. Late-instar cadavers drop to
the soil, decompose and resting spores remain dormant in the soil until the
following spring (Hajek et al. 1998a).

Entomophaga maimaiga has rapidly become a significant biological
control agent for gypsy moth (Elkinton et al. 1991, Hajek et al. 1995b, Hajek
1999, Nealis et al. 1999) and has been widely introduced throughout the present
North American range of gypsy moth (Smitley et al. 1995, Hajek et al. 1996b,
Webb et al. 1999). Despite these widespread introductions and establishment
of E. maimaiga in northeastern states, the occurrence of epizootics have been
less consistent in other states, including Michigan (Smitley et al. 1995, Bauer
and Smitley 1996, Buss 1997, Buss et al. 1999). Infrequency of epizootics in
these areas has contributed to continued gypsy moth defoliation (USDA-F8
2004). This lack of consistency could be weather related and may be
attributable to variation in spring climate.

Questions remain concerning how E. maimaiga will perform in the North
Central states of Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota,

Missouri, Ohio and Wisconsin, where gypsy moth is more recently established

94

and continuing to expand into new areas. Forest health managers and pest
specialists, especially in areas with high densities of suitable gypsy moth hosts
near the leading edge of gypsy moth range expansion, such as Minnesota and
Wisconsin, desire the ability to predict E. maimaiga success and the extent to
which it should be incorporated into gypsy moth management strategies.
Thorough evaluation of meteorological factors and their effect on E. maimaiga
germination, however, is integral to predict how well this fungal pathogen will
control gypsy moth in new areas.

Fungal entomopathogens can be highly efficacious (Carruthers and
Soper 1987, McCoy et al. 1988, Hajek 1997b), but generally tend to be effective
within a narrow range of environmental conditions (Benz 1987, Hajek and St.
Leger 1994, Burges 1998). Previous studies have suggested that the E.
maimaiga fungus is sensitive to abiotic conditions, particularly temperature and
moisture (Shimazu and Soper 1986, Shimazu 1987, Hajek et al. 1990a, 1999,
Weseloh et al. 1993, Hajek and Humber 1997) and that weather plays a critical
role in the development of E. maimaiga epizootics (Elkinton et al. 1991,
Weseloh and Andreadis 19923, Hajek et al. 1993). Weather between April and
June is likely to be critical because gypsy moth larvae are present during that
time and certain unknown environmental conditions are needed for germination
of E. maimaiga resting spores (Hajek and Roberts 1991). The variability of
weather during the April to June period may be more important than average
meteorological conditions in the long-term success of E. maimaiga.

The goal of this research was to assess the climatic conditions of the

95

North Central region and identify areas that are likely to frequently experience
conditions suitable for the development of E. maimaiga epizootics. The
objectives of the current study were to (1) compare year-specific weather from
locations with documented E. maimaiga epizootics to the climate of the North
Central region; (2) examine the temporal variability of precipitation and
temperature in the North Central region to identify areas which should be
climatically conducive for the development of E. maimaiga epizootics; and (3)
estimate the number of years in the North Central region from 1971 to 2000, in
which precipitation and temperature conditions may have been suitable for E.
maimaiga epizootics. The response of E. maimaiga to general climate patterns
remains unclear (Hajek 1999), so climate comparisons and climatic variability in
the North Central region were examined using spring (April through June) and

annual climate data.

96

METHODS AND MATERIALS

Documented epizootics

Environmental conditions specific to 11 documented E. maimaiga
epizootics (Table 3.1) were used for climate comparisons and to estimate the
number of years that may have been favorable for an epizootic to occur in the
North Central region. Weather conditions during the specific years that the
epizootics occurred were retrieved from the Midwestern, Southeastern and
Northeastern Regional Climate Centers for weather stations nearest to the
documented epizootics. These 11 epizootic locations with their respective year-
specific climate data will be referred to herein as the “epizootic—specific sites.”
Locations were selected from the scientific literature if the level of E. maimaiga
infection was greater than 60% or if it could otherwise be discerned that a large-
scale epizootic had occurred. A few additional E. maimaiga epizootics have
been documented in the literature (e.g. Hajek 1997a, Hajek and Humber 1997,
Hajek et al. 1990b, 1999), but they were not included in the analyses because I
was unable to obtain complete year-specific climate data for the respective

Iocafions.

Climate comparisons
Environmental conditions at the epizootic-specific sites (Table 3.1) were
individually compared with 1132 locations in North America using the

climatological software program CLIMEX for Windows Version 1.1

97

(Commonwealth Scientific and Industrial Research Organization [CSIRO]
Publishing, Victoria, Australia) (Sutherst et al. 1999). Models in CLIMEX
assume that temperature and moisture are primary determinants in a species’
biogeography (Sutherst and Maywald 1985, Sutherst et al. 1995). The CLIMEX
software contains a meteorological database of approximately 3000 locations
worldwide and 300 locations in North America (Appendix C). Meteorological
data from an additional 832 locations in nine North Central and two northeastern
states were imported into the standard CLIMEX meteorological database to
more thoroughly represent climatic variability within the region (Figure 3.1 ).
Specifically, meteorological data from 77 locations in Illinois, 58 locations in
Indiana, 113 locations in Iowa, 43 locations in Kentucky, 88 locations in
Michigan, 100 locations in Minnesota, 79 locations in Missouri, 39 locations in
New York, 84 locations in Ohio, 32 locations in Pennsylvania and 119 locations
in Wisconsin were added to the database (Appendix C; MRCC 2002). The
meteorological database is composed of 30-yr average monthly minimum and
maximum air temperature, precipitation, and morning and afternoon relative
humidity.

Climatic comparisons were based on minimum and maximum air
temperature, total precipitation and precipitation pattern. Relative humidity data
were not available for the additional locations imported into the CLIMEX
database, so this parameter was excluded from the climate-matching analyses.
The CLIMEX program expressed similarity between two locations as an index

for each climatic parameter. Indices were scaled between 0 and 100, with

98

higher values reflecting greater similarity in a given climatic parameter between
the two locations. The maximum similarity of a North American location to any
one of the epizootic-specific sites was used in the climate-matching analyses.

The minimum and maximum temperature indices, Itmm and (max, were
calculated by CLIMEX as:

[mm = exp(-kT*Tdm,,,), and
Itmax = 9XP('kT*Tdmax)

where, Tam," and Tdmax are the average monthly absolute differences in minimum
and maximum temperature, respectively, between two locations. By default, the
constant, k7, was set to 0.1.

CLIMEX calculated the total precipitation index, In“, as:

Ina: = 9XP(‘kR*Rd)

where, Rd = (er - RMI)/[1 + a(RT + RM)]. RT was the annual precipitation at the
target location, and RM was the annual precipitation at the matching location.
The CLIMEX software used default values of 0.001 and 0.004 for the constants
a and kR, respectively.

The precipitation pattern index, Imm, was calculated as:

lrpat = 9Xp(’kP*RD)

where, R0 was the average absolute difference between the monthly
precipitation of the target and matching locations, after the monthly precipitation
at the matching location was multiplied by R7. (R2 = Ry/RT). CLIMEX used a
value of 0.005 for the precipitation pattern constant, kp.

An overall measure of climatic similarity was estimated by the CLIMEX

99

software as a “Match Index,” which incorporated all of the equally-weighted
indices, excluding the relative humidity index, and was scaled between 0 and
100, inclusively (Sutherst and Maywald 1985). The Match Index, MI, was
calculated as:
MI = (I, x 1,0, x Imago-5 x 100

where, I, was the average monthly temperature.

The geographically-referenced overall climatic similarity index of a given
North American location with maximum similarity to any one of the epizootic-
specific sites was exported to the ArcView 3.2 geographic information system
(Environmental Systems Research Institute [ESRI], Redlands, California).

Isoclines were generated using the ArcView Spatial Analyst extension (ESRI).

Climatic deviations from 30-yr averages

Climatic variability in the North Central region was examined by
evaluating the sum of the absolute departures from 30-yr averages (1971-2000)
of precipitation and temperature for climate divisions in the North Central region
(Figure 3.2; MRCC 2002). A climate division is a climatically homogeneous
region within a state (NOAA 2002a, 2002b). Divisional climate data are used for
numerous research applications, including assessment of large-scale climatic
trends over long time periods (NOAA 2002a, 2002b).

Trends in variation of precipitation and temperature were examined for
both spring (April through June) and annual climate data. Geographically-

referenced absolute deviations for each climatic parameter were exported to

100

ArcView 3.2 and isoclines generated using the Spatial Analyst extension (ESRI).

Favorable years for epizootics in the North Central region, 1971-2000

Precipitation and temperature conditions during April, May and June at
the epizootic-specific sites used for the climate comparisons (Table 3.1) were
examined to determine the approximate range of environmental conditions that
were associated with E. maimaiga epizootics. One outlier, June precipitation
from Rockbridge County, Virginia in 1995 (Webb et al. 1999), was excluded
from calculations because it was 328 mm greater than the 30-yr average for that
site and skewed the distribution of the data.

Weather records for the North Central region from 1971 to 2000 (MRCC
2002) were assessed to estimate the number of years that may have been
favorable for an E. maimaiga epizootic to occur. Meteorological records from
the nearest available weather station to an epizootic were used to estimate an
approximate value of a given climatic parameter. This exercise was
hypothetical because E. maimaiga and gypsy moth were not established
throughout the area investigated from 1971 to 2000. When estimating the
number of years between 1971 and 2000 that may have been favorable for an
E. maimaiga epizootic to occur, I assumed that E. maimaiga was initially present
throughout the North Central region, that no varietal effects of fungal isolates
occurred and that suitable gypsy moth hosts were sufficiently available for
infection.

“Average scenarios” and “best-case scenarios” were estimated based on

101

the weather conditions at the documented epizootic locations for the years that
epizootics occurred. A year was considered favorable in an average scenario if
weather conditions met or exceeded the average for a given meteorological
parameter estimated from the 11 documented epizootics. Average scenario
estimates were 75.4, 127.5 and 116.9 mm of precipitation and temperatures of
10.6, 15.6 and 20.5 °C for April, May and June, respectively. Best-case
scenarios were estimated in a similar manner using the average minus the
standard deviation for a given meteorological parameter. Best-case scenario
estimates were 43.0, 65.9 and 75.8 mm of precipitation and temperatures of 7.7,
13.8 and 18.7 °C for April, May and June, respectively. Scenarios were
estimated based on precipitation only, temperature only, and both precipitation
and temperature.

Departures from 30-yr average climatic conditions for the epizootic-
specific sites were also examined (Table 3.1). Monthly precipitation and
temperature values at locations where epizootics were documented to occur
were tested for differences from 30-yr averages using two-tailed t-tests (Sokal
and Rohlf 1995), with critical values of t(o,5_ 20) = 2.086 and 120.5. 9) = 2.262 for
temperature and precipitation differences, respectively. The degrees of freedom
for precipitation t-tests were reduced, as recommended by Sokal and Rohlf
(1995) when sizes of the two samples are equal, because the precipitation data

were heteroscedastic.

102

RESULTS

Documented epizootics

While precipitation was the meteorological parameter that most clearly
varied from the 30-yr averages at the epizootic-specific sites (Table 3.1), precise
upper and lower thresholds governing the development of epizootics remain
unclear. Most of the sites had above average precipitation in May and June
during years with epizootics, however, seven of the 11 epizootic-specific sites
had below average precipitation in April. May precipitation was below average
in three cases, while June precipitation was below average in only two cases
(Table 3.1). Precipitation in April at the epizootic-specific sites ranged from 36
to 141 mm and April temperature ranged from 4.6 to 13.8 °C. May precipitation
and temperature ranged from 52 to 242 mm and 12.8 to 17.9 °C, respectively.
Precipitation and temperature in June at the epizootic-specific sites ranged from
42 to 432 mm and 16.8 to 22.8 °C, respectively. Overall, precipitation in April
was not significantly different from 30-yr averages (P > 0.05), but precipitation in
May and June were significantly different from 30-yr averages (P < 0.05).
Temperatures at the epizootic-specific sites were not as variable as precipitation
and, overall, departures in temperature did not differ significantly in April, May or

June (P > 0.05).

