using population structure and phenology to …
TRANSCRIPT
The Pennsylvania State University
The Graduate School
College of Agricultural Sciences
USING POPULATION STRUCTURE AND PHENOLOGY TO ADVANCE INSECT
MANAGEMENT IN DIVERSIFIED VEGETABLE AGROECOSYSTEMS
A Dissertation in
Entomology
by
Amanda C. Bachmann
2012 Amanda C. Bachmann
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
December 2012
The dissertation of Amanda C. Bachmann was reviewed and approved* by the following:
Shelby J. Fleischer
Professor of Entomology
Dissertation Advisor
Chair of Committee
Michael Saunders
Professor of Entomology
John Tooker
Assistant Professor of Entomology
Douglas Miller
Associate Professor of Geography
Andrew Michel
Assistant Professor of Entomology
The Ohio State University
Special Member
Gary Felton
Department Head, Professor of Entomology
*Signatures are on file in the Graduate School
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ABSTRACT
Population structure and phenology can both be used to advance insect management in
diverse vegetable agroecosystems. I describe the alate aphid species composition in Northeastern
US processing snap beans and update the list of aphid species found in Pennsylvania. The alate
aphid community in Pennsylvania snap bean fields is diverse and contains members that are
efficient vectors of economically important plant viruses. One of these aphids, Aphis glycines, is
largely present in the state as a result of migration and dispersal from areas with high densities of
its overwintering host, Rhamnus cathartica. Using genetic tools and air-flow trajectory models to
investigate the natal sources of A. glycines in Pennsylvania, I found A. glycines populations
sampled in Pennsylvania had high levels of genotypic diversity. This is indicative of being
sourced from many natal populations. They were also genetically similar to some populations in
the Midwest. Matching A. glycines clones were found between PA, NY and VA indicating some
level of long distance movement, which I attempted to model using air-flow trajectories. In
addition to working with aphids, I set out to validate early season activity and growing season
phenology models for three pests of cucurbits on land that is transitioning to organic production
on research farms in Pennsylvania, Iowa and Kentucky. Modeling the phenology of the striped
cucumber beetle and squash bug was challenging due to the fact that they overwinter as adults. I
was able to demonstrate a successful early season activity monitoring tool, and used air-
temperature degree-days from weather stations and development data from previous studies on
both insects to estimate the number of field generations and discuss challenges for their control. I
also described the phenology of the squash vine borer in geographic areas not represented in
previous studies.
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TABLE OF CONTENTS
Abstract ……………………………………………………………………………………...…iii
Table of Contents ………………………………………………………………………………iv
List of Figures ………………………………………………………………………..…..…….vi
List of Tables …………………………………………………………………………………..ix
Acknowledgements …………………………………………………………………..….....…..xi
Chapter 1 Introduction ............................................................................................................. 1
Aphids .............................................................................................................................. 4 Aphids as vectors of plant viruses .................................................................................... 7 Aphids and snap beans ..................................................................................................... 8 Introduced species ............................................................................................................ 9 Insect movement .............................................................................................................. 10 Phenology and pest management ..................................................................................... 13 Dissertation Objectives .................................................................................................... 16 References ........................................................................................................................ 17
Chapter 2 Alate aphid species composition in Northeastern US processing snap beans and
an update to historical lists ............................................................................................... 20
Introduction ...................................................................................................................... 20 Methods ............................................................................................................................ 24
Slide mounting protocol ........................................................................................... 25 Results .............................................................................................................................. 25 Discussion ........................................................................................................................ 27 References ........................................................................................................................ 28 Figures and Tables ........................................................................................................... 31
Chapter 3 Estimating natal sources of Aphis glycines using molecular markers and
airflow trajectories ........................................................................................................... 43
Introduction ...................................................................................................................... 43 Aphis glycines life history ........................................................................................ 43 Factors limiting the range of Aphis glycines ............................................................ 44 Aphis glycines in Pennsylvania ................................................................................ 46 Soybean management ............................................................................................... 46 Aerobiology .............................................................................................................. 47 Molecular tools for population identification ........................................................... 49 Objectives ................................................................................................................. 50
Methods ............................................................................................................................ 50 Field collection ......................................................................................................... 50 Laboratory Methods ................................................................................................. 51 Statistical Methods ................................................................................................... 51 HYSPLIT methods ................................................................................................... 52
Results .............................................................................................................................. 53 Spatial ....................................................................................................................... 53 Temporal .................................................................................................................. 55
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Aerobiology .............................................................................................................. 56 Discussion ........................................................................................................................ 56 References ........................................................................................................................ 58 Figures and Tables ........................................................................................................... 62
Chapter 4 Speciation and population structure of Therioaphis trifolii .................................... 74
Introduction ...................................................................................................................... 74 Methods ............................................................................................................................ 75 Results .............................................................................................................................. 75 Discussion ........................................................................................................................ 76 References ........................................................................................................................ 76 Figures and Tables ........................................................................................................... 78
Chapter 5 Phenology model validation of pests of cucurbits ................................................... 80
Introduction ...................................................................................................................... 80 Methods ............................................................................................................................ 83
Early season activity................................................................................................. 83 In-season phenology ................................................................................................. 85 Squash Vine Borer ................................................................................................... 86 Meteorological Data ................................................................................................. 86
Results .............................................................................................................................. 88 Striped Cucumber Beetle ......................................................................................... 88 Squash Bug ............................................................................................................... 89 Squash Vine Borer ................................................................................................... 90
Discussion ........................................................................................................................ 90 References ........................................................................................................................ 94 Figures and Tables ........................................................................................................... 96
Chapter 6 Conclusions ............................................................................................................. 111
References ........................................................................................................................ 119 Appendix HYSPLIT Screenshots ................................................................................... 121
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LIST OF FIGURES
Figure 2-1. Individual-based rarefaction curves showing aphid species accumulation in
PA and NY. ...................................................................................................................... 41
Figure 2-2. Individual-based rarefaction curve showing aphid species accumulation from
the combining of samples from PA and NY (solid line) and the 95% confidence
intervals for the curve (dashed lines). .............................................................................. 42
Figure 2-3. Proportion of aphids from the pan trapping collection in PA and NY that use
herbaceous plants, trees, or crops as primary hosts. Host associations for North
America characterized from Blackman and Eastop (1994, 2000, and 2006). .................. 42
Figure 3-1. Map of A. glycines collection locations. Sites with a black circle were used in
2009. Sites with a black diamond were used in 2010. Sites with a black star are 2009
collections from Orantes et al (2012). Rock Springs was used in both years. ................. 68
Figure 3-2. Principal component analysis based on Fst of the aphid populations collected
in Pennsylvania, New York, and Virginia 2009 showing spatial (primary axis) and
temporal (secondary axis) differentiation. The dotted lines group the populations that
separated spatially. ........................................................................................................... 69
Figure 3-3. Principal component analysis based on Fst of the aphid populations collected
in Pennsylvania, New York, and Canada in 2010 showing spatial (primary axis) and
temporal (secondary axis) differentiation. The dotted lines group the populations that
separated spatially. ........................................................................................................... 70
Figure 3-4. Principal component analysis based on Fst of populations collected in
Pennsylvania, New York, Virginia, and the Midwest in 2009 showing spatial
(primary axis) and temporal (secondary axis) differentiation. The dotted lines group
the populations that separated spatially. ........................................................................... 71
Figure 3-5. Principal component analysis based on Fst of populations collected in
Pennsylvania, New York and Canada in 2010 and the Midwest sites 2009 showing
spatial (primary axis) and temporal (secondary axis) differentiation. The dotted lines
group the populations that separated spatially. ................................................................ 72
Figure 3-6. Examples of HYSPLIT forward trajectory maps over a 48 hour time period,
clockwise from top left; PA to VA 7/13/2009 (score 0.67), NY to VA 7/8/2009
(score 0.11), PA to NY 7/10/2009 (score of 0.52), and PA 7/12/2009 (score of 0). ........ 73
Figure 4-1. The relationship between haplotypes derived using the LR primer for T.
trifolii collected in Pennsylvania and Serbia. In the web, each line as well as each
small dot is one base pair of difference. ........................................................................... 78
Figure 4-2. The relationship between haplotypes derived using the SR primer for T.
trifolii collected in Pennsylvania and Serbia. In the web, each line as well as each
small dot is one base pair of difference. ........................................................................... 79
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Figure 5-1. Gompertz curve showing early season recruitment predictions for PA and KY
2010 – 11 using cumulative degree days base 55 F for Acalymma vittatum. ................... 99
Figure 5-2. Gompertz curve showing early season recruitment predictions for PA 2010 -
2012 using calendar day for Acalymma vittatum. ............................................................ 100
Figure 5-3. Gompertz curve showing early season recruitment predictions for KY 2010 -
2012 using calendar day for Acalymma vittatum. ............................................................ 101
Figure 5-4. Average number of A. vittatum per plant during the growing season for PA
2011 with overlay showing the biofix and projected development times for the first
and second field generations ............................................................................................ 102
Figure 5-5. Average number of A. vittatum per plant during the growing season for
Kentucky with overlay showing the biofix and projected development .......................... 103
Figure 5-6. Average number of A. vittatum per plant during the growing season for Iowa
with overlay showing the biofix and projected development. ......................................... 104
Figure 5-7. Average number of squash bug adults, juveniles and egg masses per plant
during the growing season for Pennsylvania. The horizontal bar shows the biofix and
projected egg to adult development time, and the solid vertical lines indicate the
critical photperiod for diapause induction. ....................................................................... 105
Figure 5-8. Average number of squash bug adults, juveniles and egg masses per plant
during the growing season for Kentucky. The horizontal bar shows the biofix and
projected egg to adult development time, and the two vertical lines indicate the
timing of the critical photperiod for diapause induction. ................................................. 106
Figure 5-9. Average number of A. tristis adults, juveniles and egg masses per plant during
the growing season for Iowa. The horizontal bar shows the biofix and projected egg
to adult development time, and the two vertical lines indicate the timing of the
critical photperiod for diapause induction. ....................................................................... 107
Figure 5-10. Observed and predicted accumulation of A. vittatum in Pennsylvania during
the growing season. Predicted lines were generated using the equation y =
0.9913*exp(-exp(3.3906 – 0.014*x)) where x is cumulative degree days base 55 F.
The division between first and second field generation was estimated as 793.6
cumulative degree days base 55 F after the biofix. .......................................................... 108
Figure 5-11. Observed and predicted accumulation of A. vittatum in Iowa during the
growing season. Predicted lines were generated using the equation y = 0.9913*exp(-
exp(3.3906 – 0.014*x)) where x is cumulative degree days base 55 F. The division
between first and second field generation was estimated as 793.6 dd55 after the
biofix. ............................................................................................................................... 109
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Figure 5-12. Observed and predicted accumulation of A. vittatum in Kentucky during the
growing season. Predicted lines were generated using the equation y = 0.9913*exp(-
exp(3.3906 – 0.014*x)) where x is cumulative degree days base 55 F. The division
between first and second field generation was estimated as 793.6 dd55 after the
biofix. ............................................................................................................................... 110
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LIST OF TABLES
Table 2-1. Alate aphid species representing > 1 % of the capture from water pan traps in
commercial snap bean fields in PA (2004 – 2006) and NY (2002 – 2006). Derived
from Table 1 in Nault et al (2009). .................................................................................. 31
Table 2-2. Species of alate aphids with host associations, collected from water pan traps
in commercial snap bean fields in PA (2004 – 2006) and NY (2002 – 2006), and
from similar traps in orchards in central PA (2003-2004, Wallis et al. 2005). Host
associations for North America from Blackman and Eastop (1994 [AWT], 2000
[AWC], and 2006 [HPS]). ................................................................................................ 32
Table 2-3. New aphid records from PA reported in Nault et al (2009) and/or Wallis et al
(2005) but not found in Pepper (1965). ............................................................................ 35
Table 2-4. Species in six subfamilies of the family Aphididae occurring in PA. .................... 35
Table 2-5. Species in the subfamily Aphidinae, tribe Macrosiphini occurring in PA ............. 36
Table 2-6. Species in the subfamily Aphidinae, tribe Aphidini occurring in PA .................... 38
Table 2-7. Species in the subfamily Calaphidinae occurring in PA ........................................ 39
Table 2-8. Species in the subfamily Chaitophorinae occurring in PA. .................................... 39
Table 2-9. Species in the subfamily Drepanosiphinae occurring in PA .................................. 39
Table 2-10. Species in the subfamily Eriosomatinae occurring in PA .................................... 40
Table 2-11. Species in the subfamily Lachninae occurring in PA ........................................... 40
Table 3-1. Aphid collection dates and locations for 2009 and 2010. ....................................... 62
Table 3-2. Fst values for Pennsylvania, Virginia and New York 2009. None of the Fst
values were significant at P < 0.05................................................................................... 62
Table 3-3. Fst values for Pennsylvania (all collection dates combined), Virginia and New
York 2009. Significant values (P < 0.05) indicated in bold. ............................................ 62
Table 3-4. Genotypic diversity for aphid populations collected in Pennsylvania, Virginia,
and New York 2009. ........................................................................................................ 63
Table 3-5. Fst values for Pennsylvania, Virginia, New York and Midwest sites 2009.