Climate comparisons

Spring climatic conditions throughout most of the United States were

103

greater than 60% similar to any one of the epizootic-specific sites (Figure 3.3A).
A relatively small area south of the Great Lakes region that extended from
Kansas east to the Atlantic coast was greater than 80% similar in overall
climate. Annual climatic conditions throughout much of the eastern half of the
United States were 60 to 80% similar to any one of the epizootic-specific sites
(Figure 3.3B). For spring climate comparisons (April to June), individual climatic
similarity indices (i.e. similarity indices based solely on minimum air
temperature, maximum air temperature, total precipitation or precipitation
pattern) throughout the North Central region were typically greater than 80%

similar to any one of the epizootic-specific sites.

Climatic deviations from 30-yr averages

Deviations in spring precipitation in the North Central region were
greatest in the southwestern area of the region, extending from Iowa through
Kentucky (Figure 3.4A). Northern Minnesota, northern Wisconsin, most of
Michigan and northern Ohio were least variable in precipitation. Annual
deviations in precipitation in the North Central region were greatest through
southern Missouri, southern Illinois and western Kentucky (Figure 3.48). The
northern states, including Minnesota, northern Wisconsin, much of Michigan and
the northern edge of Ohio, were least variable in precipitation over the 30 year
time period, 1971 to 2000.

Deviations in spring temperature in the North Central region were

greatest throughout most of Minnesota (except northeastern Minnesota),

104

Wisconsin (except for southern Wisconsin) and Michigan (except for the
southeastern lower peninsula) (Figure 3.5A). Most of Ohio and southern areas
of the North Central region were less variable in temperature. Overall, annual
deviations in temperature in the North Central region were greatest in northern
Iowa and Minnesota (Figure 3.5B). The southern and eastern edges of the
North Central region, including most of the lower peninsula and the eastern half

of the upper peninsula of Michigan, were least variable in annual temperature.

Favorable years for epizootics in the North Central region, 1971-2000

The spring climate experienced at the epizootic-specific sites was
examined and compared to the 30-yr average of precipitation and temperature
for each of those locations (Table 3.1). Mean departures from 30-yr averages of
temperature were minimal, typically less than half a degree Celsius for April,
May or June. Total precipitation in May and June, however, tended to be much
greater than the 30-yr average (Table 3.1). Nearly 75% of the epizootic-specific
sites had May or June precipitation that exceeded the 30-yr average
precipitation by more than 40% and more than 90% of the epizootic-specific
sites had May or June precipitation that exceeded the 30-yr average
precipitation by more than 25%.

The average scenario estimates generally suggest that adequate
precipitation for E. maimaiga epizootics occurred in less than a third of the years
between 1971 and 2000 in the North Central region (Figure 3.6A—C). Large

areas of Minnesota, Wisconsin and Michigan received adequate precipitation for

105

an E. maimaiga epizootic in 6 or fewer years. The best-case scenario estimates
generally suggest that much of the North Central region received adequate
precipitation for E. maimaiga epizootics to occur in at least 19 of the 30 years
(Figure 3.6D-F). Portions of Minnesota, Wisconsin and Michigan, however, only
received adequate precipitation in 13-18 of the years between 1971 and 2000.

Based on temperature alone, the number of years from 1971 to 2000
estimated to be favorable for the development of E. maimaiga epizootics in the
North Central region exhibited a stronger geographic pattern than estimates
solely based on precipitation (Figure 3.7A—F). The average scenario estimates
generally suggest that only the southern portion of the North Central region
received adequate temperature for E. maimaiga epizootics to occur in 13 or
more years between 1971 and 2000 (Figure 3.7A-C). Temperatures in
Minnesota, Wisconsin and Michigan generally met or exceeded average
scenario estimates in 6 or fewer years from 1971 to 2000. The best-case
scenario estimates followed a similar, though less robust, trend (Figure 3.6D-F),
with northern portions of Minnesota, Wisconsin and Michigan reaching adequate
temperatures in 6 or fewer years between 1971 and 2000.

Based on both precipitation and temperature, the number of years from
1971 to 2000 estimated to be favorable for the development of E. maimaiga
epizootics in the North Central region also exhibited a strong geographic
pattern. Northern areas generally had fewer favorable years than southern
areas (Figure 3.8A-F). The average scenario estimates suggest that much of

the North Central region, except for the most southern portions, met or

106

exceeded adequate levels of precipitation and temperature in 6 or fewer years
over the 30-yr period (Figure 3.8A—C). The best-case scenario estimates were
not as conservative and much of the area had adequate climate in 13 or more
years. Only northern portions of Minnesota, Wisconsin and Michigan met or

exceeded adequate levels of precipitation and temperature in 6 or fewer years

between 1971 and 2000 (Figure 3.8D-F).

107

DISCUSSION

Climatic similarity to the current range of an organism is one of several
factors that have been proposed to influence the likelihood of establishment of a
nonindigenous species (Williamson 1996, NCR 2002). Previous researchers
have used CLIMEX to estimate the potential geographic distributions of many
exotic species (e.g. Samways et al. 1999, Scott and Yeoh 1999, Venette and
Hutchison 1999, Holt and Boose 2000, Matsuki et al. 2001; see Sutherst et al.
1999 for list of other CLIMEX citations prior to 1999) and some biological control
agents (e.g. Worner et al. 1989, Scott 1992, Julien et al. 1995, Palmer et al.
2000). Results from the CLIMEX climate-matching analyses in this study show
that a relatively small area south of the Great Lakes region was fairly similar (2
80%) in overall spring climate to one of the 11 epizootic-specific sites. I found
that the area along the southern portion of the North Central region, extending
from Kansas and Nebraska east to the Atlantic coast, experiences climatic
conditions that are conducive for the development of E. maimaiga epizootics in
an average year. However, while CLIMEX serves as a useful tool for initial
estimation of a species potential distribution, it uses relatively coarse descriptors
of climate. Phenology of gypsy moth larvae and the development of E.
maimaiga epizootics are mediated by current-year meteorological factors and
are not satisfactorily defined by calendar months. While monthly-based
meteorological factors are coarse descriptors of climate and are not adequate

for more sensitive analyses of E. maimaiga epizootic development, calendar

108

months are the most available units for long-term comparisons of meteorological
data. Additionally, CLIMEX does not account for the temporal variability of
precipitation and temperature which could strongly affect E. maimaiga and
gypsy moth dynamics.

Organisms with broad ecological tolerances are more likely to become
established in climatically variable environments, while those with narrow
tolerances, such as the E. maimaiga fungal pathogen, are more likely to
become established in climatically stable areas that are physiologically
appropriate for that species (Leigh 1981, Crawley 1986). My assessment of
climate in the North Central region shows that precipitation and temperature
conditions are not uniformly consistent throughout the region and that some
areas are more climatically variable on a year to year basis than others.
Portions of the North Central region with high climatic variability may not
experience the particular conditions necessary for the development of E.
maimaiga epizootics as often as areas with low climatic variability. Alternatively,
in regions where climate comparisons suggest that conditions may be not
favorable for the development of E. maimaiga epizootics, highly variable areas
are more likely than areas with low variability to periodically experience suitable
climatic conditions for epizootics to occur, if adequate fungal inoculum and
suitable hosts are present. This suggests that climate comparisons based on
30-yr averages only, may not provide an adequate description of an area’s
climatic conduciveness for E. maimaiga. Additionally, sites may vary more in

one meteorological parameter than another at a given time of year which could

109

affect E. maimaiga germination and infection rates and, thus, the likelihood that
an epizootic may develop. Areas, such as the southern portion of the North
Central region, that are fairly similar in overall spring climate to one of the
epizootic-specific sites are also the least variable in temperature but the most
variable in precipitation.

In my assessment of climate in the North Central region, I conducted
climate comparisons and examined deviations of meteorological factors from
30—yr averages for spring and annual climate data. It is currently unclear
specifically how E. maimaiga is affected by spring versus annual climate (Hajek
1999). Spring climate is likely to be integral to the development of E. maimaiga
epizootics, based on the organism’s close phenological association with gypsy
moth larval development (Hajek and Roberts 1991). Annual climate, on the
other hand, may affect survival of E. maimaiga resting spores, their persistence
in the environment, or perhaps the synchrony of resting spore germination with
gypsy moth larvae. In addition, climatic deviations from 30-yr averages (i.e.
normal conditions) are not uniform throughout the year and trends differ for
precipitation and temperature. For instance, more than a quarter of the total
departures in precipitation from normal conditions occur in the spring, with May
and June precipitation being generally more variable than April precipitation.
Alternatively, less than a quarter of the total departures in temperature from
normal conditions occur in the spring, with April and May temperatures being
generally more variable than June temperatures.

Based on the meteorological conditions associated with the epizootic-

110

specific sites, I estimated the number of years that conditions from 1971 to 2000
were met or exceeded in the North Central region. Entomophaga maimaiga
epizootics used in this study occurred over a relatively broad range of
meteorological conditions, but were not distributed evenly over the known range
of gypsy moth and E. maimaiga. Whether this adequately represents the range
of meteorological conditions necessary for the development of an E. maimaiga
epizootic or is an artifact of the documented epizootics being relatively confined
geographically, and therefore potentially limited in meteorological stochasticity,
remains to be determined.

While moisture clearly influences E. maimaiga dynamics (Hajek 1999),
few studies have associated levels of E. maimaiga infection with meteorological
field conditions. Weseloh and Andreadis (1992b) found that a 1989 E.
maimaiga epizootic in Fairfield County, Connecticut was positively associated
with above average precipitation in May and June. In 1991, E. maimaiga
infections were positively correlated with May precipitation across plots in four
states (Hajek et al. 1996b). Following introductions in Michigan, Smitley et al.
(1995) found E. maimaiga infection levels were positively associated with
precipitation two weeks before gypsy moth larvae were sampled.

This study further substantiates that the majority of documented E.
maimaiga epizootics that have occurred, with few possible exceptions, have
been positively associated with abundant, above average precipitation in May
and June. A few other E. maimaiga epizootics that have been documented in

the literature (e.g. Hajek 1997a, Hajek and Humber 1997, Hajek et al. 1990b,

111

1999) were not included in these analyses because I was unable to obtain
complete year-specific weather data for the locations. Hajek et al. (1990b)
associated E. maimaiga epizootics in four research plots in central
Massachusetts in 1989 with precipitation in May and June that was above the
30-yr average. However, epizootics in Tompkins County, New York, were
recorded in 1992 (Hajek 1997a, Hajek and Humber 1997, Hajek et al. 1999),
when only slightly above average precipitation occurred. Additionally, an E.
maimaiga epizootic is reported to have occurred in Tompkins County, New
York, in 1991 (Hajek 1997a, Hajek et al. 1999) which had below normal
precipitation in May and June. Unfortunately, the level of E. maimaiga infection
was not quantified and Hajek (1997a) reported that gypsy moth populations
were not reduced substantially. Other locations with documented E. maimaiga
infections were not included in analyses because I could not conclusively
discern whether or not a large-scale epizootic (>60% mortality) or some lower
level of infection had occurred.

The results of this research have implications for gypsy moth
management in the North Central region, especially for areas along the leading
edge where gypsy moth populations are expanding or have recently become
established. States in the southern portion of the North Central region, including
Indiana, Illinois, Kentucky, Missouri and Ohio, appear to be more climatically
conducive for the development of frequent E. maimaiga epizootics than states in
the northern portion of the region. The northern tier of states in the North

Central region, specifically Minnesota, Wisconsin and Michigan, do not appear

112

to consistently receive adequate levels of precipitation or temperature
necessary for frequent E. maimaiga epizootics. Additionally, these areas tend
to be least variable in precipitation compared to the rest of the region,
suggesting that the likelihood of a precipitation event necessary for an epizootic
may be relatively uncommon. Unfortunately, Minnesota, Wisconsin and
Michigan have some of the most susceptible forests to gypsy moth, as
measured by the total basal area of preferred tree species, in the North Central
region (Liebhold et al. 1997a, 1997b). This may result in larger, more damaging
gypsy moth populations and potentially a greater rate of spread, though this
remains to be determined. Lower, non-epizootic levels of E. maimaiga infection,
however, may still be beneficial in managing gypsy moth populations in these
regions. Complete assessment of the role of climatic variability and the
occurrence of E. maimaiga epizootics in the North Central region will only be
achieved through long-term monitoring and landscape-level studies. Thorough
examination of E. maimaiga infection dynamics under varying meteorological
conditions and corresponding interactions with other natural enemies will be
needed to determine the extent to which this fungal pathogen will serve in the

successful management of gypsy moth in North America.