Significant values (P < 0.05) indicated in bold. ............................................................... 64
Table 3-6. Fst values for Pennsylvania, Canada, New York (2010) and Midwest sites
2009. Significant values (P < 0.05) indicated in bold. ..................................................... 65
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Table 3-7. Fst values for PA, NY, and sites in Canada from 2010 collections. Significant
values (P < 0.05) indicated in bold................................................................................... 66
Table 3-8. Genotypic diversity for aphid populations collected in Pennsylvania, Canada,
and New York 2010. ........................................................................................................ 66
Table 3-9. Score from 0 to 1 of forward trajectories that cross the target location from the
HYSPLIT maps. 142 date/location scenarios were evaluated and dates where none
of the trajectories crossed the target location (score of 0) are not shown on this table.
Maps were generated from July 1-31 2009 for PA to VA and PA to NY, and July 1 –
August 9 2009 for NY to VA and NY to PA. .................................................................. 67
Table 5-1: Early season recruitment monitoring dates and locations for 2010 - 2012 in
KY, IA, and PA. ............................................................................................................... 96
Table 5-2: Summary of life history parameters for Acalymma vittatum, Anasa tristis and
Melittia cucurbitae from the literature (see footnotes) and this study (*). ....................... 96
Table 5-3. Mean calendar day and cumulative degree day of first capture for Acalymma
vittatum (SCB) and Anasa tristis (SB) on trap flats. N is the number of years. ............... 97
Table 5-4. Mean calendar day and cumulative degree day of first capture for Acalymma
vittatum (SCB) and Anasa tristis (SB) in the phenology plot. N is the number of
years. ................................................................................................................................ 97
Table 5-5. Mean calendar day and cumulative degree day base 50 F of first capture for
Melittia cucurbitae ........................................................................................................... 97
Table 5-6. Parameter estimates for the Gompertz equations modeling early season
recruitment of Acalymma vittatum to trap flats using degree day. ................................... 98
Table 5-7. Parameter estimates for the Gompertz equations modeling early season
recruitment of Acalymma vittatum to trap flats using calendar day. ................................ 98
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ACKNOWLEDGEMENTS
I would like to thank my advisor Shelby Fleischer and my committee; Andy Michel,
Doug Miller, Mike Saunders, and John Tooker for their guidance and advice during my time at
Penn State. Special thanks go to Andy Michel and his lab for their hospitality and assistance for a
significant portion of my soybean aphid data analysis efforts.
Thanks also go out to my officemates (Lori Shapiro and Eric Bohnenblust), and assorted
entoids (Kerry Mauck, Sean Halloran, Alexis Barbarin, Christy Harris, and Emily Kuhns) for
their friendship and support. A special shout-out goes to Tracy Conklin, my entomology sister
who shared her love for Canadian television, gardening, hatchbacks, and culinary adventures with
me. Ride forever.
Thank you to the Department of Entomology and the faculty and staff therein for
supporting me academically and financially.
The support of my family was integral to my completion of this degree and I would like
to thank; my husband, Josh, my parents, Darlene and Clarence, my sister Jaime and her husband
Doug, Uncle Les, Aunt Ardie and Uncle Roe, Megan and Pat, and Dee for their support.
This dissertation is dedicated to my late grandmother, Frances Keller, who always asked
me if I was still “working with bugs.”
Chapter 1
Introduction
Pennsylvania is part of the diverse forested Northeastern landscape. Farms in this region
compete with urbanization for deforested land, resulting in a varied patchwork of land-use
patterns. Land-use patterns on these farms are dominated by field crops, but vegetable crops
(both fresh market and processing) are important. For example, the state is one of the top ten
producers of fresh market sweet corn ($35.5 million in 2009), processing snap beans, fresh-
market peppers, fresh-market tomatoes, pumpkins, and fresh-market squash. Surveys
representing the approximately 4,300 vegetable farms reporting in the state census reveal from 5
to 20 crops on a given farm, and often multiple cultivars and planting dates of several of the crop
species. Generally, these farms are small (median farm size in Pennsylvania is 65 acres) and
comprised of multiple small fields (USDA NASS, 2009). Furthermore, both fresh and processing
markets are relevant. Fresh market vegetables are integral to the local economy in many areas of
Pennsylvania in the form of local farm stands, farmers’ markets, or community supported
agriculture organizations. The majority of farmers utilize some form of fresh-marketing outlet,
and within this sector, organic production is increasing.
Pest management in vegetable crops is challenging due to this diversity of crops, the
diversity of pests that can affect them, and the diversity of marketing systems. Vegetable crops in
diverse landscapes are particularly challenging because they are often not the dominant crop and
thus subject to control measures around them. For example, European corn borer (Ostrinia
nubilalis) is predominantly a pest of corn, but can also damage green peppers. If surrounding corn
2
fields are inadequately managed or inhospitable to ECB (in the case of Bt varieties) nearby farms
with a crop of peppers are at risk.
In a diverse landscape comprised of many small farms and fields, pest management
cannot focus on one pest at a time, and must also consider movement at multiple scales from
plant to plant, to state to state. At a local level, growers need to know what pests are in their field
(accomplished by in-field scouting or monitoring traps) to optimize treatment plans. Pest
populations in one field can move into adjacent fields under their own volition or they can
sometimes be transported by stronger forces, like prevailing winds and weather systems. This
ability to move long distances makes state-wide and regional pest monitoring networks important
for alerting growers to pest issues that might impact them in the near future.
Insect management in vegetable crops must often also consider the potential of vectoring
of plant pathogens. The definition of an insect species as a pest rises, and can expand to multiple
crops, if that species has the capacity to vector a pathogen. Many vegetable crops grown in PA
are susceptible to vectored pathogens. Examples include snap beans and a host of aphid-vectored
viruses (including cucumber mosaic, bean yellow mosaic, clover yellow vein), and cucurbits and
the bacterial pathogens Erwinia tracheiphila and Serratia marcescens (vectored by cucumber
beetles and squash bugs, respectively).
Habitat diversity encourages populations of beneficial insects and pollinators, but can
also serve as a reservoir for pests and plant diseases. Diversified landscapes are home to
increasingly diverse communities of beneficial insects, pests, and the plant diseases associated
with them. Insect communities are constantly changing due to migration and dispersal of new
species and selection due to changing management practices. Conventional and organic farms
will have different pest challenges because of the different control methods available to each of
them. With the increased interest in organic production, growers are facing challenges in
controlling insect pests without conventional insecticides.
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A challenge, then, becomes one of contributing useful information to an agricultural
system comprised of multiple crops and multiple cultivars. I employ two avenues. My first
avenue towards advancing insect management in diverse vegetable cropping systems is to
consider two processes that are influencing insect management in most agricultural systems:
population dynamics of introduced non-native species, and population phenology. In the first
case, my focus is on research contributions to define population genetic structure, with the aim of
also contributing to knowledge about movement and speciation. In the second, my focus is on
development and validation studies that will contribute towards development of Extension
methods. In all cases, I am working with insect species that are important, in part, because of
their capacity to vector plant pathogens in vegetable crops.
Second, I propose to contribute work relevant to two crops: snap beans and cucurbits.
Taking the top 20 crops listed by vegetable growers in surveys, you can group them into four
plant families. One, the grasses, includes only one species - sweet corn. A second, the legumes,
includes multiple types of beans. By including either a grass or legume, I am working in a
processing crop and with plant species and insect communities that extend readily into the larger
field-crop dominated land-use pattern. The second two plant families - Solanaceae and
Cucurbitaceae – tend to be vegetable crops that harbor insect communities predominantly
comprised of specialists perhaps due to their many defensive compounds aimed at preventing
herbivory. Within Cucurbitaceae, the genera Cucurbita, Cucumis, and Citrulis each serve as the
host to specialist pests that preferentially consume them over one of the other genera. In this
thesis, I am focusing on two vegetable crops important to Pennsylvania growers; snap beans, and
cucurbits. Snap beans are economically valuable mainly as a processing crop. In 2009, 27,660
tons were harvested for processing at a value of over $7 million (USDA NASS, 2009). Cucurbit
crops, in Pennsylvania, are comprised of winter and summer squashes, pumpkins, melons, and
cucumbers. Organic cucurbit production is increasing as consumers seek out a wider range of
4
organic produce grown in their home state. By choosing these two crops, I am learning about and
contributing towards the advancement of insect management to two sectors of vegetable crops in
Pennsylvania.
Leaving the description of vegetable production in Pennsylvania, this introduction will
cover the major themes and systems included in this dissertation beginning with an overview of
the aphids.
Aphids
Aphids are prevalent in many areas of the world. As a group, aphids appeared
approximately 170 -150 MYA in the Early to Late Jurassic and their radiation from conifers to
angiosperms occurred approximately 40 MYA (Grimaldi and Engel, 2005). There are
approximately 4800 described species, with 1400 (~29%) of them present in North America.
Surprisingly, aphid biodiversity is greater in temperate regions than in tropical areas. This is the
opposite trend from other arthropods, and Dixon et al. (1987) hypothesized that it may be the case
because aphids are very host specific, inefficient at finding hosts, and their method of
reproduction is costly, thus requiring prolonged feeding. They are phloem-feeders, and must
spend an extensive amount of time feeding in order to get adequate nutrition from their host
plants. Most aphid species feed on very specific hosts, with one of the most polyphagous (Myzus
persicae) being recorded on less than one percent of all plant species (Blackman and Eastop,
2000).
Aphids are notable for their ability to reproduce asexually (parthenogenesis). Their
lifecycles are described in two ways – whether or not they alternate hosts and whether or not they
reproduce sexually at any time during their life cycle. Aphids are either heteroecious or
monoecious, meaning that they alternate hosts (heteroecious, ~10% of species [Grimaldi and
5
Engel, 2005]) or stay on one host (monoecious) throughout the year. Reproduction is defined as
either holocyclic (asexual reproduction interrupted by a sexual phase) or anholocyclic
(reproducing only parthenogenetically). Aphid host plants are described as being primary (the
host where sexual reproduction occurs if the species is holocyclic, or the host where the entire
lifecycle occurs if the species is anholocyclic) or secondary (the host where asexual reproduction
occurs if the species is holocyclic).
Female aphids that give birth to live young are called viviparae. The young are clones of
the parent aphid. The female sexual morphs that lay eggs are called oviparae. The eggs
overwinter on the primary host and when they hatch, the resulting apterous (wingless) females are
called fundatrices, as they are the founders of the colony. The ability of aphids to give birth to
live young and their short generation time (as little as 5 days), means that aphid populations can
increase rapidly on a host plant. With this rapid increase, crowding, and declining host quality
due to the aphid feeding or due to changes in host development, apterous females will produce
young that mature into alate (winged) adults. These adult females leave the crowded or lower
quality host in search of a new host plant. Male alate sexual morphs are produced in late summer
or early fall along with the female oviparae possibly due to photoperiod cues, host effects or an
interaction of factors. These two morphs return to the primary host, where sexual reproduction
occurs.
Aphids are relatively weak fliers. They are capable of directional flight, but are also small
enough that they can get lifted by updrafts and carried by weather systems. When landing on a
potential host plant, they use a tasting probe where they insert their stylet (mouthpart derived
from the mandible and maxilla) into the epidermal layer of the plant and ingest some of the fluid
from the punctured epidermal cell (Ng and Faulk, 2006). If the host is suitable, they will
commence with a feeding probe in which the stylet is threaded between the epidermal cells and
into the vascular tissues of the plant. The feeding probe takes much longer to initiate, and once
6
the stylet is in place the aphid will remain sessile for long periods of time. If disturbed during
feeding, the aphid has to extricate its stylet carefully or risk damaging it.
In the ecosystem, aphids are a plentiful source of food for polyphagous arthropod
predators and a source of sugar (in the form of the honeydew they excrete) for ants, fungi, and
even native birds (Grimaldi and Engel, 2005). Coccinelids, syrphids, lacewings, and other
predaceous Hemipterans are some of the insects that will prey on aphid colonies. These generalist
predators are well studied in agricultural systems as a means of biocontrol for aphids. Ant
interactions with aphids can be symbiotic (ants tending aphids, actively protecting them from
predators or the elements) or more casual (no active tending or protection but still consuming the
honeydew). Some ants will move aphids to different parts of a plant or even actively defend their
colonies from predators. Aphids are good continual sources of honeydew (excreted plant phloem)
since they must feed almost constantly to extract enough amino acids from the phloem.
Aphids host on trees, shrubs, herbaceous plants and crops. They are most economically
important as crop pests, with some species being highly polyphagous and impacting multiple
crops. In addition to the feeding damage associated with aphids removing phloem, the honeydew
they excrete onto plants creates ideal conditions for the growth of sooty mold.
Some aphids are gall formers, inducing host plants to make protective enclosures for their
colonies out of their host plants. There are even a few species that make soldier morphs with
enlarged front legs to defend colonies from attack.
Aphids have intracellular symbiotic bacteria from the genus Buchnera. Buchnera
produces amino acids for the aphid that it cannot get from phloem feeding alone. The bacteria is
also associated with producing proteins necessary for some viruses to be transmitted by aphids
7
Aphids as vectors of plant viruses
In addition to their feeding damage due to the removal of phloem on heavily infested
plants, aphids can also damage plants by being efficient vectors of plant viruses. There are two
general types of relationships between aphids and the viruses they transmit. The first type is
persistent transmission, which can be replicative or non-replicative. Viruses that are persistently
transmitted are obtained by an aphid from infected tissue during a feeding episode. The virus
moves through the stylet and enters the aphid’s digestive system, eventually passing through the
gut lining (and in the case of replicative viruses, multiplying in the midgut cells) and entering the
hemocoel (Gray and Banerjee, 1999). The virus must return to the salivary glands to infect a new
host. From the salivary glands, the virus is injected into another plant when the aphid feeds again.
Persistent viruses are notable for not necessarily being detrimental to the aphid, but once infected
the aphid will vector them for life (Gray and Banerjee, 1999).
The second type of virus transmission is non-persistent. These viruses are obtained
quickly by their aphid vector during short tasting probes which only puncture the epidermal cells
and last a few seconds or minutes versus the hours necessary for feeding. The viruses stick to the
stylet lining by binding to helper component proteins (a protein that binds to the stylet wall and
the virus coat protein) or directly to the stylet depending on the type of virus (Ng and Falk, 2006).
They will remain there until they are flushed out during another tasting probe, which from the
perspective of the virus would ideally be on a new host. Non-persistently transmitted viruses are
vectored for only a short time, minutes to hours, by the aphid due to their association with the
stylet lining (Ng and Falk, 2006). Both types of aphid/virus relationship can be economically
important. For example, aphids can vector cucumber mosaic virus (CMV) in a non-persistent
manner, which causes serious damage to cucurbits and legumes.
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Aphids and snap beans
Aphids are rarely direct pests of snap beans in Pennsylvania, but they are important
vectors of plant viruses. Snap beans are a relatively short season crop with multiple plantings.
The shorter growing time means that few plantings will have sufficient aphid populations develop
to damaging levels, however disease transmission as the alates migrate through fields is still a
risk. In addition to the cultural control that is partially provided by a relatively short-season crop,
aphids are maintained below damaging population thresholds through natural biological control,
seed treatments, and foliar insecticide use. Other factors that are important in predicting and
mitigating the effects of viral epidemics include timing of infection, presence of the virus in the
ecosystem, and movement of competent vector species.
Viruses in snap beans can be economically important. In the early 2000s, severe plant
stunting, flower abortion, mild to severe mosaic patterns on leaves, rolling of leaves, yellowing of
leaves, stunted pods, necrotic streaks on pods and necrosis of internal pod tissues and reduced
yields were observed in beans from the upper Midwest, parts of Canada, and into New York.
These symptoms were typical of virus infection and followed the presence of large numbers of
the soybean aphid (A. glycines), which at that time was a new invasive species. Further testing
determined that a legume strain of cucumber mosaic virus (CMV) was the cause of these
symptoms in snap bean. The recently introduced aphid, A.glycines was particularly important
because it was found to be a competent vector of CMV in single-aphid assays and also captured
in large numbers in snap bean and surrounding fields (Gildow et al. 2008, Nault et al. 2009).
9
Introduced species
In general, the introduction of species – be they plant, invertebrate, or vertebrate – to new
areas has increased in prevalence with the increase in global trade and travel. In order to be
considered introduced, a species must be transported outside of its native range and establish
itself in its new range. Introduced insect species of recent note in PA or nearby include emerald
ash borer, Asian longhorned beetle, brown marmorated stink bug, spotted wing drosophila,
Hemlock wooly adelgid, gypsy moth, Western bean cutworm, small hive beetle, and the
multicolored Asian lady beetle. Introduced species are the cause of novel and unexpected
problems in vegetable crops when they first appear and establish. As they become established it is
important to understand their movement and life history in order to effectively manage them.
Aphis glycines is a relatively new introduced species from Asia, being first recorded in
the US in 2000 (Ragsdale et al 2004). It is mainly a problem on soybean, but it is also a
competent virus-vector making it important in other legumes, like snap bean. When A. glycines
was introduced, both the primary and secondary hosts, Glycines max (soybean) and Rhamnus spp.
(buckthorn) respectively, were already established in the United States. Soybean is an important
field crop grown on over 69 million acres nationally, with the greatest concentration in the
American Midwest (USDA NASS, 2009). Rhamnus spp. are understory and edge trees and
include both native and non-native (notably Rhamnus cathartica) species. This facilitated
colonization and undetected spread until populations reached the point where they were causing
economically significant crop damage.
Therioaphis trifolii is another species that was introduced to the United States and
became important because of its abundance and preference for a legume crop. Like A. glycines, it
is a competent vector of CMV in snap bean (Gildow et al. 2008, Nault et al. 2009). The spotted
alfalfa aphid (referred to as SAA and formerly of the species Therioaphis maculata) was first
10
reported in the United States in 1954, in New Mexico on alfalfa. It spread rapidly through the
Southwest and on to the Eastern states (Dickson 1959). The population in North America was
noted to have biotypes with varying resistance to organophosphate insecticides (Berg and
Ridland, 1981). Its introduction and subsequent unexpected damage to crops mirrors that of A.
glycines. Since it has been in the U.S. for over 50 years, its populations have equilibrated due to
effective management strategies and it is no longer the cause of extreme economic loss in its host
crops of alfalfa and clover. T. trifolii can serve as a model invasive aphid species and potentially
give insight into the future population structure of A. glycines.
Insect movement
Insects, especially those capable of strong directional flight, can move long distances
without the aid of human travel and trade (as was probably the case for both A. glycines and T.
trifolii). An extreme example is that of the Monarch butterfly which migrates from the United
States (as far north as New England) back to its overwintering grounds in Mexico. The adults
cover the over 3000 mile distance in ~4 months. Insects can also avail themselves of the
prevailing winds and travel long distances as aerial plankton within weather systems.
Alate aphids end up in weather systems, with over 140,000 individuals collected from
suction traps in a 4 year period (Schmidt et al. 2012). This ability to travel with weather systems
(whether directly or indirectly), allows aphids to reach new areas with possibly previously
uninfested host plants. With A. glycines this long distance movement is especially important for
legume-growing areas of the country that were not in the range of the original introduction and/or
do not have the preferred primary host on which the aphid can overwinter.
Air flow trajectory models can be used to better understand insect movement, especially
those of insects (like aphids) that are not strong fliers. HYSPLIT (Hybrid Single Particle
11
Lagrangian Integrated Trajectory Model) is a publicly availably computer model from the
National Oceanic and Atomospheric Administration (NOAA) to calculate simple air parcel
trajectories (Draxler and Rolph 2012). This model can calculate simple forward and back
trajectories from single or multiple starting points for a single particle or plume. HYSPLIT is
used by meteorologists and others to calculate or forecast particle dispersion.
Rhamnus cathartica (common buckthorn) is the North American overwintering host for
Aphis glycines, which is an economically important pest of soybeans. In its native range, R.
davurica is the overwintering host for A. glycines in its native range, but it is not present in North
America. R. cathartica distribution is thought to be one of the limiting factors defining where A.
glycines can maintain local populations. Determining its range is important when assessing the
contributions of migrants versus a local population for IPM.
Rhamnus cathartica is a shrub/small tree (2-6 m) that is native to Europe and Asia
(Archibold et al. 1997, Converse 1985). It was introduced to North America in the 1800s for
windbreaks and hedgerows because of its wide tolerance for many environmental conditions and
eventually escaped from cultivation. Now, R. cathartica commonly invades forests and open
fields where the soil is moist (Archibold et al. 1997, Kurylo et al. 2007). It can out-compete
native species in shaded areas to become a dominant species in the understory (Archibold et al.
1997). Most of the fruit falls beneath the parent tree where seedlings survive well, but some are
eaten and dispersed by birds, mice, and white-tailed deer (Archibold et al. 1997, Myers et al.
2004). When R. cathartica is removed from an area the native vegetation returns quickly as long
as the seed bank is viable, indicating that R. cathartica may be allelopathic (Bodreau 1992).
Two experiments confirmed R. cathartica as a suitable host for Aphis glycines (Yoo et al.
2005, Voegtlin et al. 2005). On plants in outdoor cages, the soybean aphid over-wintered
successfully, developed colonies with viable eggs and alates, on three Rhamnus species (R.
cathartica, R. alnifolia, R. lanceolata: Voegtlin et al. 2005). Alates were also observed feeding on
12
R. cathartica, but were not even observed on the non-Rhamnus species in the experiment
(Voegtlin et al. 2005). Yoo et al. (2005) confirmed R. cathartica and R. alnifolia as suitable hosts
for A. glycines through a single-choice experiment. The aphids were induced to produce sexual
morphs in the laboratory using an autumnal light and temperature regime. Gynoparae and
oviparae were caged with a member of the Rhamnaceae family and a soybean plant, and they
survived the longest and produced more nymphs on R. cathartica and R. alnifolia. The other
Rhamnaceae taxa were unsuitable hosts because the soybean aphid could not complete
development on them without an alternative plant choice.
R. cathartica is the more widespread of the suitable host plants and had the highest
fecundity and survival of aphids. Thus, it is most likely to be a major part of the soybean aphid
lifecycle in the United States (Voegtlin et al. 2005, Yoo et al. 2005).
Current IPM tools and plant databases do not accurately represent the distribution of R.
cathartica in Pennsylvania. The PAPIPE (http://pa-pipe.zedxinc.com/) operates under the
assumption that the entire state is suitable habitat in its A. glycines phenology model. Other sites,
like PLANTS Database (http://plants.usda.gov/java/), do not have records from every county and
one specimen is enough to consider that county suitable for the species.
Central Pennsylvania appears to be a transition zone for this species. R. cathartica exists
locally in disturbed forests and along trails, which is similar to what McCay et al. (2009) found in
New York, but not in the same density and/or distribution as some high A.glycines population
areas like the Midwest or Northeast. The Forest Inventory Analysis (http://www.fia.fs.fed.us/)
does not include buckthorn in its surveys and it is important to understand what is limiting R.
cathartica in Pennsylvania and areas to the south. For the purposes of this thesis, I am using this
information about the range of R. cathartica as an impetus to investigate the influence of long
distance A. glycines migrants on soybeans grown in Pennsylvania.
13
Phenology and pest management
Phenology is simply defined as the timing of biological events. Understanding the
phenology of pest species can improve management practices by timing planting for when a pest
is absent or dormant and applying control measures when the pest is most vulnerable. Phenology
models are complicated by the fact that they require the estimation or discovery of what
environmental cues the insects are using in their development and the impact of each cue.
Phenology models driven by temperature are well-developed for many crop pests (Brown
1982, Hansen 2006, Hodgson et al 2011,Tobin et al 2001). Pesticide use and control measures
can be more accurately deployed when growers are well informed about risk level, which must
incorporate pest phenology models. These are usually developed from laboratory studies that
measure development of non-diapausing stages under controlled temperatures. In the case of
cucurbit crops, however, phenology models for the two most important pest species – the striped
cucumber beetle (Acalymma vittatum) and the squash bug (Anasa tristis) – exist in the literature
for portions of their life stages, but components that define diapause initiation and termination are
missing, and no models have been validated under field conditions.
Both the striped cucumber beetle and squash bug overwinter as adults, move among crop
patches, and both insect species are vectors of bacterial plant pathogens. In general, pests that
overwinter as adults, or those that exhibit extensive movement, are more challenging to work
with when trying to utilize phenology models. Field-validation of phenology models for these
species, and incorporation of phenology models useful to growers in Extension programs, will
need to consider these characteristics about overwintering, movement, and vector-capacity.
In conventional systems, these pests are controlled with neonicotinoid pesticides
(Fleischer et al. 1998) as drenches when transplanting seedlings, as seed treatments, or as foliar
sprays during the season. Certified organic producers are restricted to using organically certified
14
products to control these pests, and cannot rely on the same pesticides that are used by
conventional growers. Knowledge of pest phenology will allow growers to more effectively
deploy organic and cultural controls like approved pesticides, row covers, soil applied biocontrols
targeting larval stages (Ellers-Kirk and Fleischer, 2006) and adjusted planting dates.