113

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120

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131

MANAGEMENT IMPLICATIONS

The range of gypsy moth, Lymantria dispar L. (Lepidoptera:
Lymantriidae), in North America continues to expand from its initial introduction
in the northeastern United States. Gypsy moth populations are currently
spreading into the North Central region and have recently become established
in Illinois, Indiana, Ohio and Wisconsin (NAPIS 2004). Gypsy moth populations
threaten to expand into areas with large extents of forest that are highly
susceptible to defoliation (Liebhold et al. 1997a, 1997b). Resource managers in
these areas are very interested in incorporating E. maimaiga into their gypsy
moth management strategies. However, if the pattern of epizootics observed in
Michigan is representative of what may be expected in other states in the North
Central region, then E. maimaiga may not reliably suppress gypsy moth
populations on a consistent basis. The success of E. maimaiga is directly
dependent on environmental conditions and effective suppression of gypsy
moth with this fungal pathogen in the North Central region will likely be mediated
by local variability of microclimate.

Results from this research indicate that E. maimaiga infection is
associated with environmental moisture and that above average precipitation
may be necessary for the development of epizootics in the northern portions of
the North Central region. Increased levels of E. maimaiga infection in field
bioassays in Michigan were correlated with June precipitation levels which were

significantly greater than 30-year average conditions. In a 6-wk field study,

132

infection of gypsy moth larvae through primary transmission by E. maimaiga
occurred from late May to early July. Infection rates of E. maimaiga during
primary transmission were somewhat variable but were generally associated
with the number of hours that precipitation occurred.

The apparent association of increased levels of E. maimaiga infection
with above average precipitation suggests that the development of epizootics
may be strongly affected by climatic variability. The northern tier of states in the
North Central region, specifically Minnesota, Wisconsin and Michigan, do not
appear to consistently receive adequate levels of precipitation necessary for
frequent E. maimaiga epizootics. Additionally, these areas tend to be least
variable in precipitation compared to the rest of the region, suggesting that the
likelihood of a precipitation event necessary to initiate an epizootic may be
relatively uncommon. As these areas have some of the most susceptible
forests to gypsy moth, this may result in larger, more damaging gypsy moth
populations and potentially a greater rate of spread, though this remains to be
determined.

Despite E. maimaiga’s apparent mediation by variability of microclimate
in northern portions of the North Central region, continued efforts to introduce
and establish this fungal pathogen in areas where gypsy moth has recently
become established or is expanding into should still be attempted. Emphasis,
however, should be placed on developing an integrated approach for managing
gypsy moth in these areas and not assuming that E. maimaiga epizootics will

consistently suppress gypsy moth populations. Lower levels of E. maimaiga

133

infection between large-scale epizootics may still be beneficial in managing
gypsy moth populations by maintaining adequate levels of fungal inoculum in
the soil. The frequency of epizootics and the interactions of E. maimaiga with
other natural enemies between epizootics may ultimately determine the role that
this fungal pathogen plays in managing gypsy moth in northern regions.

Much remains to be learned about E. maimaiga and its effect on gypsy
moth populations in the North Central region. Future research should involve
thorough examination of E. maimaiga infection dynamics and corresponding
interactions with other natural enemies, especially NPV, under varying
meteorological conditions. The infection dynamics of these pathogens and the
role of microclimatic factors should be evaluated for transmission-specific (i.e.
primary versus secondary transmission) stages in the development of
epizootics. Evaluation of hourly microclimatic conditions during larval
development in stands with a range of gypsy moth population densities would
improve our understanding of the primary and secondary transmission dynamics
of E. maimaiga and NPV. Additionally, this could improve our understanding of
the requisite host and pathogen population sizes necessary for the development
of large-scale epizootics. Future research involving landscape-level studies and
long-term monitoring will be needed to fully assess the role of climatic variability
and could significantly aid in the development of our ability to accurately predict
epizootics in North American gypsy moth populations. This information would
be useful in developing improved methods to successfully incorporate E.

maimaiga into an integrated pest management system for the effective control

134

of gypsy moth, as it’s range expands through in North America.

135

APPENDICES

136

APPENDIX A

137

Appendix A
Record of Deposition of Voucher Specimens

The specimens listed on the following sheets have been deposited in the named
museum as samples of those species or other taxa which were used in this
research. Voucher recognition labels bearing the Voucher No. have been
attached or included in fluid-preserved specimens.

Voucher No.2 MSU 2003-04
ARSEF 6626 - 6630
ARSEF 6652 - 6657
ARSEF 6663 - 6668
ARSEF 6724 - 6729
ARSEF 7103, 7107 - 7111

Title of dissertation:
Meteorological factors affecting the success of the gypsy moth fungal pathogen
Entomophaga maimaiga (Zygomycetes: Entomophthorales) in Michigan
Museums where deposited and abbreviations for table on following sheets:
1) A.J. Cook Arthropod Research Collection
Department of Entomology, Michigan State University (MSU)
243 Natural Sciences Building, East Lansing, Michigan 48824-1115
Gary L. Parsons, Curator
2) United States Department of Agriculture, Agricultural Research Service
Collection of Entomopathogenic Fungal Cultures (ARSEF)
United States Plant, Soil, and Nutrition Laboratory
Tower Road, Ithaca, New York 14853 - 2901

Richard A. Humber, Curator

Investigator's Name: Nathan Wade Siegert

Date: 13 March 2003

The Voucher No. are assigned by the respective curators at ARSEF & MSU.

138

Appendix A

Voucher Specimen Data

Page 1 of 19 Pages

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139

Appendix A

Voucher Specimen Data

Page 2 of 19 Pages

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140

Appendix A

Voucher Specimen Data

Page 3 of 19 Pages

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141

Appendix A

Voucher Specimen Data

Page 4 of 19 Pages

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142

Appendix A

Voucher Specimen Data

Page 5 of 19 Pages

 

 

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143

Appendix A

Voucher Specimen Data

Page 6 of 19 Pages

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144

Appendix A

Voucher Specimen Data

Page 7 of 19 Pages

        
 

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145

Appendix A

Voucher Specimen Data

Page 8 of 19 Pages

 

 

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146

Appendix A

Voucher Specimen Data

Page 9 of 19 Pages

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147

Appendix A

Voucher Specimen Data

Page 10 of 19 Pages

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148

Appendix A

Voucher Specimen Data

Page 11 of 19 Pages

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149

Appendix A

Voucher Specimen Data

Page 12 of 19 Pages

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150

Appendix A

Voucher Specimen Data

Page 13 of 19 Pages

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151

Appendix A

Voucher Specimen Data

Page 14 of 19 Pages

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152

Appendix A

Voucher Specimen Data

Page 15 of 19 Pages

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153

Appendix A

Voucher Specimen Data

Page 16 of 19 Pages

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154

Appendix A

Voucher Specimen Data

Page 17 of 19 Pages

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155

Appendix A

Voucher Specimen Data

Page 18 of 19 Pages

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156

Appendix A

Voucher Specimen Data

Page 19 of 19 Pages

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157

APPENDIX B

158

Appendix B

Figure B1. Michigan average daily temperature (°C) January through
December, 1961-1990 (adapted from MRCC 2000). Temperature gradient
varies by month, with areas covered by darker shades of gray indicating higher
average daily temperatures than areas covered by lighter shades of gray. White
dots indicate locations of Entomophaga maimaiga field bioassay sites in
Michigan, 1999-2002.

 

 

 

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159

Appendix B

Figure 82. Michigan average daily maximum temperature (°C) January through
December, 1961-1990 (adapted from MRCC 2000). Temperature gradient
varies by month, with areas covered by darker shades of gray indicating higher
average daily maximum temperatures than areas covered by lighter shades of
gray. White dots indicate locations of Entomophaga maimaiga field bioassay
sites in Michigan, 1999-2002.

 

 

 

 

 

 

 

 

 

160

Appendix B

Figure B3. Michigan average daily minimum temperature (°C) January through
December, 1961-1990 (adapted from MRCC 2000). Temperature gradient
varies by month, with areas covered by darker shades of gray indicating higher
average daily minimum temperatures than areas covered by lighter shades of
gray. White dots indicate locations of Entomophaga maimaiga field bioassay
sites in Michigan, 1999-2002.

 

 

 

 

 

*l' 11“
, “Hf-.14 ‘1

$5 -.1e~ ,.

 

 

 

 

 

 

161

Appendix B

Figure 84. Michigan average monthly precipitation (mm) January through
December, 1961-1990 (adapted from MRCC 2000). Precipitation gradient
varies by month, with areas covered by darker shades of gray indicating higher
average monthly precipitation averages than areas covered by lighter shades of
gray. White dots indicate locations of Entomophaga maimaiga field bioassay
sites in Michigan, 1999-2002.

 

 

 

 

.- 3:"
’ 0107 -l

_7;.15 1‘"

 

 

 

 

162

 

APPENDIX C

163

Appendix C

Table C1. North American locations used in the CLIMEX climate-matching
analyses (n = 1132 locations). Additional locations added to the CLIMEX
meteorological database to better represent climatic variability in the North
Central region (n = 832 locations) are listed in upper-case characters.

 

 

164

Country Province/State Location Latitude Longitude
Barbados no states Bridgetown 13.2 N 59.7 W
Canada Alberta Banff 51.2 N 115.6 W
Beaverlodge 55.2 N 119.4 W

Calgary 51.1 N 114.0 W

Edmonton 53.6 N 113.5 W

Embarras 58.2 N 111.4 W

Fort McMurray 56.7 N 111.2 W

Grande Prairie 55.2 N 118.9 W

Jasper 52.9 N 118.1 W

Keg River 57.8 N 117.9 W

Lethbridge 49.6 N 112.8 W

Medicine Hat 50.0 N 110.7 W

British Columbia Bull Harbour 50.9 N 127.9 W
Cranbrook 49.5 N 115.8 W

Estevan Point 49.4 N 126.5 W

Fort Nelson 58.8 N 122.6 W

Hope 49.4 N 121.4 W

Penticton 49.5 N 119.6 W

Prince George 53.9 N 122.7 W

Prince Rupert 54.3 N 130.4 W

Vancouver 49.2 N 123.2 W

Victoria 48.4 N 123.3 W

Manitoba Churchill 58.8 N 94.1 W
Dauphin 51.1 N 100.1 W

Gillam 56.3 N 94.7 W

Rivers 50.0 N 100.3 W

The Pas 53.9 N 101.2 W

Winnipeg 49.9 N 97.2 W

New Brunswick Chatham 47.0 N 65.4 W
Moncton 46.1 N 64.7 W

Newfoundland Belle Isle 51.9 N 55.4 W
Cape Race 46.7 N 53.1 W

Cartwright 53.7 N 57.0 W

Fogo 49.7 N 54.3 W

Goose Bay 53.3 N 60.4 W

Table C1 (cont’d.)

Cuba

Northwest Terr.

Nova Scotia

Ontario

Prince Edward Is.