Striped cucumber beetle is an important pest of cucurbits in any production system
because of its direct feeding damage to vines and fruit as well as its ability to vector Erwinia
tracheiphila, the causal agent of bacterial wilt. Striped cucumber beetle overwinters as an adult in
leaf litter and/or crop residue and resumes activity in the spring. When it becomes active in the
spring is a point for investigation. Radin and Drummond (1994) suggest that the beetles can be
active on any day with an average temperature above 12 C in Maine, and Lewis et al. (1990)
found beetle activity on trap flats when temperatures were above 18 C. Previous trapping efforts
in Pennsylvania at Rock Springs caught beetles in emergence cages in mid-May (Fleischer,
unpublished). The cue that causes these beetles to enter diapause in the fall is unknown, and they
do not have a synchronous spring emergence.
There are control measures directed towards both the larval and adult stages of this insect
(Ellers-Kirk et al. 2006). Systemic and foliar pesticide sprays are effective in conventional
systems against the adults. Larval control can be achieved by growing cucurbits on black plastic
with drip irrigation and also introducing entomopathogenic nematodes through the drip line
(Ellers-Kirk et al 2000).
Squash bug is an important pest of Cucurbita but not Cucumis. This insect also inflicts
feeding damage on vines and fruit, and can vector yellow vine decline (S. marcescens), another
cucurbit disease. It also overwinters as an adult and has a non-synchronous start to spring activity.
During the growing season, the lifecycle occurs above ground, making it relatively easy to
observe egg masses, juvenile instars, and subsequent adult generations. Common cucurbit
cultivation practices include the use of mulch (usually plastic), which provides harborage for
15
squash bug adults and nymphs, thus increasing pest pressure (Cartwright et al. 1990). It is
unknown how squash bugs terminate diapause, but it is known that the bugs enter diapause due to
photoperiod cues (Decker and Yeargan 2008). The critical photoperiod is between 14:10 (L:D)
and 14.5:9.5 (Nechols 1988).
Squash Vine Borer (Melittia cucurbitae, Lepidoptera: Sesiidae) is the third pest in this
study. In Pennsylvania, squash vine borer has mainly been a problem for small-scale growers or
home gardeners, which increases its importance to small-scale organic growers. This uneven
influence may be the result of the scale at which the crop is grown and yields are expected by
both audiences. On a small farm, if a few squash vine borers are present they could decimate a
small planting, while the same number of insects would cause damage that is barely noticeable in
a large field. This insect is most damaging as a caterpillar, which burrows into the cucurbit vine,
and the resulting feeding damage can potentially kill the entire vine. The most effective way to
control this insect is to spray when egg masses and/or first instars are present before they enter
the vine. Adults can be monitored with pheromone traps. Squash vine borer overwinters as a late
instar larva or pupae and has an extended emergence through the growing season. It requires
1687.5 degree days base 50° Fahrenheit to complete development (Canhilal et al. 2006).
In the case of these cucurbit pests, predicting early season activity may be useful for
optimizing planting time to give the plants a head start with limited pest pressure. For striped
cucumber beetle and squash bug, we are defining early season activity as the recruitment of the
pest to a host. These two pests are active in the spring before crops are planted, and monitoring
their presence can estimate the intensity of in-season pest pressure. Once cucurbit crops are
planted, in-field monitoring identifies a biofix for each pest. Using degree day developmental
requirements from the literature for each pest, we can use the biofix, temperature-dependent
development models, and forecast and 30-year climate records to estimate the emergence of
subsequent field generations. Since cucurbits require the services of pollinators, predicting pest
16
levels in the growing season is important for timing pest control measures while still allowing for
adequate pollination.
Dissertation Objectives
The goal of my thesis is to use population structure and phenology to advance insect
management in diverse vegetable agroecosystems. My work will be presented in four chapters:
In Chapter 2 I describe the alate aphid species composition in Northeastern US
processing snap beans and update the list of aphid species found in Pennsylvania. In addition to
describing the alate aphid community in Pennsylvaina snap beans, I used the J. O. Pepper aphid
slide collection in the Frost Entomological museum, previously published works (Pepper 1965,
Wallis et al 2005), and the results of an aphid trapping survey in snap beans to generate a
comprehensive review of aphid species present in Pennsylvania. Two of the most abundant aphid
species in our survey of snap bean fields were A. glycines and T. trifolii, and they were selected
for further investigation.
Chapter 3 and 4 improve our understanding of population structure of two invasive aphid
species relevant to virus transmission in snap beans. Because of the absences of large quantities
of the primary host (Rhamnus spp.) in Pennsylvania, I hypothesize that the A. glycines population
found in Pennsylvania during the growing season is largely influenced by migrants. I am using
genetic tools and air-flow trajectory models to investigate the natal sources of A. glycines in
Pennsylvania. In Chapter 4, I am also using genetic tools to understand the possible origin of T.
trifolii.
In Chapter 5 I set out to validate phenology models for pests of cucurbits. I monitored the
early season activity and growing season phenology of three pests on land that is transitioning to
organic production on research farms in Pennsylvania, Iowa and Kentucky on two commercially
17
important cucurbit crops (muskmelon – Cucumis melo and butternut squash – Cucurbita
moschata) to better inform organic management practices. I am also testing our ability to create
accurate phenology models of these pests using air-temperature degree-days from weather
stations and development times from the existing literature.
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(Coleoptera: Chrysomelidae), a vector of Erwinia tracheiphila in Cucurbits. Environmental
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Fleischer SJ, Orzoleck MD, deMackiewicz D, Otjen L (1998) Imidacloprid effects on Acalymma
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Gildow FE, Shah DA, Sackett WM, et al. (2008) Transmission efficiency of cucumber mosaic
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Gray SM, Banerjee N (1999) Mechanisms of arthropod transmission of plant and animal viruses.
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Grimaldi D, Engel M (2005) The Evolution of the Insects. Cambridge University Press.
Hansen LM (2006) Models for spring migration of two aphid species Sitobion avenae (F.) and
Rhopalosiphum padi (L.) infesting cereals in areas where they are entirely holocyclic.
Agricultural and Forest Entomology 8:83–88.
Hodgson JA, Thomas CD, Oliver TH, et al. (2010) Predicting insect phenology across space and
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McCay TS, McCay DH, Caragiulo AV, Mandel TL (2009) Demography and distribution of the
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Nault BA, Shah DA, Straight KE, et al. (2009) Modeling temporal trends in aphid vector
dispersal and cucumber mosaic virus epidemics in snap bean. Environmental Entomology
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Diapause Induction and Maintenance. Environmental Entomology 17:427-431.
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Transactions of the American Entomological Society 91:181-231.
Radin AM, Drummond FA (1994) Patterns of initial colonization of cucurbits, reproductive
activity, and dispersion of striped cucumber beetle, Acalymma vittatum (F.) (Coleoptera:
Chrysomelidae). Journal of Agricultural Entomology 11:115-123.
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of the Entomological Society of America 97:204–208.
Schmidt NP, O’Neal ME, Anderson PF, et al. (2012) Spatial distribution of Aphis glycines
(Hemiptera: Aphididae): a summary of the suction trap network. Journal of Economic
Entomology 105:259-271.
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(Lepidoptera: Tortricidae). Environmental Entomology 30(4): 692-699.
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Chapter 2
Alate aphid species composition in Northeastern US processing snap beans
and an update to historical lists
Introduction
Aphids are a small, but diverse group of insects with an origin in the Jurassic period and a
total of 4800 species world-wide (Grimaldi and Engel 2005, Dixon 1985a, Dixon 1985b). They
are primarily phloem feeders and in high numbers this can be damaging to their host plant.
Aphids excrete excess carbohydrates from their diet of phloem sap, and sooty mold fungi which
develop on this substrate can result in unmarketable produce. Also, aphids can serve as the host
resource for an array of generalist predators and specialist parasitoids in agroecosystems.
Some species of aphids are also important pests because of their ability to vector plant
viruses. There are two types of relationships between aphids and the viruses they transmit. The
first type is persistent transmission, which can be replicative or non-replicative. Viruses that are
persistently transmitted are obtained by an aphid from infected tissue during a feeding episode.
The virus moves through the stylet and enters the aphid’s digestive system, eventually passing
through the gut lining (and in the case of replicative viruses, multiplying in the midgut cells) and
entering the hemocoel (Gray and Banerjee 1999). The virus must return to the salivary glands to
infect a new host. From the salivary glands, the virus is injected into another plant when the aphid
feeds again. Persistent viruses are notable for not necessarily being detrimental to the aphid, but
once infected the aphid will vector them for life (Gray and Banerjee 1999).
The second type of virus transmission is non-persistent. These viruses are obtained
quickly by their aphid vector during short tasting probes which only puncture the epidermal cells
21
and last a few seconds or minutes versus the hours necessary for feeding. The viruses stick to the
stylet lining by binding to helper component proteins (a protein that binds to the stylet wall and
the virus coat protein) or directly to the stylet depending on the type of virus (Ng and Falk 2006).
They will remain there until they are flushed out during another tasting probe, which from the
perspective of the virus would ideally be on a new host. Non-persistently transmitted viruses are
vectored for only a short time, minutes to hours, by the aphid due to their association with the
stylet lining (Ng and Falk 2006). Both types of aphid/virus relationship can be economically
important. For example, aphids can vector cucumber mosaic virus (CMV) in a non-persistent
manner, which causes serious damage to cucurbits and legumes.
Aphids can reproduce parthenogenically, sexually, or using both methods depending on
the species and can spend the entire year on one host or alternate hosts. Both the type of
reproduction and host choice are used to categorize aphid life cycles. For reproduction, aphid
species are holocyclic (parthenogenesis interrupted by sexual phase), or anholocyclic (year round
parthenogenesis). For host selection, aphid species are heteroecious (host alternating), or
monoecious (non-host alternating). Unlike most other insect groups, aphids are more diverse in
temperate zones than in the tropics (Dixon 1985a). Dixon et al. (1987) hypothesized that this is
the case because of their host selectivity, the cost of parthenogenesis, and their lack of efficiency
when locating host plants. As Pennsylvania and New York are situated in the Northeastern region
of the United States and contain diverse landscapes of deciduous and coniferous forest,
agriculture, and developed land, one would expect both states to be species-rich when it comes to
aphids.
In Pennsylvania, J.O. Pepper was an entomologist specializing in aphids and actively
collected them for most of the 20th century. His collections were centered on his home in central
Pennsylvania, State College, and included much of the surrounding forest and farmland. The bulk
of his slides resulting from his collection efforts are housed in the Frost Entomological Museum
22
(University Park, PA), and he also contributed slides to the United States National Collection
(Beltsville, MD). Pepper (1965) reported 345 species in a published list of the aphids of
Pennsylvania and their host plants. To date, this is the most comprehensive published list of
aphids for the state. However, since taxonomy and systematics are in flux, the names that Pepper
published are currently out of date and in need of revision. Pepper’s contemporary in New York
was M. Leonard. Leonard (1963) is a list of the aphids in New York which was updated with
Leonard (1968), ultimately reporting over 430 aphid species in the state.
The ability of aphids to transmit viruses recently became important in Pennsylvania
orchards because of the appearance of an invasive species of virus, plum pox virus (Wallis et al
2005). Since it was unknown which aphid species (or group of species) could serve as the
primary vector of plum pox, it was important to set up a trapping system in PA orchards to
determine what species were present. The resulting aphid captures were slide mounted and
identified in order to determine what aphid species were visiting the trees and possibly vectoring
the viruses. Once the species were identified, colonies could be set up and each species could be
tested to see which were the most efficient at vectoring the viruses (Gildow et al. 2008).
In 2003, snap bean crops in Pennsylvania had virus-like symptoms (leaf mosaic and
blistering, deformed pods) and experienced dramatic yield loss. This coincided with the
appearance of a newly invasive aphid, Aphis glycines, and earlier reports of similar epidemics in
the Midwest and East (Larsen et al. 2002). Damage from plant viruses in snap beans was
previously documented, but large scale epidemics were novel for the region and further
investigation was necessary to determine the vector(s).
Ten species of aphid have been recorded on Phaseolus spp. (Blackman and Eastop,
2000). Of the aphids listed, six are found in PA (Aphis craccivora, A. fabae, A. gossypii,
Macrosiphum euphorbiae, Myzus persicae, and Smynthurodes betae). Aphid feeding is not as
severe a problem on snap beans as feeding by the defoliating pests (ex Mexican bean beetle,
23
cabbage looper), but aphids are important vectors of plant viruses. The shorter growing time
means that few plantings will have sufficient aphid populations develop to damaging levels,
however pathogen transmission as the alate aphids migrate through fields is still a risk. In
addition to the cultural control that is partially provided by a relatively short-season crop, aphids
are maintained below damaging population thresholds through natural biological control, seed
treatments, and foliar insecticide use. Other factors that are important in predicting and mitigating
the effects of viral epidemics include timing of infection, presence of the virus in the ecosystem,
and movement of competent vector species.
In Pennsylvania, late planted beans are the most susceptible to CMV (Nault et al 2004,
and 2009). By July, aphid colonies on surrounding hosts are producing alates that are moving
through and tasting plants in multiple fields. Since A. glycines especially do not use snap beans as
one of their secondary hosts (Ragsdale et al. 2004), the chances that they will cause significant
feeding damage are low. Damage occurs when a virus infected aphid tastes or feeds on an
uninfected plant, passing on the pathogen. If infection occurs during flowering and pod set, it is
likely that it will affect yield. Infection after pod set is less likely to affect yield as the virus
causes the most damage to the growing points (Jones et al 2008, Zitter and Murphy 2009).
To determine the species composition of the aphid community that could contribute to
CMV transmission in snap beans, we collected alate (winged) aphids from local snap bean fields
from 2004 through 2006 in Pennsylvania and 2002 through 2006 in New York, identified them to
species, estimated species richness and collated the results with those of previous collections.
24
Methods
Detailed methods for alate aphid collection in snap bean fields in Pennsylvania and New
York were published in Nault et al. (2009). To summarize, we used water pan traps baited with a
green ceramic tile (Webb et al 1994) and filled with a 20% propylene glycol solution in late-
planted snap bean fields in both states from 2002 – 2006 in NY and 2004 – 2006 in PA. Traps
were installed in a total of 56 fields in western NY (12 each year, except for 2004 which had 8
fields) and 18 fields in Centre county PA (6 each year). The traps in Centre County formed an
approximately 40 mile transect in the southern portion of the county roughly following state
routes 45 and 192. The traps were checked weekly for aphids from the early trifoliate stage (early
to mid July) until field harvest. Aphids collected in NY were identified by R. Eckel (RVWE
Consulting, Frenchtown, NJ), and aphids from Pennsylvania were slide mounted and identified
by W. Sackett and A. Bachmann using keys by Smith et al. (1992) and Blackman and Eastop
(2000). The aphid species list resulting from all of the trap catches was used for the species
rarefaction curves. Voucher specimens are located at the New York State Agricultural
Experiment Station in Geneva, NY, and Dr. Fleischer’s lab collection in the Department of
Entomology, Pennsylvania State University, University Park, PA.
Species rarefaction curves were calculated for the PA and NY collections individually
and combined using EstimateS (Colwell 2005).
To generate a complete list of the aphids of PA, I searched the J. O. Pepper aphid slide
collection housed at the Frost Entomological Museum (University Park, PA) and listed every
species present and compared that with the aphids recorded in Pepper (1965). I searched the slide
25
collection in addition to using Pepper (1965) because Pepper continued to collect aphids and
make slides into the late 1980s, but did not publish any updates to his original 1965 paper.
Because the collection and Pepper (1965) contained aphid species names from the early 20th
century, I consulted two online aphid databases to ensure that the final list used the most current
nomenclature (Aphid Species File – http://aphidspeciesfile.org, accessed April 22, 2012 and the
United States Collection of Aphididae - http://www.sel.barc.usda.gov/aphid/aphframe.htm,
accessed April 22, 2012). I combined my findings from the Pepper collection material with the
results of our pan trapping study and Wallis et al (2005) to create a more current list of the aphids
of PA (Tables 2-3 – 2-11).
Slide mounting protocol
Aphids collected from the water pan traps in Pennsylvania were prepared for
identification by slide mounting individuals. Aphids were stored in 70% EtOH, then transferred
to KOH and heated for 1 hour or until clear. Cleared aphids were rinsed for 10 minutes each in a
sequence of 95% EtOH, absolute EtOH, and clove oil. Once rinsed, each aphid was placed on a
drop of Canada balsam on a glass slide and positioned to expose diagnostic features before a
coverslip was placed on top.
Results
We caught and identified a total of 8821 aphids from PA and NY, with 7484 from NY
and 1337 from PA. A total of 97 species were caught; 71 from NY and 41 from PA. Only 254
(2.8%) of the aphids were unable to be identified. Of the aphids captured, those species
26
representing 1% or greater of the total number caught in either state are listed in Table 2-1
(originally published in Nault et al 2009) with their abundances.
Our efforts resulted in a list of aphids found in the PA and NY snap bean fields (Tables 2-
3 – 2-11) and we used Blackman and Eastop (1994, 2000, and 2006) to determine host ranges for
the species. From this host information we estimated that 61 percent of the species trapped in
snap beans in both states were most likely coming in from the surrounding forests as their hosts
are woody, not herbaceous, species (Figure 2-3).
Combining the list of aphids collected in this study as well as Wallis et al 2005 (which
was conducted in the same geographic region, but in peach orchards) with the list published by
J.O. Pepper in 1965, I developed a new, more comprehensive list of the aphids present in PA. I
found 8 species present in our collections that were not present in the slide collection housed in
the Frost Entomological Museum (University Park, PA) or published in Pepper (1965) (Table 2-
3). One of these aphids, Aphis glycines, was introduced to the US around the turn of the 21st
century and is now present in our agroecosystem.
More aphids overall were trapped in New York than Pennsylvania (Figure 2-1). Species
accumulations followed asymptotic patterns suggesting reasonably adequate sampling of the
aphid species present as alates in commercial snap bean fields. Overall, there were fewer aphids
collected in PA but based on the rarefaction curve there were a similar number of total species
represented in a sample of the same number of individuals (Figure 2-1, at 1250 individuals there
would be 45 species sampled in PA and 50 in NY). Based on the historical collections reported by
Pepper, there are approximately 350 aphid species in PA. Historical reports by Leonard suggest
that there are approximately 430 aphid species in NY.
27
Discussion
Our passive trapping in snap bean fields alone yielded a surprisingly high percentage of
the species present throughout PA and NY (~14% and ~18% respectively). Our sampling method
concentrated on only one habitat (commercial snap bean fields), but did intercept aphids moving
from the surrounding forests and hedgerows. The high degree of landscape heterogeneity and
crop diversity in the trapping areas includes plants that serve as hosts for many of the species that
represented less than 1% of the total capture (Pfleeger et al. 2006). These aphids were captured in
very small numbers (mostly singletons), and are not important contributers to the plant virus
epidemics reported by Wallis et al (2005) and Nault et al (2009).
Of the aphids we captured, two species were especially notable; T. trifolii which
comprised 31.8% of the identified aphids, and A. glycines which represented 18.2 % of the
identified aphids. Both of these aphids were introduced to North America (A. glycines from Asia
and T. trifolii from Europe) and were quite destructive to crops immediately after their
introduction (soybean and alfalfa, respectively). A. glycines continues to cause significant
economic damage in soybean. While not known to colonize Phaseoulus spp., both species were
determined to be competent vectors of the legume strain of CMV (Gildow et al 2008).
The Pepper (1965) aphid list in addition to the Pepper slide collection allowed us to
compile a comprehensive list of the aphids present in PA, but the nomenclature was in need of
updating. Our efforts to update the nomenclature, and incorporate our more recent sampling
efforts resulted in a modern list of aphids of PA that includes recently introduced species (like A.
glycines).
The intermittent appearance of CMV in central Pennsylvania snap bean crops could be a
result of our unique agricultural landscape. Our agricultural fields are located in valleys bordered
by the low, but steep, forested ridges of the Appalachian Mountains. Our ridge and valley system
28
might be acting like a filter, keeping CMV out for most of the season. We did not search for a
CMV reservoir outside of testing a few alfalfa fields, which were also negative for CMV. It is
possible, that much like our A. glycines population, legume strains of CMV are also a migrant
species. If this is the case, migrating aphids may be scrubbed of virions when they land in one of
our many bordering forests containing many non-host plants.
References
Blackman R, Eastop V (2000) Aphids on the World’s Crops: An Identification and Information
Guide, 2nd ed. Wiley, Chichester.
Blackman R, Eastop V (2006) Aphids on the World’s Herbaceous Plants and Shrubs: An
Identification Guide. Wiley, Chichester.
Blackman R, Eastop V (1994) Aphids on the World’s Trees: An Identification and Information
Guide. Wiley, Chichester.
Colwell RK (2005) EstimateS: Statistical estimation of species richness and shared species from
samples Version 8.2.0. <purl.oclc.org/estimates>
Dixon, AFG (1985a) Aphid Ecology. Blackie & Son Ltd
Dixon AFG (1985b) Structure of aphid populations. Annual Review of Entomology 30(1):155-
174.
Dixon AFG, Kindlmann P, Leps J, Holman J (1987) Why are there so few species of aphids,
especially in the tropics. The American Naturalist 129:580-592.
Favret C. Aphid Species File Version 1.0/4.1. http://aphid.speciesfile.org. Accessed 22 Apr 2012
Gildow FE, Shah DA, Sackett WM, et al. (2008) Transmission efficiency of Cucumber mosaic
virus by aphids associated with virus epidemics in snap bean. Phytopathology 98:1233-41.
Gray SM, Banerjee N (1999) Mechanisms of arthropod transmission of plant and animal viruses.
Microbiology and Molecular Biology Reviews 63:128-48.
Grimaldi D, Engel M (2005) The Evolution of the Insects. Cambridge University Press.
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Jones RAC, Coutts BA, Latham LJ, and McKirdy SJ (2008) Cucumber mosaic virus infection of
chickpea stands: temporal and spatial patterns of spread and yield-limiting potential. Plant
Pathology 57: 842-853.
Larsen RC, Miklas PN, Eastwell KC, et al. (2002) A virus disease complex devastating late
season snap bean production in the Midwest. Annual Report of the Bean Improvement Coop
45: 36-37.
Leonard MD (1963) A list of the aphids of New York. Proceedings of the Rochester Academy of
Science 10:289-428.
Miller G. United States National Collection of Aphididae.
http://www.sel.barc.usda.gov/aphid/aphframe.htm. Accessed 22 Apr 2012
Nault BA, Shah DA, Dillard HR, McFaul AC (2004) Seasonal and spatial dynamics of alate
aphid dispersal in snap bean fields in proximity to alfalfa and implications for virus
management. Environmental Entomology 33:1593-1601.
Nault BA, Shah DA, Straight KE, et al. (2009) Modeling temporal trends in aphid vector
dispersal and cucumber mosaic virus epidemics in snap bean. Environmental Entomology
38:1347-59.
Ng JCK, Falk BW (2006) Virus-vector interactions mediating nonpersistent and semipersistent
transmission of plant viruses. Annual review of Phytopathology 44:183-212.
Pepper JO (1965) A list of the Pennsylvania Aphididae and their host plants (Homoptera).
Transactions of the American Entomological Society 91:181-231.
Pfleeger TG, Olszyk D, Burdick CA et al. (2006) Using a geographic information system to
identify areas with potential for off-target pesticide exposure. Environmental Toxicology
and Chemistry 25(8): 2250-2259.
Smith C, Eckel R, Lampert E (1992) A key to many of the common alate aphids of North
Carolina (Aphididae: Homoptera). North Carolina Agriculture Research Service Technical
Bulletin 299.
Ragsdale DW, Voegtlin DJ, and O’Neil RJ (2004) Soybean aphid biology in North America.
Annals of the Entomological Society of America 97(2): 204-208.
Wallis CM, Fleischer SJ, Luster D, Gildow FE (2005) Aphid (Hemiptera: Aphididae) species
composition and potential aphid vectors of plum pox virus in Pennsylvania peach orchards.
Journal of Economic Entomology 98:1441-50.
Webb SE, Kok-Yokomi ML, Voegtlin DJ (1994) Effect of trap color on species composition of
alate aphids (Homoptera: Aphididae) caught over watermelon plants. The Florida
Entomologist 77:146-154.
30
Zitter TA, and Murphy JF (2009) Cucumber mosaic virus. The Plant Health Instructor.
http://www.apsnet.org/edcenter/intropp/lessons/viruses/Pages/Cucumbermosaic.aspx.
Accessed 28 May 2012.
Figures and Tables
Table 2-1. Alate aphid species representing > 1 % of the capture from water pan traps in
commercial snap bean fields in PA (2004 – 2006) and NY (2002 – 2006). Derived from Table 1
in Nault et al (2009).