Quebec

Saskatchewan

Yukon Territory

no states

Grand Bank
Hopedale
St Johns

Fort Resolution
Fort Simpson
Nottingham Island
Resolution Island

Halifax
Yarmouth

Armstrong
Earlton
Kapuskasing
Lansdowne House
London
Moosonee
Nakina

North Bay
Ottawa

Pagwa

Pickle Lake
Porquis Junction
Sioux Lookout
Toronto

Trout Lake

Summerside

Fort Chimo

Great Whale River
Grindstone Island
Harrington Harbour
lnoucdjouac
Megantic

Montreal
Nitchequon
Quebec

Regina
Saskatoon
Swift Current

Watson Lake
Whitehorse

Colon
Habana

165

47.1 N
55.5 N
47.6 N

61.2 N
61.9 N
63.1 N
61.4 N

44.7 N
43.8 N

50.3 N
47.7 N
49.4 N
52.2 N
43.0 N
51.3 N
50.2 N
46.4 N
45.4 N
50.0 N
51.5 N
48.7 N
50.1 N
43.7 N
53.8 N

46.4 N

58.1 N
55.3 N
47.4 N
50.5 N
58.4 N
45.6 N
45.5 N
53.2 N
46.8 N

50.5 N
52.1 N
50.3 N

60.1 N
60.7 N

22.7 N
23.2 N

55.8 W
60.2 W
52.7 W

113.7W
121.3W

77.9 W
64.9 W

63.6 W
66.1 W

89.0 W
79.8 W
82.5 W
87.9 W
81.2 W
80.7 W
86.7 W
79.4 W
75.7 W
85.3 W
90.3 W
80.8 W
91.9 W
79.4 W
89.9 W

63.8 W

68.4 W
77.8 W
61.9 W
59.5 W
78.3 W
70.8 W
73.6 W
70.9 W
71.3 W

104.6 W
106.6 W
107.7 W

128.8 W
135.1 W

80.8 W
82.5 W

El Salvador

Guatemala

Mexico

Table C1 (cont’d.)

no states

no states

no states

San Salvador

Coban
Guatemala City
Puerto San Jose
Santa Elena

Acapulco de Juarez
Aguascalientes
Campeche
Chihuahua
Chilpancingo
Ciudad Lerdo
Colima

Comitan
Cozumel
Culiacan
Durango
Ensenada
Guadalajara
Guanajuato
Guaymas
Hermosillo
Huejucar

lsla Guadalupe
Jalapa Enriquez
La Paz

Lagos de Moreno
Leon
Manzanillo
Mazatlan
Merida

Mexico
Monclova
Monterrey
Morelia

Oaxaca de Juarez
Orizaba
Pachuca
Piedras Negras
Progreso
Puebla

Puerto Cortes
Queretaro

Rio Verde
S.Cristobel de Cas
Salina Cruz
Saltillo

San Luis Potosi
Soto la Marina

166

13.7 N

15.5 N
14.7 N
13.9 N
16.6 N

16.8 N
21.9 N
19.9 N
28.6 N
17.5 N
25.5 N
19.2 N
16.3 N
20.5 N
24.8 N
24.0 N
31.9 N
20.7 N
21.0 N
28.0 N
29.1 N
22.4 N
29.2 N
19.5 N
24.2 N
21.4 N
21.1 N
19.0 N
23.2 N
21.0 N
19.4 N
26.9 N
25.7 N
19.7 N
17.1 N
18.9 N
20.1 N
28.7 N
21.3 N
19.0 N
24.4 N
20.6 N
21.9 N
16.7 N
16.2 N
25.5 N
22.1 N
23.8 N

89.2 W

90.3 W
90.3 W
90.5 W
89.6 W

99.9 W
102.3 W
90.6 W
106.1 W
99.5 W
103.5 W
103.7 W
92.1 W
86.9 W
107.4 W
104.7 W
116.6 W
103.4 W
101.3 W
111.0 W
111.0 W
103.2 W
118.3 W
96.9 W
110.2 W
101.9 W
101.7 W
104.3 W
106.4 W
89.6 W
99.2 W
101.4 W
100.3 W
101.2 W
96.7 W
97.1 W
98.7 W
100.5 W
89.7 W
98.2 W
111.9 W
100.4 W
100.0 W
92.6 W
95.2 W
101.0 W
101.0 W
98.2 W

Table C1 (cont’d.)

U.S.A.

Alabama

Alaska

Arizona

Arkansas

California

Colorado

Tampico
Tapachula
Tepic

Tlaxcala de Xico
Toluca

Torreon
Tulancingo
Tuxtla Gutierrez
Veracruz

Birmingham
Mobile
Montgomery

Anchorage
Bethel

Eagle
Fahbanks
Gambell
Juneau
Ketchikan
Nome

St Paul Island

Flagstaff
Phoenix
Tucson
Winslow
Yuma

Fort Smith
Little Rock

Bakersfield
Bishop
Eureka
Fresno

Los Angeles
Mount Wilson
Mt. Shasta
Red Bluff
Sacramento
San Diego
San Francisco
San Jose
Santa Maria
Stockton

Colorado Springs
Denver

167

22.2 N
14.9 N
21.5 N
19.3 N
19.3 N
25.5 N
20.1 N
16.8 N
19.2 N

33.6 N
30.7 N
32.3 N

61.2 N
60.8 N
64.8 N
64.3 N
63.8 N
58.3 N
55.4 N
64.5 N
57.2 N

35.1 N
33.4 N
32.3 N
35.0 N
32.7 N

35.3 N
34.7 N

35.4 N
37.3 N
40.8 N
36.8 N
33.9 N
34.2 N
41.3 N
40.2 N
38.5 N
32.7 N
37.6 N
37.3 N
34.9 N
38.0 N

38.8 N
39.8 N

97.8 W
92.3 W
104.9 W
98.2 W
99.7 W
103.4 W
98.4 W
93.1 W
96.1 W

86.8 W
88.0 W
86.4 W

149.8 W
161.8 W
141.2 W
147.9 W
171.8 W
134.3 W
131.7 W
165.5 W
170.2 W

111.7 W
112.0W
110.9W
110.7W
114.6W

94.4 W
92.2 W

119.0W
118.4W
124.2W
119.7W
118.4W
118.1 W
122.3W
122.3W
121.5W
117.2W
122.4W
121.9W
120.4W
121.3W

104.7 W
104.9 W

Table C1 (cont’d.)

Connecticut

District of Columbia

Georgia

Florida

Hawaii

Idaho

Illinois

Grand Junction
Pueblo

New Haven
Washington

Jacksonville
Key West
Miami
Pensacola
Tampa

Atlanta
Thomasville

Hilo
Honolulu
Lihue

Boise
Pocatello

ALBION

ALEDO
ALTON_DAM_26
ANNA_1_E

ANTIOCH

AURORA
BELLEVILLE_SIU_RES
BROOKPORT_DAM_52
CAIRO_3_N

CARBONDALE_SEWAGE_

CARLINVILLE
CHARLESTON

CHENOA
CHICAGO_MIDWAY_AP_
CHICAGO_O'HARE_WSO
CHICAGO_UNIVERSITY
DANVILLE

DECATUR
DIXON_1_NW
DU_QUO|N_2_S
EFFINGHAM
FAIRFIELD_RADIO_WF
FLORA_5_NW
FULTON_LOCK_&_DAM_
GALESBURG

GALVA

GENESEO

168

39.1 N
38.3 N

41.3N

38.9 N

30.4 N
24.5 N
25.8 N
30.4 N
28.0 N

33.7 N
30.8 N

19.7 N
21.3 N
22.0 N

43.6 N
42.9 N

38.4 N
41.2 N
38.9 N
37.5 N
42.5 N
41.8 N
38.5 N
37.1 N
37.0 N
37.7 N
39.3 N
39.5 N
40.7 N
41.7 N
42.0 N
41.8 N
40.1 N
39.8 N
41.8 N
38.0 N
39.1 N
38.4 N
38.7 N
41.9 N
41.0 N
41.2 N
41.5 N

108.5 W
104.5 W

72.9 W

77.1 W

81.7 W
81.8 W
80.3 W
87.2 W
82.5 W

84.4 W
84.0 W

155.1 W
157.8 W
159.4 W

116.2W
112.6W

88.1 W
90.7 W
90.2 W
89.2 W
88.1 W
88.3 W
89.8 W
88.7 W
89.2 W
89.2 W
89.9 W
88.2 W
88.7 W
87.8 W
87.9 W
87.6 W
87.7 W
89.0 W
89.5 W
89.2 W
88.5 W
88.3 W
88.6 W
90.2 W
90.4 W
90.1 W
90.2 W

Table C1 (cont’d.)

GOLDEN
GRIGGSVILLE
HARRISBURG
HILLSBORO_2_ssw
HOOPESTON
JACKSONVILLE_2_E
JERSEWILLE_2_SW
LACON_1_N
LA_HARPE_1_SW
LINCOLN

MARENGO
MASON_CITY_1_W
MATTOON
MC_LEANSBORO_2_E
MINONK
MOLINE_WSO_AP
MONMOUTH
MORRISON
MOUNT_CARROLL
MT_VERNON_3_NE
NASHVILLE_4_NE
NEWTON_6_SSE
OLNEY
OTTAWA_4_SW
PALESTINE

PANA
PARIS_WATERWORKS
PARK_FOREST
PAW_PAW_1__E
PEORIA_WSO_AIRPORT
PONTIAC
PRINCEVILLE
QUINCY_FAA_AIRPORT
RANTOUL
ROCKFORD_WSO_AP
RUSHVILLE

SALEM

SPARTA_3_N
SPRINGFIELD_WSO_AP
STOCKTON_1_N
TUSCOLA

URBANA
VIRDEN_1_N
WALNUT

WATERLOO
WATSEKA_2_NW
WAUKEGAN_2_WNW
WHEATON_3_SE
WHITE_HALL_1_E
WINDSOR

169

40.1 N
39.7 N
37.7 N
39.2 N
40.5 N
39.7 N
39.1 N
41.0 N
40.6 N
40.2 N
42.3 N
40.2 N
39.5 N
38.1 N
40.9 N
41.5 N
40.9 N
41.8 N
42.1 N
38.3 N
38.4 N
38.9 N
38.7 N
41.3 N
39.0 N
39.4 N
39.6 N
41.5 N
41.7 N
40.7 N
40.9 N
40.9 N
39.9 N
40.3 N
42.2 N
40.1 N
38.6 N
38.2 N
39.8 N
42.3 N
39.8 N
40.1 N
39.5 N
41.5 N
38.3 N
40.8 N
42.3 N
41.8 N
39.4 N
39.4 N

91.0 W
90.7 W
88.5 W
89.5 W
87.7 W
90.2 W
90.3 W
89.4 W
91.0 W
89.4 W
88.6 W
89.7 W
88.3 W
88.5 W
89.1 W
90.5 W
90.7 W
90.0 W
90.0 W
88.9 W
89.3 W
88.1 W
88.1 W
88.9 W
87.6 W
89.1 W
87.7 W
87.7 W
89.0 W
89.7 W
88.6 W
89.8 W
91.2 W
88.2 W
89.1 W
90.6 W
88.9 W
89.7 W
89.7 W
90.0 W
88.3 W
88.2 W
89.8 W
89.6 W
90.2 W
87.8 W
87.9 W
88.1 W
90.4 W
88.6 W

Table C1 (cont’d.)

Indiana

AN DERSON_QUARTZ_PL
BERNE

BLOOMINGTON
BROOKVILLE
CAMBRIDGE_CITY
COLUMBIA_CITY
COLUMBUS
CRANE_NAVAL_DEPOT
DELPHI
DUBOIS_S_IND_FORAG
ELWOOD

EVANSVILLE

Evansville
EVANSVILLE_WSO_AP
FARMLAND_5_NNW

Fort Wayne

F ORT_WAYN E_W SO_AP
FRANKFORT_DISPOSAL
GOSHEN_COLLEGE
GREENCASTLE_1_E
GREENFIELD
GREENSBURG
HOBART_2_W NW
Indianapolis
INDIANAPOLIS_SE_SI
INDIANAPOLIS_WSFO_
KENTLAND
LAFAYETTE_5_S
LAGRANGE_SEWAG E_PL
LA_PORTE

LOWELL

MADISON

MARION_2_N
MARTINSVILLE_2_SW
MOUNT_VERNON_WATER
NEW_CASTLE
NORTH_VERNON_2_SW
OAKLANDON_GEIST_RE
OOLITIC__EXP_FARM
PAOLI

PRINCETON_1_W
ROCHESTER
ROCKVILLE
RUSHVILLE_SEWAGE_P
SAINT_MEINRAD
SCOTTSBURG
SEYMOUR_1_N
SHELBWILLE
SHOALS_HIWAY_50_BR
South Bend

170

40.1 N
40.7 N
39.2 N
39.4 N
39.8 N
41.2 N
39.2 N
38.9 N
40.6 N
38.5 N
40.3 N
38.0 N
38.0 N
38.0 N
40.3 N
41.0 N
41.0 N
40.3 N
41.6 N
39.7 N
39.8 N
39.3 N
41.5 N
39.7 N
39.7 N
39.7 N
40.8 N
40.3 N
41.7 N
41.6 N
41.3 N
38.7 N
40.6 N
39.4 N
37.9 N
39.9 N
39.0 N
39.9 N
38.9 N
38.6 N
38.3 N
41.1 N
39.8 N
39.6 N
38.2 N
38.7 N
39.0 N
39.5 N
38.7 N
41.7 N

85.7 W
84.9 W
86.5 W
85.0 W
85.2 W
85.5 W
85.9 W
86.8 W
86.7 W
86.7 W
85.8 W
87.6 W
87.5 W
87.5 W
85.2 W
85.2 W
85.2 W
86.5 W
85.8 W
86.8 W
85.8 W
85.5 W
87.3 W
86.2 W
86.0 W
86.3 W
87.4 W
86.9 W
85.4 W
86.7 W
87.4 W
85.4 W
85.7 W
86.4 W
87.9 W
85.4 W
85.7 W
86.0 W
86.5 W
86.5 W
87.6 W
86.2 W
87.2 W
85.4 W
86.8 W
85.8 W
85.9 W
85.8 W
86.8 W
86.3 W

Table C1 (cont’d.)

Iowa

SOUTH_BEND_WSO_AIR
SPENCER

TELL_CITY
VALPARAISO_WATER_W
VEVAY

WABASH
WANATAH_2_W NW
WASHINGTON
WEST_LAFAYETTE_6_N
WHITESTOWN
WINAMAC
WINCHESTER_AIRPORT

ALBIA_3_NNE
ALGONA_3_W

ALLISON
ANAMOSA_1_NW
ANKENY
ATLANTIC_1_NE
AUDUBON_1_SSE
BEACONSFIELD_2_N
BEDFORD

BELLEVU E_LOCK_&_DA
BELLE_PLAINE
BLOOMFIELD_1_WNW
BOONE

BRITT

CARROLL

CASCADE
CASTANA_EXPERIMENT
CEDAR_RAPIDS_AP
CEDAR_RAPIDS_NO_1
CENTERVILLE
CHARITON_1_E
CHARLES_CITY

CH EROKEE_2_S
CLARINDA

CLARION

CLINTON_1
COLUMBUS_JUNCT_2_S
CORNING
CRESCO_1_NE
CRESTON_2_SW
DECORAH

DENISON

Des Moines
DES_MOINES_WSFO_AR
DUBUQUE_LOCK_&_DAM
DUBUQUE_WSO_AP
ELDORA

171

41.8 N
39.3 N
38.0 N
41.5 N
38.8 N
40.8 N
41.4 N
38.7 N
40.5 N
40.0 N
41.0 N
40.2 N

41.1 N
43.1 N
42.8 N
42.1 N
41.7 N
41.4 N
41.7 N
40.8 N
40.7 N
42.3 N
41.9 N
40.8 N
42.0 N
43.1 N
42.1 N
42.3 N
42.1 N
41.9 N
42.0 N
40.7 N
41.0 N
43.0 N
42.8 N
40.7 N
42.7 N
41.8 N
41.3 N
41.0 N
43.4 N
41.0 N
43.3 N
42.0 N
41.5 N
41.5 N
42.5 N
42.4 N
42.3 N

86.2 W
86.8 W
86.8 W
87.0 W
85.1 W
85.8 W
86.9 W
87.2 W
87.0 W
86.3 W
86.6 W
84.9 W

92.8 W
94.3 W
92.8 W
91.3 W
93.6 W
95.0 W
94.9 W
94.1 W
94.7 W
90.4 W
92.3 W
92.4 W
93.9 W
93.8 W
94.8 W
91.0 W
95.8 W
91.7 W
91.6 W
92.9 W
93.3 W
92.7 W
95.5 W
95.0 W
93.7 W
90.3 W
91.4 W
94.8 W
92.1 W
94.4 W
91.8 W
95.3 W
93.7 W
93.7 W
90.7 W
90.7 W
93.1 W

Table C1 (cont’d.)

ELKADER_5_SSW
EMMETSBURG
ESTHERVILLE
FAIRFIELD

FAYETTE
FOREST_CITY_2_NNE
FORT_DODGE
FORT_MADISON
GLENWOOD_3_SW
GREENFIELD_1_WNW
GRINNELL__3_SW
GRUNDY_CENTER
GUTTENBERG_L_&_D_1
HAMPTON

HARLAN

HAWARDEN
HUMBOLDT_3_W
IDA_GROVE_5_NW
INDIANOLA
IOWA_CITY_1_S
IOWA_I=ALLS
JEFFERSON_1_S
KEOKUK
KEOSAUQUA_STATE_PA
KNOXVILLE
LAKE_PARK
LEON_6_ESE
LE_CLA|RE_L_&_D_14
LE_MARs

LOGAN
MAPLETON_NO_2
MAQUOKETA_3__S
MARSHALLTOWN_2
MASON_CITY
MASON_CITY_AP
MILFORD_4_NW
MOUNT_AYR_4_SW
MOUNT_PLEASANT_1_S
MUSCATINE
NEWTON_2_E
NEW_HAMPTON_1_E
NORTHWOOD
OAKLAND_4_WSW
OELWEIN

ONAWA

OSAGE

OSKALOOSA
OTTUMWA__A|RPORT
PERRY
POCAHONTAS_2_SE

172

42.8 N
43.1 N
43.4 N
41.0 N
42.8 N
43.3 N
42.5 N
40.6 N
41.0 N
41.3 N
41.7 N
42.4 N
42.8 N
42.8 N
41.7 N
43.0 N
42.7 N
42.4 N
41.4 N
41.7 N
42.5 N
42.0 N
40.4 N
40.7 N
41.3 N
43.5 N
40.7 N
41.6 N
42.8 N
41.6 N
42.2 N
42.0 N
42.1 N
43.2 N
43.2 N
43.4 N
40.7 N
41.0 N
41.4 N
41.7 N
43.0 N
43.5 N
41.3 N
42.7 N
42.0 N
43.3 N
41.3 N
41.1 N
41.8 N
42.7 N

91.4 W
94.7 W
94.8 W
91.9 W
91.8 W
93.6 W
94.2 W
91.3 W
95.8 W
94.5 W
92.7 W
92.8 W
91.1 W
93.2 W
95.3 W
96.5 W
94.3 W
95.5 W
93.6 W
91.5 W
93.3 W
94.4 W
91.4 W
92.0 W
93.1 W
95.3 W
93.6 W
90.4 W
96.2 W
95.8 W
95.8 W
90.7 W
92.9 W
93.2 W
93.3 W
95.2 W
94.3 W
91.6 W
91.1 W
93.1 W
92.3 W
93.2 W
95.5 W
91.9 W
96.1 W
92.8 W
92.7 W
92.4 W
94.1 W
94.7 W

Table C1 (cont’d.)

Kansas

Kentucky

PRIMGHAR

RED_OAK
ROCKWELL_CITY
ROCK_RAPIDS
SAC_CITY

SANBORN

SHELDON
SHENANDOAH
SIBLEY_5_NNE
SIDNEY_1_NNW
SIGOURNEY

Sioux City
SIOUX_CENTER_2_SE
SIOUX_CITY_WSO_AP
SIOUX_RAPIDS_4_E
SPENCER_1_N
STORM_LAKE_2_E
SW EA_CITY
TIPTON_4_NE
TOLEDO

TRIPOLI

VINTON
WASHINGTON
WATERLOO_WSO_AP
WAUKON
WEBSTER_CITY
WILLIAMSBURG
WINTERSET_2_N NW

Concordia
Dodge City
Goodland
Topeka
Wichita

ASHLAND_DAM_29
BARBOURVILLE__WATER
BARREN_RIVER_RESER
BAXTER

BEAVER_DAM
BEREA_COLLEGE
BOWLING_GREEN_FAA_
CARROLLTON_LOCK_1
COVINGTON_WSO__AIRP
DANVILLE

DIX_DAM

FALMOUTH
FARMERS_2_S
FRANKFORT_LOCK__4
GLASGOW_WKAY

173

43.1 N
41.0 N
42.4 N
43.4 N
42.4 N
43.2 N
43.2 N
40.8 N
43.5 N
40.8 N
41.3 N
42.4 N
43.0 N
42.4 N
42.9 N
43.0 N
42.6 N
43.4 N
41.8 N
42.0 N
42.8 N
42.2 N
41.3 N
42.5 N
43.3 N
42.5 N
41.7 N
41.4 N

39.5 N
37.8 N
39.4 N
39.1 N
37.7 N

38.5 N
36.9 N
36.9 N
36.8 N
37.4 N
37.6 N
37.0 N
38.7 N
39.0 N
37.7 N
37.8 N
38.7 N
38.1 N
38.2 N
37.0 N

95.6 W
95.2 W
94.6 W
96.2 W
95.0 W
95.7 W
95.8 W
95.4 W
95.7 W
95.7 W
92.2 W
96.4 W
96.2 W
96.4 W
95.1 W
95.2 W
95.2 W
94.3 W
91.1 W
92.6 W
92.3 W
92.0 W
91.7 W
92.4 W
91.5 W
93.8 W
92.0 W
94.0 W

97.7 W

100.0 W
101.7W

95.6 W
97.4 W

82.6 W
83.9 W
86.1 W
83.3 W
86.9 W
84.3 W
86.4 W
85.2 W
84.7 W
84.8 W
84.7 W
84.3 W
83.6 W
84.9 W
85.9 W

Table C1 (cont’d.)

Louisiana

Maryland
Massachusetts

Michigan

GOLDEN_POND_8_N
GREENSBURG
HEIDELBERG
HENDERSON_7_SSW
HOPKINSVILLE
LEITCHFIELD_2_N
Lexington
LEXINGTON_WSO_AIRP
LONDON_FAA_AIRPORT
Louisville
LOUISVILLE_WSO_AIR
LOVELACEVILLE
MADISONVILLE

MAM MOTH_CAVE_PARK
MANCHESTER_4_W
MAYSVILLE_SEWAG E_P
MONTICELLO_3_NE
MOUNT_VERNON
MURRAY
OWENSBORO_3_W
PADUCAH_WSO
ROUGH_R|VER_DAM
SCOTTSVILLE
SHELBYVILLE_1_E
SOMERSET_2_N
SUMMER__SHADE
WARSAW_MARKLAN D_DA
WEST_LIBERTY
WILLIAMSBURG
WILLIAMSTOW N_5_WSW

New Orleans

Eastport
Portland

Baltimore
Boston

ADRIAN_2_NNE
ALBERTA_FORD_FORST
ALLEGAN_5_NE

ALMA
ALPENA_SEWAGE_PLAN
ALPENA_WSO_AIRPORT
ANN__ARBOR_UNIV_OF_
BAD_AXE
BALDWIN_STATE_FORE
BATTLE_CREEK_5_NW

174

36.9 N
37.3 N
37.5 N
37.8 N
36.8 N
37.5 N
38.0 N
38.0 N
37.1 N
38.2 N
38.2 N
37.0 N
37.3 N
37.2 N
37.2 N
38.7 N
36.9 N
37.3 N
36.6 N
37.8 N
37.1 N
37.6 N
36.7 N
38.2 N
37.1 N
36.9 N
38.8 N
37.9 N
36.7 N
38.7 N

30.0 N

44.9 N
43.7 N

39.3 N

42.4 N

41.9 N
46.7 N
42.6 N
43.4 N
45.1 N
45.1 N
42.3 N
43.8 N
43.9 N
42.4 N

88.0 W
85.5 W
83.8 W
87.6 W
87.5 W
86.3 W
84.6 W
84.6 W
84.1 W
85.7 W
85.7 W
88.8 W
87.5 W
86.1 W
83.8 W
83.8 W
84.8 W
84.3 W
88.3 W
87.2 W
88.8 W
86.5 W
86.2 W
85.2 W
84.6 W
85.7 W
85.0 W
83.3 W
84.2 W
84.6 W

90.2 W

67.0 W
70.3 W

76.6 W

71.1 W

84.0 W
88.5 W
85.8 W
84.7 W
83.4 W
83.6 W
83.7 W
83.0 W
85.8 W
85.3 W

Table C1 (cont’d.)