New York
Pennsylvania
Overall
Species Total Percent of
Total Total Percent of
Total Total Percent of
Total
Therioaphis trifolii 2274 30.4 535 40.0 2809 31.8
Aphis glycines 1475 19.7 131 9.8 1606 18.2
Acyrthosiphon pisum 1106 14.8 28 2.1 1134 12.9
Rhopalosiphum maidis 685 9.2 75 5.6 760 8.6
Pemphigus populicaulis 239 3.2 0 0.0 239 2.7
Aphis craccivora 179 2.4 123 9.2 302 3.4
Aphis gossypii 130 1.7 201 15.0 331 3.8
Hayhurstia atriplicis 128 1.7 1 0.1 129 1.5
Lipaphis erysimi 128 1.7 0 0.0 128 1.5
Myzus persicae 97 1.3 26 1.9 123 1.4
Capitophorus eleagni 79 1.1 7 0.5 86 1.0
Aphis sp. 77 1.0 0 0.0 77 0.9
Rhopalosiphum padi 77 1.0 45 3.4 122 1.4
Aphis fabae 15 0.2 14 1.0 29 0.3
Anoecia sp. 1 <0.1 14 1.0 15 0.2
Brachycaudus persicae 2 <0.1 15 1.1 17 0.2
Unknown 216 2.9 38 2.8 254 2.9
Others 576 7.7 84 6.3 660 7.5
Total 7484 100.0 1337 100.0 8821 100.0
Table 2-2. Species of alate aphids with host associations, collected from water pan traps in commercial snap bean fields in PA (2004 – 2006)
and NY (2002 – 2006), and from similar traps in orchards in central PA (2003-2004, Wallis et al. 2005). Host associations for North America
from Blackman and Eastop (1994 [AWT], 2000 [AWC], and 2006 [HPS]).
Aphid Species PA NY Wallis Primary Host Secondary Host Source
Acyrthosiphon kondoi yes yes Leguminosae, Trifoleae, Loteae AWC
Acyrthosiphon pisum yes yes yes Leguminosae, Genistae, Trifoleae, Fabae, Hedysareae AWC
Amphorophora rubi yes yes Rubus spp. HPS
Anoecia corni yes yes yes Cornus sanguinea Gramineae AWT
Anoecia cornicola yes Cornus spp. Gramineae AWT
Anoecia oenotherae yes Cornus spp. Oenothera biennis AWT
Aphis armoraciae yes yes Compositae, Cruciferae, Umbelliferae, Graminae AWC
Aphis cephalanthi yes Cephalanthus occidentalis HPS
Aphis craccivora yes yes polyphagous, Leguminosae AWC
Aphis fabae yes yes yes Euonymous europaeus, Viburnum opulus polyphagous AWC
Aphis forbesi yes Fragaria spp. HPS
Aphis glycines yes yes Rhamnus spp. Glycine max AWC
Aphis gossypii yes yes yes Catalpa, Hibiscus, Celastrus, Rhamnus, Punica polyphagous, cotton, cucurbits all
Aphis helianthi yes yes Cornus stolonifera Umbelliferae AWT
Aphis lugentis yes Senecio spp., Erigeron sp. HPS
Aphis nasturtii yes Rhamnus cathartica, R. alnifolia wide range HPS
Aphis oestlundi yes Oenothera biennis HPS
Aphis pomi yes Pyroidea AWC
Aphis pseudohedrae yes
Aphis pulchella yes yes Euphorbia HPS
Aphis rubifolii yes Rubus spp. HPS
Aphis rumicis yes Rumex spp., Rheum spp. HPS
Aphis spiraecola yes yes Citrus, Spiraea spp., polyphagous AWC
Aphis viburniphila yes yes Viburnum spp. HPS
Aulacorthum solani yes yes Polyphagous HPS
Brachycaudus persicae yes yes Prunus persica, P. armeniaca Scrophulariaceae AWT
Brevicoryne brassicae yes yes Cruciferae AWC
Capitophorus elaeagni yes yes yes Elaeagnus spp. tubuliferous Compositae AWT
Capitophorus hippophaes yes yes Elaeagnaceae Polygonum spp., Persicaria spp.
AWT
Carolinaia rhois yes Rhus glabra, R. typhina Gramineae AWC
33
Chaitophorus populifolii yes yes Populus spp. AWT
Cinara atlantica yes Pinus spp. AWT
Drepanaphis acerifoliae yes Acer saccharinum, A. rubrum, A. saccharum AWT
Drepanaphis carolinensis yes Acer saccharum, A. rubrum AWT
Drepanaphis nigricans yes yes Acer rubrum AWT
Drepanaphis sabrinae yes Acer saccharum AWT
Drepanosiphum platanoidis yes Acer pseudoplatanus, Acer spp., sycamore AWT
Dysaphis plantaginea yes Malus spp., Pyrus Plantago spp. AWT
Dysaphis tulipae yes many monocots AWC
Eriosoma lanigerum yes Pyroidea, apple, Crataegus, Coloneaster AWC
Essigella pini yes yes Pinus spp. AWT
Eulachnus rileyi yes yes Pinus spp. AWT
Geoica squamosa yes
Hayhurstia atriplicis yes yes Chenopodiaceae, Atriplex, Chenopodium spp. HPS
Hyadaphis foeniculi yes Lonicera spp. Umbelliferae spp. AWC
Hyalopterus pruni yes Prunus domestica, P. armeniaca Phragmites communis, Arundo donax
AWC
Hydaphias foeniculi yes Lonicera spp. Umbelliferae HPS
Hyperomyzus lactucae yes yes Ribes spp. Sonchus spp. AWC
Hysteroneura setariae yes Prunus domestica Graminieae AWC
Illinoia liriodendri yes Liriodendron tulipifera AWT
Kaltenbachiella ulmifusa yes Ulmus rubra Labiatae AWT
Lipaphis erysimi yes yes Cruciferae AWC
Macrosiphoniella ludovicianae
yes Artemisia ludoviciana, A. vulgaris HPS
Macrosiphoniella sanborni yes Dendranthema indicum, morifolium, frutescens, Compositae
AWC
Macrosiphum euphorbiae yes yes yes Rosa spp highly polyphagous, Solanaceae
AWC
Macrosiphum pallidum yes yes Rosaceae, Rosa spp. AWC
Macrosiphum pseudocoryli yes Ostrya virginiana, Corylus spp. AWT
Macrosiphum rosae yes Rosa spp. Dipsacaceae AWC
Melaphis rhois yes Rhus spp. (glabra, typhina) mosses AWT
Monellia caryella yes yes yes Carya spp. AWC
Myzus persicae yes yes yes Prunus persica, Prunus spp. polyphagous, over 40 families AWC
Nearctaphis bakeri yes yes yes Crataegus, Cydonia, Malus, Pyrus Leguminosae AWC
Nearctaphis clydesmithi yes yes Crataegus unknown AWT
34
Nearctaphis crataegifoliae yes yes yes Crataegus spp Trifolium spp. AWC
Ovatus crataegarius yes Crateagus spp. Labiatae esp Mentha AWT
Pemphigus populicaulis yes yes Populus deltoides, P. tremuloides unknown AWT
Pemphigus populitransversus
yes yes Populus spp. Cruciferae AWC
Pemphigus populivenae yes yes Populus spp. Chenopodiaceae AWT
Periphyllus americanus yes Acer spp. AWT
Periphyllus testudinaceus yes Acer spp., Aesculus spp. AWT
Phorodon humuli yes Prunus spp. Humulus lupulus (hops) AWC
Prociphilus fraxinifolii yes Fraxinus spp. AWT
Protaphis middletonii yes yes Compositae, Cruciferae, Graminae, and others HPS
Pterocomma bicolor yes Populus spp., Salix spp. AWT
Pterocomma smithiae yes Populus spp., Salix spp. AWT
Rhodobium porosum yes yes
Rhopalomyzus poae yes yes Lonicera alpigena grasses AWC
Rhopalosiphoninus latysiphon
yes bulbs (Tulipa, Gladiolus), runners AWC
Rhopalosiphum maidis yes yes yes Gramineae AWC
Rhopalosiphum nymphaeae yes Prunus spp. water plants AWC
Rhopalosiphum oxyacanthae
yes yes Alus, Pyrus, Cotoneaster, Crataegus, Sorbus grasses AWC
Rhopalosiphum padi yes yes yes Prunus virginiana Gramineae AWC
Rhopalosiphum rufiabdominale
yes yes Prunus spp. Gramineae, Cyperaceae, Solanaceae
AWC
Schizaphis graminum yes yes Gramineae AWC
Sipha flava yes yes Gramineae AWC
Sipha glyceriae yes Gramineae AWC
Sitobion avenae yes yes Gramineae AWC
Tetraneura akinire yes
Tetraneura nigriabdominalis yes yes Ulmus spp. Gramineae AWC
Therioaphis riehmi yes Melilotus spp. HPS
Therioaphis trifolii yes yes yes Leguminoseae AWC
Uroleucon anomalae yes Aster novaeangliae HPS
Uroleucon pseudambrosiae yes Compositae, Lactuca spp. HPS
Utamphorophora crataegi yes Crataegus spp. AWT
Vesiculaphis caricis yes Rhododendron spp. Cyperus spp. HPS
Table 2-3. New aphid records from PA reported in Nault et al (2009) and/or Wallis et al (2005)
but not found in Pepper (1965).
Species Nault et al Wallis et al
Acyrthosiphon kondoi yes
Aphis armoraciae yes yes
Aphis glycines yes
Aphis lugentis yes
Aphis pulchella yes
Lipaphis erysimi yes
Nearctaphis clydesmithi yes
Tetraneura nigriabdominalis yes
Table 2-4. Species in six subfamilies of the family Aphididae occurring in PA.
Subfamily Tribe Species
Anoeciinae Anoecia corni
Anoecia cornicola
Anoecia oenotherae
Anoecia setariae
Hormaphidinae Cerataphidini Cerataphis brasiliensis
Cerataphis lataniae
Hormaphidini Hamamelistes spinosus
Hormaphis hamamelidis
Mindarinae Mindarus abietinus
Phyllaphidinae Phyllaphis fagi
Stegophylla quercicola
Stegophylla quercifoliae
Stegophylla quercina
Pterocommatinae Fullawaya terricola
Plocamaphis flocculosa
Pterocomma bicolor
Pterocomma medium
Pterocomma populifoliae
Pterocomma smithiae
Saltusaphidinae Saltusaphidini Iziphya flabella
Iziphya grandipes
Iziphya vittata
Strenaphis elongata
Thripsaphidini Allaphis verrucosa
Subsaltusaphis virginica
Thripsaphis ballii
Table 2-5. Species in the subfamily Aphidinae, tribe Macrosiphini occurring in PA
Abstrusomyzus phloxae Dysaphis plantaginea Macrosiphum carpinicolens Pleotrichophorus glandulosus
Acuticauda solidaginifoliae Dysaphis tulipae Macrosiphum coryli Pleotrichophorus patonkus
Acyrthosiphon kondoi Ericaphis scammelli Macrosiphum cystopteris Pleotrichophorus wasatchii
Acyrthosiphon lactucae Ericaphis wakibae Macrosiphum euphorbiae Pseudacaudella rubida
Acyrthosiphon malvae Hayhurstia atriplicis Macrosiphum gaurae Rhodobium porosum
Acyrthosiphon malvae malvae Hayhurstia atriplicis atriplicis Macrosiphum gei Rhopalosiphoninus latysiphon
Acyrthosiphon pisum Hyadaphis foeniculi Macrosiphum geranii Rhopalosiphoninus solani
Acyrthosiphon pseudodirhodum Hyalomyzus collinsoniae Macrosiphum lilii Rhopalosiphoninus staphyleae
Amphorophora agathonica Hyalomyzus eriobotryae Macrosiphum pallidum Rhopalomyzus lonicerae
Amphorophora ampullata laingi Hyalomyzus humboldti Macrosiphum pseudocoryli Rhopalomyzus poae
Amphorophora rossi Hyalomyzus mitchellensis Macrosiphum ptericolens Sitobion avenae
Amphorophora rubi Hyalomyzus sensoriatus Macrosiphum rosae Sitobion avenae avenae
Amphorophora sensoriata Hyadaphis pseudobrassica Macrosiphum siriodentri Uroleucon ambrosiae
Aulacorthum solani Hyalopteroides humilis Macrosiphum tiliae Uroleucon ambrosiae ambrosiae
Aulacorthum solani solani Hyperomyzus lactucae Mastopoda pteridis Uroleucon anomalae
Brachycaudus cardui Hyperomyzus nabali Metopolophium dirhodum Uroleucon caligatum
Brachycaudus helichrysi Hyperomyzus picridis Microlophium sibiricum Uroleucon chrysanthemi
Brachycaudus knowltoni Idiopterus nephrelepidis Microlophium sibiricum sibiricum Uroleucon chrysopsidicola
Brachycaudus persicae Illinoia azaleae Microparsus desmodiorum Uroleucon erigeronense
Brachycaudus rociadae Illinoia azaleae azaleae Microparsus kislankoi Uroleucon eupatoricolens
Brachycaudus rumexicolens Illinoia azaleae kalmiaflora Microparsus olivei Uroleucon eupatorifoliae
Brachycaudus schwartzi Illinoia borealis Microparsus singularis Uroleucon floricola
Brachycorynella asparagi Illinoia canadensis Muscaphis musci Uroleucon gravicorne
Brevicoryne brassicae Illinoia goldmaryae Myzaphis rosarum Uroleucon helianthicola
Cachryphora canadensis Illinoia liriodendri Myzodium modestum Uroleucon illini
Cachryphora serotinae Illinoia pepperi Myzus avenae Uroleucon impatiensicolens
Capitophorus carduinus Illinoia rhokalaza Myzus cerasi Uroleucon lanceolatum
Capitophorus elaeagni Illinoia richardsi Myzus formosanus Uroleucon leonardi
37
Capitophorus hippophaes Illinoia rubicola Myzus lythri Uroleucon luteolum
Carolinaia caricis Illinoia spiraecola Myzus ornatus Uroleucon nigrotibium
Carolinaia howardii Linosiphon sanguinarium Myzus persicae Uroleucon nigrotuberculatum
Carolinaia rhois Liosomaphis berberidis Nasonovia aquilegiae Uroleucon obscuricaudatum
Catamergus kickapoo Lipaphis erysimi Nasonovia compositellae Uroleucon paucosensoriatum
Cavariella aegopodii Lipaphis pseudobrassicae Nasonovia cynosbati Uroleucon pepperi