BENTON_HARBOR_ARPT
BERGLAND_HYDRO_PLA
BIG_RAPIDS_WATERWO
BLOOMINGDALE
BOYNE_FALLS

CADILLAC
CARO_REGIONAL_CENT
CHAMPION_VAN_RIPER
CHARLOTTE
CHEBOYGAN
COLDWATER_ST_SCHOO
DEARBORN
DETOUR_VILLAGE

Detroit

DETROIT_M ETRO_WSO_
DOWAGIAC_1_W
EAST_JORDAN
EAST_LANSING_4_S
EAST_TAWAS
EAU_CLAIRE_4_NE
Escanaba
FAYETTE_4_SW

Flint

FLINT_WSO_AP
GAYLORD

GLADWIN

Grand Rapids
GRAND_HAVEN_FIRE_D
GRAN D_MARAIS_2_E
GRAND_RAPIDS_WSO_A
GRAYLING

GREENVILLE
GROSSE_POINTE_FARM
GULL_LAKE_EXPERIME
HALE_LOUD_DAM
HARBOR_BEACH_1_SSE
HART

HASTINGS
HESPERIA_4_W NW
HILLSDALE
HOLLAND_HOPE_COLLE
HOUGHTON_FAA_AIRPO
HOUGHTON_LAKE_6_WS
IONIA_1_W NW
IRONWOOD_DA|LY_GLO
lRON_MTN-KINGSFORD
JACKSON_FAA_ARPT
LAKE_CITY_EXP_FARM
Lansing
LANSING_WSO_AIRPOR

175

42.1 N
46.6 N
43.7 N
42.4 N
45.2 N
44.3 N
43.5 N
46.5 N
42.5 N
45.7 N
42.0 N
42.3 N
46.0 N
42.4 N
42.2 N
42.0 N
45.2 N
42.7 N
44.3 N
42.0 N
45.8 N
45.7 N
43.0 N
43.0 N
45.0 N
44.0 N
42.9 N
43.1 N
46.7 N
42.9 N
44.7 N
43.2 N
42.4 N
42.4 N
44.5 N
43.8 N
43.7 N
42.7 N
43.6 N
41.9 N
42.8 N
47.2 N
44.3 N
43.0 N
46.5 N
45.8 N
42.3 N
44.3 N
42.8 N
42.8 N

86.4 W
89.6 W
85.5 W
86.0 W
84.9 W
85.4 W
83.4 W
88.0 W
84.8 W
84.5 W
85.0 W
83.2 W
83.9 W
83.0 W
83.3 W
86.1 W
85.1 W
84.5 W
83.5 W
86.3 W
87.1 W
86.7 W
83.7 W
83.8 W
84.7 W
84.5 W
85.5 W
86.2 W
85.9 W
85.5 W
84.7 W
85.3 W
82.9 W
85.4 W
83.7 W
82.6 W
86.3 W
85.3 W
86.1 W
84.6 W
86.1 W
88.5 W
84.9 W
85.1 W
90.2 W
88.1 W
84.5 W
85.2 W
84.6 W
84.6 W

Table C1 (cont’d.)

Minnesota

LAPEER
LUDINGTON_5_SE
LUPTON_1_SW
MANISTEE_3_SE
MAPLE_CITY
MARQUETTE
MARQUETTE_WSO
MILFORD_GM_PROVING
MIO_HYDRO_PLANT
MONROE_WATERWORKS
MONTAGUE_4_NW
MOUNT_PLEASANT_COL
MUSKEGON_WSO_AIRPO
NEWBERRY_STATE_HOS
ONAWAY_BLACK_L_FOR
OWOSSO_3_NNW
PELLSTON_FAA_AIRPO
PETOSKEY
PONTIAC_STATE_HOSP

PORT_HURON_SEWAGE_

SAGINAW_FAA_AIRPOR
SAULT_STE_MARIE_Ws
SOUTH_HAVEN
STAMBAUGH_2_SSE
STANDISH_5_SW
STEPHENSON_8_WNW
ST_JAMES_2_S_BEAVE
ST_JOHNS
THREE_RIVERS
TRAVERSE_CITY_FAA__
VANDERBILT_STATE_F
WEST_BRANCH_3_SE
WHITEFISH_POINT

ADA
AGASSIz_REFUGE
ALBERT_LEA_3__SE
ALEXANDRIA_FAA__AIR
ARGYLE_4_E
ARTICHOKE_LAKE
AUSTIN_3_S

BAUDETTE

BEMIDJI

BENSON

BIG_FALLs

BUFFALO

CALEDONIA
CAMBRIDGE_STATE_HO
CANBY

CASS_LAKE

176

43.0 N
43.9 N
44.4 N
44.2 N
44.8 N
46.5 N
46.5 N
42.6 N
44.7 N
41.9 N
43.5 N
43.6 N
43.2 N
46.3 N
45.4 N
43.0 N
45.6 N
45.4 N
42.7 N
43.0 N
43.5 N
46.5 N
42.4 N
46.0 N
44.0 N
45.5 N
45.7 N
43.0 N
41.9 N
44.7 N
45.2 N
44.3 N
46.8 N

47.3 N
48.3 N
43.7 N
45.9 N
48.3 N
45.4 N
43.6 N
48.7 N
47.5 N
45.3 N
48.2 N
45.2 N
43.6 N
45.6 N
44.7 N
47.4 N

83.3 W
86.4 W
84.0 W
86.3 W
85.8 W
87.4 W
87.6 W
83.7 W
84.1 W
83.4 W
86.4 W
84.8 W
86.2 W
85.5 W
84.2 W
84.2 W
84.8 W
85.0 W
83.3 W
82.4 W
84.1 W
84.3 W
86.3 W
88.6 W
84.0 W
87.8 W
85.5 W
84.5 W
85.6 W
85.6 W
84.4 W
84.2 W
85.0 W

96.5 W
96.0 W
93.3 W
95.4 W
96.7 W
96.1 W
93.0 W
94.6 W
94.9 W
95.6 W
93.8 W
93.9 W
91.4 W
93.2 W
96.3 W
94.6 W

Table C1 (cont’d.)

CEDAR

CHASKA

CLOQUET
COLLEGEVILLE_ST_JO
COOK_18_W
CROOKSTON_NW_EXP_S
DETROIT_LAKES__1_NN
Duluth
DULUTH_WSO_AP
FAIRMONT

FARIBAULT
FARMINGTON_3_NW

F ERGUS_FALLS
FOREST_LAKE_5_NE
FOSSTON_1_E
GAYLORD
GLENWOOD_2_WNW
GRAND_MARAIS
GRAND_MEADOW
GRAND_RAPIDS_FORES
GULL_LAKE_DAM
HALLOCK
HIBBING_FAA_AIRPOR
HINCKLEY
HUTCHINSON_1_N
INTERNL_FALLS_WSO_
ITASCA_UNIV_OF_M|N
JORDAN_1_S
LAMBERTON_SW_EXP_S
LEECH_LAKE_FEDERAL
LITCHFIELD
LITTLE_FALLS_1_N
LONG_PRAIR|E
LUVERNE
MADISON_SEWAGE_PLA
MAHNOMEN_1_W
MARSHALL

MELROSE
MILACA_1_ENE
MILAN_1_NW
Minneapolis
MINNEAPOLIS_WSFO_A
MONTEVIDEO_1_SW
MOOSE_LAKE_1_SSE
MORA
MORRIS_WC_EXP_STN
NEW_ULM

OTTERTAIL
OWATONNA
PARK_RAPIDS_2_S

177

45.3 N
44.8 N
46.7 N
45.6 N
47.9 N
47.8 N
46.8 N
46.8 N
46.8 N
43.6 N
44.3 N
44.7 N
46.3 N
45.3 N
47.6 N
44.5 N
45.7 N
47.7 N
43.7 N
47.2 N
46.4 N
48.8 N
47.4 N
46.0 N
44.9 N
48.6 N
47.2 N
44.7 N
44.3 N
47.3 N
45.1 N
46.0 N
46.0 N
43.7 N
45.0 N
47.3 N
44.5 N
45.7 N
45.8 N
45.1 N
44.8 N
44.9 N
44.9 N
46.5 N
45.9 N
45.6 N
44.3 N
46.4 N
44.1 N
46.9 N

93.3 W
93.6 W
92.5 W
94.4 W
93.1 W
96.6 W
95.8 W
92.2 W
92.2 W
94.5 W
93.3 W
93.2 W
96.1 W
92.9 W
95.8 W
94.2 W
95.4 W
90.3 W
92.6 W
93.5 W
94.3 W
96.9 W
92.9 W
92.9 W
94.4 W
93.4 W
95.2 W
93.6 W
95.3 W
94.2 W
94.5 W
94.3 W
94.8 W
96.2 W
96.2 W
96.0 W
95.8 W
94.8 W
93.7 W
95.9 W
93.3 W
93.2 W
95.8 W
92.8 W
93.3 W
95.9 W
94.4 W
95.6 W
93.2 W
95.1 W

Table C1 (cont’d.)

Mississippi

Missouri

PINE_RIVER_DAM
PIPESTONE
POKEGAMA_DAM
PRESTON
REDWOOD_FALLS_FAA_
RED_LAKE_FALLS
RED_LAKE_INDIAN_AG
ROCHESTER_WSO__AP
ROSEMOUNT_AGRI_EXP
ROTHSAY
SANDY_LAKE_DAM_LIB
SANTIAGO_3_E
SPRINGFIELD_1_NW
STEWART
STILLWATER_1_SE
ST_CLOUD_Wso_AP
ST_JAMES_FILT_PLAN
ST_PAUL
ST_PETER_2_SW
THEILMAN

TOWER__3_S

TRACY

TWO_HARBORS
WADENA_3_S
WALKER_AH_GWAH_CHI
WARROAD
WASECA_EXP_STATION
WHEATON
WILLMAR_CNTY_HWY_G
WINDOM

WINNEBAGO
WINNIBIGOSHISH_DAM
WINONA
WINTON_POWER_PLANT
WRIGHT_4_NW
ZUMBROTA

Vicksburg

ADVANCE_1_S
ANDERSON
APPLETON_CITY
ARCADIA
BETHANY
BOLIVAR_1_NE
BOONVILLE
BROOKFIELD
BRUNSWICK
BUFFALO_3_S
BUTLER

178

46.7 N
44.0 N
47.3 N
43.7 N
44.5 N
47.9 N
47.9 N
43.9 N
44.7 N
46.5 N
46.8 N
45.5 N
44.3 N
44.7 N
45.0 N
45.5 N
44.0 N
45.0 N
44.3 N
44.3 N
47.8 N
44.2 N
47.0 N
46.4 N
47.1 N
48.9 N
44.1 N
45.8 N
45.1 N
43.9 N
43.8 N
47.4 N
44.0 N
47.9 N
46.7 N
44.3 N

32.3 N

37.1 N
36.7 N
38.2 N
37.6 N
40.3 N
37.6 N
39.0 N
39.8 N
39.4 N
37.6 N
38.3 N

94.1 W
96.3 W
93.6 W
92.1 W
95.1 W
96.3 W
95.0 W
92.5 W
93.1 W
96.3 W
93.3 W
93.8 W
95.0 W
94.5 W
92.8 W
94.1 W
94.6 W
93.1 W
94.0 W
92.2 W
92.3 W
95.6 W
91.7 W
95.2 W
94.6 W
95.3 W
93.5 W
96.5 W
95.0 W
95.1 W
94.2 W
94.1 W
91.6 W
91.8 W
93.1 W
92.7 W

90.9 W

89.9 W
94.4 W
94.0 W
90.6 W
94.1 W
93.4 W
92.8 W
93.1 W
93.1 W
93.1 W
94.3 W

Table C1 (cont’d.)

CALIFORNIA
CAMDENTON_2_NW
CANTON_L_AN D_D_20
CAPE_GIRARDEAU_FAA
CARROLLTON
CARUTHERSVILLE
CLEARWATER_DAM
CLINTON

CONCEPTION
DONIPHAN

ELDON

ELSBERRY_1_S
FARMINGTON

F REDERICKTOWN
FREEDOM

FULTON

GRANT_CITY
GREENVILLE_6_N
HAMILTON_2_W
HANNIBAL_WATER_WOR
JACKSON
JEFFERSON_CITY_WAT
JOPLIN_FAA_AIRPORT
Kansas City
KENNETT_RADIO_KBOA
KIRKSVILLE

LAKESIDE

LAMAR

LEBANON_2_W
LEES_SUMMIT_REED_W
LEXINGTON_3__NE
LICKING_4_N
LOCKWOOD
MARBLE_HILL
MARSHFIELD
MARYVILLE_2_E
MEXICO

MOBERLY
MOUNTAIN_GROVE_2_N
MT_VERNON_M_U_SW_C
NEOSHO
NEVADA_SEWAG E_PLAN
NEW_FRANKLIN_1_W
OSCEOLA
OZARK_BEACH
POMME_DE_TERRE_DAM
POPLAR_BLU FF
PRINCETON_6_SW
SALEM

SALISBURY

179

38.6 N
38.2 N
40.2 N
37.2 N
39.4 N
36.2 N
37.1 N
38.4 N
40.3 N
36.6 N
38.3 N
39.2 N
37.8 N
37.6 N
38.5 N
38.8 N
40.5 N
37.2 N
39.8 N
39.7 N
37.4 N
38.6 N
37.2 N
39.2 N
36.2 N
40.2 N
38.2 N
37.5 N
37.7 N
38.9 N
39.2 N
37.5 N
37.4 N
37.3 N
37.3 N
40.3 N
39.2 N
39.4 N
37.2 N
37.1 N
36.9 N
37.8 N
39.0 N
38.0 N
36.7 N
37.9 N
36.8 N
40.3 N
37.6 N
39.4 N

92.6 W
92.8 W
91.5 W
89.6 W
93.5 W
89.7 W
90.8 W
93.8 W
94.7 W
90.8 W
92.6 W
90.8 W
90.4 W
90.3 W
91.7 W
91.9 W
94.4 W
90.4 W
94.0 W
91.4 W
89.7 W
92.2 W
94.5 W
94.7 W
90.1 W
92.6 W
92.6 W
94.3 W
92.7 W
94.3 W
93.9 W
91.9 W
93.9 W
90.0 W
92.9 W
94.8 W
91.9 W
92.4 W
92.3 W
93.9 W
94.4 W
94.4 W
92.8 W
93.7 W
93.1 W
93.3 W
90.4 W
93.7 W
91.5 W
92.8 W

Table C1 (cont’d.)

Montana

Nebraska

Nevada

New Hampshire
New Jersey

New Mexico

SAVERTON_L_&_D_22
SEDALIA_WATER_PLAN
SHELBINA
SPICKARD_7_W
Springfield
SPRINGFIELD_WSO_AP
St Louis
STEELVILLE_2_N
STEFFENVILLE
ST_CHARLES
ST_LOUIS_WSCMO_AIR
SWEET_SPRINGS
TRENTON

UNION

VANDALIA

VERSAILLES

VIEN NA__2_W NW
WAPPAPELLO_DAM
WAYNESVILLE_2_W
WEST_PLAINS

WILLOW_SPRG_RADIO_

Billings
Glasgow
Great Falls
Havre
Helena
Kalispell
Lewiston
Miles City
Missoula

Grand Island
Lincoln
Norfolk
North Platte
Omaha

Ely

Las Vegas

Reno
Winnemucca
Mount Washington
New York

Roswell
Santa Fe

180

39.6 N
38.7 N
39.7 N
40.3 N
37.2 N
37.2 N
38.8 N
38.0 N
40.0 N
38.8 N
38.8 N
39.0 N
40.1 N
38.5 N
39.3 N
38.4 N
38.2 N
36.9 N
37.8 N
36.7 N
37.0 N

45.8 N
48.2 N
47.5 N
48.6 N
46.6 N
48.3 N
46.4 N
46.4 N
46.9 N

41.0 N
40.8 N
42.0 N
41.1 N
41.3 N

39.3 N
36.1 N
39.5 N
41.0 N
44.3 N
40.7 N

33.4 N
35.7 N

91.3 W
93.2 W
92.1 W
93.7 W
93.4 W
93.4 W
90.4 W
91.4 W
91.9 W
90.5 W
90.4 W
93.4 W
93.6 W
91.0 W
91.5 W
92.8 W
92.0 W
90.3 W
92.2 W
91.8 W
92.0 W

108.5W
106.6W
111.4 W
109.7W
112.0W
114.3W
117.0 W
105.9W
114.0W

98.3 W
96.8 W
97.4 W
100.7 W
95.9 W

114.9W
115.2 W
119.8W
117.8 W
71.3W
74.0W

104.5 W
105.9 W

Table C1 (cont’d.)

New York

North Carolina

North Dakota

Albany
ALBANY_WSFO_AP

AU RORA_RESEARCH_FA
BATAVIA
BINGHAMPTOM_WSO_AP
BOONVILLE__2_SSW

BU FFALO_WSCMO_AP
CANTON_3_SE
COLDEN_1_N
DANNEMORA
DOBBS_FERRY

ELMIRA

F REDONIA
GLENS_FALLS_AP
HUDSON_CORRECTIONL
INDIAN_LAKE_2_SW
ITHACA_CORNELL_UNI
LAKE_PLAC|D_2_S
LOWVILLE

MASSENA_AP

MINEOLA
MOUNT_MORRIS_2_W
NEW_YORK_LAGUARDIA
NY_WESTERLEIGH_STA
OLD_FORGE
OSWEGO_EAST
POUGHKEEPSIE_FAA_A
RIVERHEAD_RESEARCH
ROCHESTER_WSO_AP
SCARDALE
SLIDE_MOUNTAIN
SPENCER_2_N
STILLWATER_RESERVI
SYRACUSE_WSO_AIRPO
UTICA_FAA_AP
WANAKENA_RANGER_SC
WARSAW_6_SW
WATERTOWN
WATERTOW N_AP
WHITEHALL

AsheviIle
Wilmington

Bismark
EIIendaIe
Fargo
Williston

181

42.7 N
42.8 N
42.7 N
43.0 N
42.2 N
43.5 N
43.0 N
44.5 N
42.7 N
44.7 N
41.0 N
42.2 N
42.5 N
43.3 N
42.3 N
43.8 N
42.5 N
44.3 N
43.8 N
45.0 N
40.7 N
42.7 N
40.8 N
40.7 N
43.7 N
43.5 N
41.7 N
41.0 N
43.2 N
41.0 N
42.0 N
42.3 N
43.8 N
43.2 N
43.2 N
44.2 N
42.7 N
44.0 N
44.0 N
43.5 N

35.6 N
34.3 N

46.8 N
46.0 N
46.9 N
48.2 N

73.8 W
73.8 W
76.7 W
78.2 W
76.0 W
75.3 W
78.7 W
75.2 W
78.7 W
73.7 W
73.8 W
76.8 W
79.2 W
73.7 W
73.8 W
74.3 W
76.5 W
74.0 W
75.5 W
74.8 W
73.7 W
77.8 W
74.0 W
74.2 W
75.0 W
76.5 W
74.0 W
72.7 W
77.7 W
73.8 W
74.5 W
76.5 W
75.0 W
76.2 W
75.3 W
74.8 W
78.2 W
75.8 W
76.0 W
73.3 W

82.5 W
77.9 W

100.8 W

98.5 W
96.8 W

103.6 W

Table C1 (cont’d.)

Ohio

Akron
AKRON_CANTON_WSO_A
ASHLAND_2_SW
ASHTABULA
BARNESVILLE
BELLEFONTAINE
BOWLING_GREEN_WWTP
BUCYRUS_SEWAGE_PLA
CADIZ

CANFIELD_1_S
CELINA_3_NE
CENTERBURG_2_SE
CHARDON

Chicago
CHILO_MELDAHL_L&D
CHIPPEWA_LAKE
CINCINNATI_LUNKEN_
CIRCLEVILLE

Cleveland
CLEVELAND_WSFO_AP
Columbus
COLUMBUS_VLY_CROSS
COLUMBUS_WSO_AIRPO
COSHOCTON_AGR_RES_
COSHOCTON_WPC_PLAN
DANVILLE_2_W

Dayton

DAYTON_MCD
DAYTON_WSO_AP
DEFIANCE

DELAWARE

DORSET_2_E

EATON

ELYRIA_3_E

F INDLAY_FAA_AIRPOR
FINDLAY_WPCC
FRANKLIN_2__W

F REDERICKTOWN_4_S
FREMONT_WATER_WORK
GALLIPOLIS
GREENVILLE_SEWAGE_
HILLSBORO

HIRAM

HOYTVILLE__2_NE

IRWIN

KENTON
LANCASTER_2__NW
LIMA_WWTP

LONDON
MANSFIELD_6_W

182

41.1 N
40.9 N
40.8 N
41.8 N
40.0 N
40.3 N
41.4 N
40.8 N
40.3 N
41.0 N
40.6 N
40.3 N
41.6 N
41.8 N
38.8 N
41.0 N
39.1 N
39.6 N
41.4 N
41.4 N
40.0 N
39.9 N
40.0 N
40.4 N
40.3 N
40.4 N
39.9 N
39.8 N
39.9 N
41.3 N
40.3 N
41.7 N
39.7 N
41.4 N
41.0 N
41.0 N
39.5 N
40.4 N
41.3 N
38.8 N
40.1 N
39.2 N
41.3 N
41.2 N
40.1 N
40.7 N
39.7 N
40.7 N
39.9 N
40.8 N

81.5 W
81.4 W
82.3 W
80.8 W
81.2 W
83.8 W
83.6 W
83.0 W
81.0 W
80.8 W
84.5 W
82.7 W
81.2 W
87.8 W
84.2 W
81.9 W
84.4 W
82.9 W
81.8 W
81.9 W
83.0 W
82.9 W
82.9 W
81.8 W
81.9 W
82.3 W
84.2 W
84.2 W
84.2 W
84.4 W
83.1 W
80.7 W
84.6 W
82.1 W
83.7 W
83.7 W
84.3 W
82.5 W
83.1 W
82.2 W
84.7 W
83.6 W
81.2 W
83.8 W
83.5 W
83.6 W
82.6 W
84.1 W
83.4 W
82.6 W

Table C1 (cont’d.)

Oklahoma

Oregon

MANSFIELD_WSO_AP
MARIETTA_WWTP
MARION_2_N
MARYSVILLE
MC_CONNELSVILLE_LO
MILLPORT__2_NW
MINERAL_RIDGE_WTR_
MONTPELIER

NAPOLEON
NEWARK_WATER_WORKS
NEW_LEXINGTON_2_NW
NEW_PHILADELPHIA
NORWALK_WWTP
OBERLIN
PAINESVILLE_2_N
PANDORA
PAULDING_1_S

Peofia

PHILO__3_SW
PORTSMOUTH_SCIOTOV
PUT-IN-BAY
RIPLEY_EXP_FARM
SANDUSKY
STEUBENVILLE

TIFFIN

Toledo

TOLEDO_BLADE
TOLEDO_EXPRESS_WSO
UPPER_SANDUSKY
URBANA_WWTP
VAN_WERT
WARREN_3_S
WASHINGTON_COURT_H
WAUSEON_WATER_PLAN
WAVERLY

WESTERVILLE
WILMINGTON_3_N
WOOSTER_EXP_STN
XENIA_6_SSE

Youngstown

YOU NGSTOWN_WSO_AP
ZAN ESVILLE_FAA_AIR

Oklahoma City
Tulsa

Astoria
Baker
Burns
Eugene

183

40.8 N
39.4 N
40.6 N
40.2 N
39.7 N
40.7 N
41.2 N
41.6 N
41.4 N
40.1 N
39.7 N
40.5 N
41.3 N
41.3 N
41.8 N
41.0 N
41.1 N
40.7 N
39.8 N
38.8 N
41.7 N
38.8 N
41.5 N
40.4 N
41.1 N
41.6 N
41.7 N
41.6 N
40.8 N
40.1 N
40.8 N
41.2 N
39.5 N
41.5 N
39.1 N
40.1 N
39.5 N
40.8 N
39.6 N
41.2 N
41.3 N
40.0 N

35.4 N
36.2 N

46.2 N
44.8 N
43.6 N
44.1 N

82.5 W
81.4 W
83.1 W
83.4 W
81.8 W
80.9 W
80.8 W
84.6 W
84.2 W
82.4 W
82.2 W
81.4 W
82.6 W
82.2 W
81.3 W
84.0 W
84.6 W
89.7 W
81.9 W
82.9 W
82.8 W
83.8 W
82.7 W
80.6 W
83.2 W
83.6 W
83.5 W
83.8 W
83.3 W
83.8 W
84.6 W
80.8 W
83.4 W
84.2 W
83.0 W
82.9 W
83.8 W
81.9 W
83.9 W
80.7 W
80.7 W
81.9 W

97.6 W
95.9 W

123.9W
117.8W
119.1 W
123.1 W

Table C1 (cont’d.)