Cavariella cicutae Longicaudus trirhodus trirhodus Nasonovia heucherae Uroleucon pieloui
Cavariella hendersoni Macrosiphoniella abrotani Nasonovia purpurascens Uroleucon pseudambrosiae
Cavariella pastinacae Macrosiphoniella cystopteris Nasonovia ribisnigri Uroleucon rudbeckiae
Cavariella salicis Macrosiphoniella frigidicola Nearctaphis bakeri Uroleucon rurale
Cavariella theobaldi Macrosiphoniella leucanthemi Nearctaphis clydesmithi Uroleucon russellae
Ceruraphis eriophori Macrosiphoniella ludovicianae Nearctaphis crataegifoliae Uroleucon sonchellum
Ceruraphis viburnicola Macrosiphoniella millefolii Nearctaphis crataegifoliae crataegifoliae Uroleucon sonchi
Chaetosiphon fragaefolii Macrosiphoniella pennsylvanica Neomyzus circumflexus Uroleucon sonchi sonchi
Chaetosiphon minor Macrosiphoniella sanborni Neotoxoptera formosana Uroleucon taraxaci
Chaetosiphon minor minor Macrosiphoniella siriodendri Neotoxoptera violae Uroleucon tardae
Chaetosiphon tetrarhodum Macrosiphoniella subterranea Ovatus crataegarius Uroleucon tuataiae
Coloradoa rufomaculata Macrosiphoniella tanacetaria Papulaphis sleesmani Utamphorophora crataegi
Cryptomyzus ribis Macrosiphoniella tapuskae Phorodon humuli Utamphorophora humboldti
Decorosiphon corynothrix Macrosiphum adianti Pleotrichophorus ambrosiae Diuraphis holci Macrosiphum californicum Pleotrichophorus asterifoliae
Table 2-6. Species in the subfamily Aphidinae, tribe Aphidini occurring in PA
Aphis armoraciae Aphis helianthi Aphis varians
Aphis angelicae Aphis illinoisensis Aphis vernoniae
Aphis asclepiadis Aphis impatientis Aphis viburniphila
Aphis caliginosa Aphis lugentis Protaphis knowltoni
Aphis carduella Aphis maculatae Protaphis middletonii
Aphis cephalanthi Aphis melliferum Sanbornia juniperi
Aphis coreopsidis Aphis middletonii Toxoptera viridirubra
Aphis cornifoliae Aphis nasturtii Hyalopterus pruni
Aphis craccivora Aphis neilliae Hysteroneura setariae
Aphis debilicornis Aphis nerii Pseudasiphonaphis corni
Aphis decepta Aphis oenotherae Rhopalosiphum cerasifoliae
Aphis fabae Aphis oenotherae sanborni Rhopalosiphum enigmae
Aphis farinosa Aphis oestlundi Rhopalosiphum maidis
Aphis feminea Aphis pawneepae Rhopalosiphum musae
Aphis folsomii Aphis pomi Rhopalosiphum niger
Aphis forbesi Aphis pseudohedrae Rhopalosiphum nymphaeae
Aphis frangulae Aphis pulchella Rhopalosiphum oxyacanthae
Aphis gerardiae Aphis rubicola Rhopalosiphum padi
Aphis glycines Aphis rubifolii Rhopalosiphum parvae
Aphis gossypii Aphis rumicis Rhopalosiphum rufiabdominale
Aphis hamamelidis Aphis sambuci Rhopalosiphum sanguinarium
Aphis hederae Aphis spiraecola Schizaphis graminum
Aphis hederae pseudohederae Aphis spiraephila Schizaphis nigra
39
Table 2-7. Species in the subfamily Calaphidinae occurring in PA
Tribe Species Calaphidini Betulaphis quadrituberculata Callipterinella calliptera Euceraphis mucida
Calaphis alni Cepegillettea myricae Hannabura alnosa
Calaphis betulaecolens Euceraphis betulae Euceraphis punctipennis
Calaphis betulella Euceraphis gillettei
Calaphis leonardi Euceraphis lineata
Panaphidini Hoplochaitophorus heterotrichus Myzocallis melanocera Monellia hispida
Hoplochaitophorus quercicola Myzocallis multisetis Monellia microsetosa
Lachnochaitophorus obscurus Myzocallis punctata Monelliopsis bisselli
Myzocallis alhambra Myzocallis spinosa Monelliopsis caryae
Myzocallis asclepiadis Myzocallis synthri Monelliopsis nigropunctata
Myzocallis bellus Myzocallis tuberculata Protopterocallis fumipennella
Myzocallis castaneae Myzocallis walshii Protopterocallis gigantea
Myzocallis castanicola Neosymydobius albasiphus Protopterocallis pergandei
Myzocallis coryli Patchia virginiana Pterocallis alnifoliae
Myzocallis discolor Tuberculatus punctatellus Therioaphis ononidis
Myzocallis exultans Chromaphis juglandicola Therioaphis riehmi
Myzocallis frisoni Eucallipterus tiliae Therioaphis trifolii
Myzocallis granovskyi Melanocallis caryaefoliae Therioaphis trifolii maculata
Myzocallis longiunguis Monellia caryella Tinocallis ulmifolii
Table 2-8. Species in the subfamily Chaitophorinae occurring in PA.
Tribe Species Chaitophorini Chaitophorus longipes Chaitophorus populifolii Chaitophorus viminicola
Chaitophorus nigrae Chaitophorus populifolii simpsoni Periphyllus americanus
Chaitophorus nigricentrus Chaitophorus pusillus Periphyllus californiensis
Chaitophorus nudus Chaitophorus saliniger Periphyllus lyropictus
Chaitophorus populicola Chaitophorus stevensis Periphyllus negundinis
Chaitophorus populifoliae Chaitophorus viminalis
Siphini Sipha elegans Sipha flava Sipha glyceriae
Table 2-9. Species in the subfamily Drepanosiphinae occurring in PA
Drepanaphis acerifoliae Drepanaphis nigricans Drepanaphis simpsoni
Drepanaphis carolinensis Drepanaphis parva Drepanaphis spicata
Drepanaphis kanzensis Drepanaphis platanoides Drepanosiphum platanoidis
Drepanaphis monelli Drepanaphis sabrinae Shenahweum minutum
40
Table 2-10. Species in the subfamily Eriosomatinae occurring in PA
Tribe Species Eriosomatini Colopha graminis Eriosoma lanigerum Eriosoma wilsoni
Colopha ulmicola Eriosoma lanuginosum Kaltenbachiella ulmifusa
Eriosoma americanum Eriosoma mimicum Tetraneura nigriabdominalis
Eriosoma crataegi Eriosoma rileyi Tetraneura ulmi
Fordini Forda marginata Geoica pellucida Melaphis rhois
Forda olivacea Geoica ultricularia Smynthurodes betae
Pemphigini Grylloprociphilus imbricator Pemphigus populitransversus Prociphilus longianus
Mordwilkoja vagabunda Pemphigus populivenae Prociphilus probosceus
Neoprociphilus aceris Prociphilus americanus Prociphilus tessellatus
Pachnypappa pseudobyrsa Prociphilus caryae Thecabius affinis
Pemphigus bursarius Prociphilus caryae fitchii Thecabius gravicornis
Pemphigus monophagus Prociphilus corrugatans Thecabius populimonilis
Pemphigus nortonii Prociphilus erigeronensis
Pemphigus populicaulis Prociphilus fraxinifolii
Table 2-11. Species in the subfamily Lachninae occurring in PA
Tribe Species Eulachnini Cinara atlantica Cinara laricifex Cinara taedae
Cinara banksiana Cinara laricis Cinara tujafilina
Cinara braggii Cinara osborniana Cinara watsoni
Cinara canatra Cinara pergandei Essigella pini
Cinara costata Cinara pilicornis Eulachnus agilis
Cinara cupressi Cinara pinea Eulachnus americanus
Cinara fornacula Cinara pinivora Eulachnus rileyi
Cinara gracilis Cinara pruinosa Schizolachnus parvus
Cinara harmonia Cinara pruinosa pruinosa Schizolachnus piniradiatae
Cinara juniperi Cinara spiculosa
Cinara juniperivora Cinara strobi
Lachnini Lachnus allegheniensis Longistigma trirhodus Tuberolachnus salignus
Longistigma caryae
Tramini Trama rara
Figure 2-1. Individual-based rarefaction curves showing aphid species accumulation in PA and
NY.
0
10
20
30
40
50
60
70
80
90
100
0 1000 2000 3000 4000 5000 6000 7000 8000
Me
an #
sp
eci
es
individuals
PA
NY
42
Figure 2-2. Individual-based rarefaction curve showing aphid species accumulation from the
combining of samples from PA and NY (solid line) and the 95% confidence intervals for the
curve (dashed lines).
Figure 2-3. Proportion of aphids from the pan trapping collection in PA and NY that use
herbaceous plants, trees, or crops as primary hosts. Host associations for North America
characterized from Blackman and Eastop (1994, 2000, and 2006).
0
10
20
30
40
50
60
70
80
90
100
110
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
Me
an #
sp
eci
es
individuals
7%
29%
61%
3%
Crop
Herb/grass
Woody
Unknown
Chapter 3
Estimating natal sources of Aphis glycines using molecular markers and airflow
trajectories
Introduction
Aphis glycines is an important pest of soybean as well as a competent virus vector in
other legumes (Gildow et al 2008, Nault et al 2009). Since its introduction to the US around 2000,
this invasive species has spread through the Midwest and into the Northeast, wherever soybeans
are plentiful (Ragsdale et al 2004, Ragsdale et al 2011). Crop losses in soybean resulting from A.
glycines if left untreated have the potential to total $2.4 billion annually (Song et al 2006). These
dramatic losses were the first time North American soybean growers had to contend with a
significant insect pest, and this spurred research on its life history and effective control methods
(Tilmon et al 2011, Hodgson et al 2012). A. glycines is important because of the physical damage
high populations cause to soybean, but it is of special concern in Pennsylvania because it is an
efficient vector of legume viruses (Gildow et al. 2008).
Aphis glycines life history
Soybean aphid (Hemiptera: Aphididae) is heteroecious and holocyclic. It alternates
between sexual and asexual reproduction (holocyclic) and requires two hosts to complete its
lifecycle (heteroecious) – the primary host, Rhamnus spp. (buckthorn) is a woody invasive shrub
and the secondary host is soybean (Voegtlin et al 2004 and 2005).
44
Factors limiting the range of Aphis glycines
Rhamnus cathartica is the North American overwintering host for Aphis glycines. In its
native range, R. davurica is the overwintering host for A. glycines but it is not present in North
America. R. cathartica distribution is thought to be one of the limiting factors in where A.
glycines can maintain local populations. Determining its range is important when assessing the
contributions of migrants versus a local population for IPM.
Rhamnus cathartica is a shrub/small tree (2-6 m) that is native to Europe and Asia
(Archibold et al. 1997, Converse 1985). It was introduced to North America in the 1800s for
windbreaks and hedgerows because of its tolerance for many environmental conditions and
eventually escaped from cultivation. Now, R. cathartica commonly invades forests and open
fields where the soil is moist (Archibold et al. 1997, Kurylo et al. 2007). It can out-compete
native species in shaded areas to become a dominant species in the understory (Archibold et al.
1997). Most of the fruit falls beneath the parent tree where seedlings survive well, but some are
eaten and dispersed by birds, mice, and white-tailed deer (Archibold et al. 1997, Myers et al.
2004). When R. cathartica is removed from an area the native vegetation returns quickly as long
as the seed bank is viable, indicating that R. cathartica may be alleopathic (Bodreau 1992).
Two previous experiments confirmed R. cathartica as a suitable host for Aphis glycines
(Yoo et al. 2005, Voegtlin et al. 2005). On plants in outdoor cages, the soybean aphid over-
wintered successfully on three Rhamnus species (R. cathartica, R. alnifolia, R. lanceolata:
Voegtlin et al. 2005). Successful over-wintering required developed colonies with hatching eggs
and the production of spring migrants. Alates were also observed feeding on R. cathartica
(Voegtlin et al. 2005). Aphids were not observed on the non-Rhamnus species in the experiment
(Voegtlin et al. 2005).