Pennsylvania

Rhode Island

South Carolina

South Dakota

Pendleton
Portland
Roseburg
Salem

ALLENTOWN_BETHLEHE
BLOSERVILLE_1_N
CHAMBERSBURG
CLARION_3_SW
CLERMONT_8_SW
CONFLUENCE_1_SW_DA
DUBOIS_FAA_AP
ERIE_WSO_ARPT
FRANKLIN
GRATERFORD_1_E
INDIANA_3_SE
JAMESTOW N_2_NW
KANE_1_NNE
LANDISVILLE
MEADVILLE_1_S
MERCER
MONTROSE_1_E
NEW_CASTLE_1_N
Philadelphia
PHILADELPHIA_WSO_A
PHILIPSBURG_8_E
PHOENIXVILLE_1_E
Pittsburgh
PITTSBURGH_WSCOM_2
PLEASANT_MOUNT_1_W
PUTNEWILLE_2_SE_D
Scranton
SLIPPERY_ROCK
STROUDSBURG_2_E
TIONESTA_2_SE_LAKE
TITUSVILLE_WATER_W
TOBYHANNA
TOWANDA_1_ESE
WAYNESBURG_1_E
WELLSBORO_3_S

Providence

Charleston
Columbia

Huron
Pierre
Rapid City
Sioux Falls

184

45.7 N
45.5 N
43.2 N
44.9 N

40.7 N
40.3 N
40.0 N
41.2 N
41.7 N
39.8 N
41.2 N
42.2 N
41.3 N
40.2 N
40.7 N
41.5 N
41.7 N
40.2 N
41.7 N
41.2 N
41.8 N
41.0 N
39.9 N
39.8 N
41.0 N
40.2 N
40.5 N
40.5 N
41.7 N
41.0 N
41.3 N
41.0 N
41.0 N
41.5 N
41.7 N
41.2 N
41.8 N
39.8 N
41.7 N

41.7N

32.9 N
34.0 N

44.4 N
44.4 N
44.0 N
43.6 N

118.8 W
122.7 W
123.3 W
123.0 W

75.5 W
77.3 W
77.7 W
79.5 W
78.5 W
79.3 W
78.8 W
80.2 W
79.8 W
75.5 W
79.2 W
80.5 W
78.8 W
76.5 W
80.2 W
80.2 W
75.8 W
80.3 W
75.3 W
75.2 W
78.0 W
75.5 W
80.2 W
80.2 W
75.5 W
79.3 W
75.7 W
80.0 W
75.2 W
79.5 W
79.7 W
75.3 W
76.5 W
80.2 W
77.3 W

71.4W

80.1 W
81.1 W

98.2 W
100.3 W
103.1 W

96.7 W

Table C1 (cont’d.)

Tennessee

Washington

Wisconsin

Texas

Utah

Virginia

Nashville

Abilene
Amarillo
Austin
Brownsville
Dallas

El Paso
Houston
Lubbock
VVaco
Wichita Falls

Milford
Modena
Salt Lake City

Norfolk
Richmond
Wytheville

North Head
Seattle
Spokane
Tacoma
Tatoosh Island
Walla Walla
Yakima

ALMA_DAM_4
AMERY_2_N
ANTIGO_1_SSW
APPLETON
ARLINGTON_EXP_FARM
ASHLAND_EXP_FARM
BARABOO_WATER_WORK
BAYFIELD_6_N
BEAVER_DAM
BELOIT_COLLEGE
BLAIR
BLOOMER_CITY_HALL
BOWLER_RANGER_STN
BREED_6_SSE
BRODHEAD_1_SW
BURUNGTON
CHARMANY_FARM
CHILTON_SEWAGE_PLA
CLINTONVILLE_SEWAG
CRIVITZ_HIGH_FALLS
CUMBERLAND

185

36.1 N

32.4 N
35.2 N
30.3 N
25.9 N
32.8 N
31.8 N
29.8 N
33.7 N
31.6 N
33.9 N

38.4 N
37.8 N
40.8 N

36.8 N
37.5 N
36.9 N

46.3 N
47.5 N
47.6 N
47.3 N
48.4 N
46.0 N
46.6 N

44.3 N
45.3 N
45.1 N
44.3 N
43.3 N
46.6 N
43.5 N
46.9 N
43.5 N
42.5 N
44.3 N
45.1 N
44.9 N
45.0 N
42.6 N
42.7 N
43.0 N
44.0 N
44.6 N
45.3 N
45.5 N

86.7 W

99.7 W
101.7 W
97.7 W
97.4 W
96.8 W
106.4 W
95.4 W
101.8 W
97.2 W
98.5 W

113.0W
113.9W
111.9W

76.2 W
77.3 W
81.1 W

124.1 W
122.3 W
117.5 W
122.4 W
124.7 W
118.3 W
120.5 W

91.9 W
92.4 W
89.2 W
88.4 W
89.3 W
91.0 W
89.7 W
90.8 W
88.8 W
89.0 W
91.2 W
91.5 W
89.0 W
88.4 W
89.4 W
88.3 W
89.5 W
88.2 W
88.8 W
88.2 W
92.0 W

Table C1 (cont’d.)

DALTON

DANBURY

DARLINGTON

DODGE

DODGEVILLE
EAU_CLAIRE_FAA_AIR
ELLSWORTH_1_E
FAIRCHILD_RANGER_S
FOND_DU_LAC
FORT_ATKINSON_2_SS
FOXBORO
GENOA_DAM_8
GERMANTOW N_2_W
GOODMAN

GORDON

GRANTSBURG
GREEN_BAY_WSO_AIRP
GURNEY
HANCOCK_EXP_FARM
HARTFORD_SEWAGE__PL
HATFIELD_DAM
HILLSBORO_SEWAGE_P
HOLCOMBE_1_W
JUMP_RIVER_1_ESE
KENOSHA

KEWAUNEE
LAKE_GENEVA
LAKE_MILLS
LANCASTER_4_WSW
LAONA_6_SW
LA_CROSSE_WSO_AIRP
LONG_LAKE_DAM
LYNXVILLE_DAM_9
MADELINE_ISLAND
Madison
MADISON_WSO_AIRPOR
MANITOWOC
MARINETTE
MARSHFIELD_EXP_FAR
MATHER_3_NW
MAUSTON
MEDFORD_1_SW
MELLEN_4_NE
MENOMONIE_SEWAGE_P
MERRILL
MILWAUKEE_MT_MARY_
MILWAUKEE_WSO
MINOCQUA_DAM
MONDOVI

MONTELLO

186

43.7 N
46.0 N
42.7 N
44.1 N
43.0 N
44.9 N
44.7 N
44.6 N
43.8 N
42.9 N
46.5 N
43.6 N
43.2 N
45.6 N
46.3 N
45.8 N
44.5 N
46.5 N
44.1 N
43.3 N
44.4 N
43.7 N
45.2 N
45.4 N
42.5 N
44.4 N
42.6 N
43.1 N
42.8 N
45.5 N
43.9 N
45.9 N
43.2 N
46.8 N
43.1 N
43.1 N
44.1 N
45.1 N
44.7 N
44.2 N
43.8 N
45.1 N
46.4 N
44.9 N
45.2 N
43.1 N
43.1 N
45.9 N
44.6 N
43.8 N

89.2 W
92.4 W
90.1 W
91.6 W
90.1 W
91.5 W
92.5 W
91.0 W
88.4 W
88.8 W
92.3 W
91.2 W
88.1 W
88.3 W
91.8 W
92.7 W
88.1 W
90.5 W
89.5 W
88.4 W
90.7 W
90.3 W
91.1 W
90.8 W
87.8 W
87.5 W
88.4 W
88.9 W
90.8 W
88.8 W
91.3 W
89.1 W
91.1 W
90.7 W
89.3 W
89.3 W
87.7 W
87.6 W
90.1 W
90.4 W
90.1 W
90.3 W
90.6 W
91.9 W
89.7 W
88.0 W
87.9 W
89.7 W
91.7 W
89.3 W

Table C1 (cont’d.)

NECEDAH
NEILLSVILLE_3_SW
NEWALD_4_N
NEW_LONDON
NORTH_PELICAN
OCONOMOWOC_1_SW
OCONTo_4_W
OSHKOSH

OWEN

PARK_FALLS
PLATTEVILLE
PLYMOUTH

PORTAGE
PORT_WASHINGTON
PRAIRIE_DU_CHIEN
PRAIRIE_DU_SAC_2_N
PRENTICE_NO._2
RACINE
RAINBOW_RESERVOIR
REST_LAKE
RHINELANDER_WATER_
RICE_LAKE
RICHLAND_CENTER
RIDGELAND
RIVER_FALLS
ROSHOLT_9_NNE
SHAWANO_2_SSW
SHEBOYGAN
SOLON_SPRINGS
SPOONER_EXPERMNT_F
STANLEY
STEVENs_POINT

STU RGEON_BAY_EXP_F
ST_CROIX_FALLS
SUPERIOR
TREMPEALEAU_DAM_6
TWO_RIVERS
VIROQUA_2_NW
WASHINGTON_ISLAND_
WATERTOWN
WAUPACA
WAUSAU_AIRPORT
WEST_BEND
WEYERHAUSER
WHITEWATER
WILLOW_RESERVOIR
WINTER_5_NW
WISCONSIN_DELLS
WISCONSIN_RAPIDs

187

44.0 N
44.5 N
45.8 N
44.4 N
45.6 N
43.1 N
44.9 N
44.0 N
45.0 N
45.9 N
42.8 N
43.8 N
43.5 N
43.4 N
43.0 N
43.3 N
45.5 N
42.7 N
45.8 N
46.1 N
45.6 N
45.5 N
43.3 N
45.2 N
44.9 N
44.8 N
44.8 N
43.8 N
46.3 N
45.8 N
45.0 N
44.5 N
44.9 N
45.4 N
46.7 N
44.0 N
44.2 N
43.6 N
45.4 N
43.2 N
44.3 N
44.9 N
43.4 N
45.4 N
42.8 N
45.7 N
45.9 N
43.6 N
44.4 N

90.1 W
90.6 W
88.7 W
88.7 W
89.3 W
88.5 W
87.9 W
88.6 W
90.5 W
90.4 W
90.5 W
88.0 W
89.4 W
87.9 W
91.2 W
89.7 W
90.3 W
87.8 W
89.6 W
89.9 W
89.4 W
91.7 W
90.4 W
91.9 W
92.6 W
89.3 W
88.6 W
87.7 W
91.8 W
91.9 W
90.9 W
89.6 W
87.3 W
92.7 W
92.0 W
91.4 W
87.6 W
90.9 W
86.9 W
88.7 W
89.1 W
89.6 W
88.2 W
91.4 W
88.7 W
89.8 W
91.1 W
89.8 W
89.8 W

Table C1 (cont’d.)

Wyoming

Casper
Cheyenne
Lander

Sheridan
Yellowstone Park

42.9 N
41.2 N
42.8 N
44.8 N
45.0 N

106.5 W
104.8 W
108.7 W
107.0 W
110.7 W

 

188

APPENDIX D

189

 

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193

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194

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