45
Yoo et al. (2005) confirmed R. cathartica and R. alnifolia as suitable hosts for A. glycines
through a single-choice experiment. The aphids were induced to produce sexual morphs in the
laboratory using an autumnal light and temperature regime. Gynoparae and oviparae were caged
with a member of the Rhamnaceae family and a soybean plant. Gynoparae survived the longest
and produced more nymphs on R. cathartica and R. alnifolia. The other Rhamnaceae taxa were
unsuitable hosts because the soybean aphid could not develop on them in the absence of an
alternative plant choice.
R. cathartica is the more widespread of the suitable host plants and had the highest
fecundity and survival of aphids. Thus, it is most likely to be a major part of the soybean aphid
lifecycle in the United States (Voegtlin et al. 2005, Yoo et al. 2005).
Current IPM tools and plant databases do not accurately represent the distribution of R.
cathartica in Pennsylvania. The PAPIPE operates under the assumption that the entire state is
suitable habitat in its A. glycines phenology model. Other sites, like plants.gov, do not have
records from every county and one specimen is enough to consider that county suitable for the
species.
In the summer of 2006 I scouted local forests and fencerows adjacent to snap bean fields
for R. cathartica. I planned on surveying the forests closest to the six snap bean fields used in our
CMV study (Chapter 2) using transects that ran perpendicular to the edge forest edge. At that
point, I wanted to measure how deep into the forest this species was present, but recent papers
suggest that it is predominantly an edge species. Ultimately, I did not find any R. cathartica after
applying the transect method once to each of the six field sites, although it is present in parks (e.g.
Sunset Park) and backyards in State College.
46
Aphis glycines in Pennsylvania
Initially, an outbreak of cucumber mosaic virus (CMV) in snap beans posed a risk to the
snap bean processing crop in Pennsylvania. CMV is a non-persistently transmitted virus that is
vectored by multiple aphid species. Soybean aphid is a very competent vector of the legume
strain of CMV in snap beans, and is a significant component of the epidemiology of CMV in
processing snap beans (Gildow et al. 2008, Nault et al. 2009). This led to further work, focusing
on the movement of this virus vector in legumes.
In Pennsylvania, soybean aphid is present during the growing season, but the primary
host species, common buckthorn (Rhamnus cathartica), needed for overwintering populations to
exist is scarce. The scarcity of common buckthorn in Pennsylvania – especially as compared to
its distribution in parts of the Midwest and northern New York, may severely limit the
overwintering populations. If this is true, then management can be improved by tracking
migration and dispersal, since population establishment in Pennsylvania is influenced heavily by
long-distance dispersal as opposed to that from local overwintering hosts.
Soybean management
Current conventional pest management in soybean relies on the use of neonicotinoid seed
treatments (e.g. imidacloprid, thiamethoxam). Those systemic seed treatments are effective
against aphids, but for limited times. Pesticide trials in the Midwest found that seed treatments
could effectively manage A. glycines until the soybean R2 stage or until July – August when the
pest pressure increases (Rice and O’Neal 2008 and Schulz et al 2011). With early-season
effectiveness, seed treatment value would be greatest when significant populations are appearing
during the first stages of plant growth – and this will be much more likely in geographic areas
47
where overwintered populations are occurring (Bahlai et al 2010). In contrast, if we find that
most of the early population establishment in Pennsylvania requires successful long-distance
migration, then we may find less utility from insecticidal seed treatments, at least for earlier
planted crops. Additional management implications for biological control, such as the
conservation of natural enemies, exist as well (Brewer and Noma 2010, Costamagna and Landis
2011, Zhang and Swinton 2009). Thus, to better manage soybean aphid in Pennsylvania it helps
to understand their population origins: are our summer populations from local colonies or are they
migrants from neighboring states?
Aerobiology
Aphids can move locally and over long distances. Alate morphs can be produced as a
result of a variety of cues including population density on a host plant, nutritional quality,
interaction with natural enemies, and temperature (Müller et al 2001). Alate aphids are weak
fliers and can successfully move between plants and fields. Alate aphids are not strong enough to
resist updrafts, and individuals that find, or propel, themselves in weather systems can be
transported long distances. If the deposition occurs on or near a suitable host, this can seed
populations in new areas or add to an existing population.
Air flow trajectory models can be used to better understand insect migrations, especially
those of insects (like aphids) that are not strong fliers. HYSPLIT (Hybrid Single Particle
Lagrangian Integrated Trajectory Model) is a computer model to calculate simple air parcel
trajectories (Draxler and Rolph 2012). This model can calculate simple forward and back
trajectories from single or multiple starting points for a single particle or plume. HYSPLIT is
used by meteorologists and others to calculate or forecast particle dispersion. Recent uses include
48
tracking dust storms and volcanic ash plumes after an eruption (Stunder et al 2007, Wang et al
2011).
In agricultural systems, aerobiological models and simulations were used to track and
forecast the movement of the fungal spores (Phakopsora pachyrhizi) that cause soybean rust.
Using information about soybean rust biology, a scouting program, and meteorological data, Isard
et al (2007) created a model that was used to successfully predict the appearance of the pathogen
in the Ohio River valley.
Soybean rust was monitored with a field scouting program set up from Florida to
southern Ontario, Canada (IPM-PIPE). A similar program was in effect for soybean aphid for the
eastern half of the US and southern Canada from 2007-2010. Each state in the program
established sentinel fields and monitored them throughout the soybean growing season for
soybean aphid. The weekly counts were reported to a website which displayed a map of the
catches. This tool was good for showing where the populations were high and/or increasing, but it
did not have any forecast capabilities.
Since aphids, including soybean aphid, fly at speeds lower than the relative movement of
the air, when they are transported in an updraft they cannot escape and move with that air packet
(Dixon 1985). This movement of air packets is what HYSPLIT can be used to model. In 2005, a
suction trap network using ~6 m tall traps was established in the Midwest to monitor the
movement of alate aphids, specifically A. glycines. The network was successful in catching alate
aphids and noted peak catches of A. glycines in the summer (late July – mid-August) and fall
(Schmidt et al 2012).
49
Molecular tools for population identification
In order to better understand the origin of soybean aphid in Pennsylvania, we used
microsatellite markers and single nucleotide polymorphisms (SNPs) to investigate its population
structure. We looked at temporal change in allele frequency by collecting aphids from soybean
every other week during the summer of 2009 and 2010 from unsprayed sentinel fields in central
Pennsylvania. Aphids from early August 2009 in Pennsylvania were compared to those collected
close to the same time from soybean in New York and Virginia to identify any spatial differences
in allele frequency. This was repeated in 2010 with samples from New York, Quebec, and
Ontario. When samples from Pennsylvania matched those in other geographic regions, we used
HYSPLIT to determine temporally relevant weather systems that could have been responsible for
their immigration. This information will further our understanding of aphid movement to states
that do not have a strong overwintering population.
Michel et al 2009a and 2009b provide the basis for this work. They established effective
microsatellites for soybean aphid and looked at their spatial differentiation in the Midwest and
South Korea (part of its native range). Michel et al (2009a) found that much of the genotypic
variation was due to collection time. Since the Eastern US was not included in that data, we
worked together to establish a sampling plan for Pennsylvania and contacted collaborators in
Ontario, Quebec, New York and Virginia. We hypothesize that Pennsylvania differs from
portions of the Midwest regarding soybean aphid in that it does not have widespread
overwintering habitat. Thus, early-season populations in Pennsylvania may match with those
from the Midwest, which would support the conclusion that these are migrants.
We included the sites in New York and Virginia as positive and negative controls for
Rhamnus presence, respectively. Buckthorn is present in Northern New York in forests and
50
adjacent to snap bean fields. In Virginia, buckthorn is only recorded in one northern county and
our collection sites in the southeastern part of the state are well outside of its range.
Objectives
The focus of this study was to investigate the potential of using molecular markers and
airflow trajectories to provide an estimation of the natal sources of A. glycines that were found in
areas with no consistent over-wintering population. With the molecular evidence serving as a
proof of concept, future efforts to track or predict the movement of soybean aphid could include
the use of HYSPLIT forecast trajectories. Here, we used archived weather information to
compute forward trajectories between points where aphids with matching genotypes were
collected during our study.
Methods
Field collection
In Pennsylvania, Aphis glycines was collected weekly in an unsprayed soybean field at
the Russell E. Larson Agricultural Research and Extension Center in Centre County (2009 and
2010) and on a Penn State farm near the University Park Airport (2010). At least 30 randomly
selected plants > 1 m apart were searched, and any aphids found were collected and preserved in
70% EtOH. Fields were checked weekly for aphids beginning June 1. Collecting began when
aphids first appeared in the field (late June or early July) and continued biweekly until September.
Cooperators in other states/provinces (New York, Virginia, Ontario and Quebec) collected aphids
51
in the same manner at one or two time points and sent the samples to University Park (Table 3-1
and Figure 3-1).
Laboratory Methods
The collected aphids were taken to Dr. Michel’s lab at OARDC for analysis as per the
methods in Michel et al. 2009a and Michel et al. 2009b. Aphid genomic DNA was extracted
using the E.Z.N.A. Tissue DNA kit (Omega Bio-tek, Norcross, GA), isolated, tagged with
fluorescent primers, and amplified using PCR (PCR cycling in Michel et al 2009a). Six
microsatellites described in Michel et al (2009) were used in this study: Ago 66, Ago 69, Ago 89,
AF 85, AF 181, and AF I. Microsatellite genotyping was performed using the Beckman-Coulter
CEQ8800XL (Fullerton, CA) at the Molecular Cellular and Imaging Center at OARDC (Wooster,
OH) and individual genotypes were manually scored using the CEQ Fragment Analysis software
(version 9.0.25).
Seventeen of the SNPs developed and tested in Bai et al (2010) and Orantes et al (2012)
were used on the 2010 samples. The 2009 samples were unavailable to be reanalyzed with the
SNPS because they were lost during the 2010 Wooster tornado.
Statistical Methods
Fst values and the Bonferroni corrected P-values were calculated using Microsatellite
Analyzer 4.05 (Dieringer et al 2003). Fst is the mean reduction in heterozygosity in a
subpopulation relative to the entire population. It is the measure of the extent of genetic
differentiation among subpopulations. Fst values range from 0, which indicates no differentiation
52
to 1, which indicates complete differentiation. Clone analysis, genetic distance, heterozygosity,
and principle component analysis was completed with GenAlEx 6.4 (Peakall and Smouse 2006).
HYSPLIT methods
We calculated trajectories for dates and locations in 2009 and 2010 where we observed
matching haplotypes using archived data through the online HYSPLIT program (accessible at:
http://ready.arl.noaa.gov/HYSPLIT_traj.php). For this study, we selected one starting location
and an ensemble trajectory, which creates 27 trajectories from one point by offsetting each one
slightly. This enabled us to estimate the range of possible ending locations from each starting
point, as opposed to the simple trajectory which would only give one line from the starting point.
We calculated the archived forward trajectories for 3 scenarios in 2009 and one in 2010
(Table 3-9). The trajectory starting location was one of our fields in Pennsylvania where we
found an aphid with a genotype that matched to another location. We used the EDAS 40km data
for the relavent time periods, and set up the model parameters (see Appendix A). The following
changes were made to the default settings: total run time was 48 hours, starting height was 100 m
AGL (above ground level), yes to ‘plot color trajectories,’ and label interval of 24 hours
(screenshots of the procedure in Appendix A). We produced trajectory maps for all of the dates in
the month before the last matching aphid was collected.
We scored the maps we generated on the basis of how many of the forward trajectories
crossed the location of the other matching aphid catch. The scores range from 0 to 1, with a score
of 0 indicating that none of the trajectories on that map crossed the target location, and a score of
1 indicated that all of trajectories crossed the target location.
53
Results
Spatial
The PCA of the 2009 data indicates clustering over a large spatial scale, where 60% of
the total variation was explained along the first axis (Figure 3-2). Populations from VA and NY
clustered away from populations in central PA along this axis. However, the pairwise genetic
differentiation (Fst) between A. glycines populations in Pennsylvania (PA), Virginia (VA) and
New York (NY) in 2009 ranged from 0.003 to 0.045 and were not significant (Table 3-2). This
indicates low genetic differentiation between our sampled subpopulations. If all of the PA
collection dates were combined into one population and compared with NY (Table 3-3), there
was a low (0.020) but significant Fst value.
When comparing this data with data from the Midwest, significant Fst values emerged
(Table 3-5). The four PA collection dates were all significantly different from the early
collections in OH-G, MN-L, SD, OH-T, and ON and the Fst values ranged from 0.056 (PA June
30 vs. OH-G1) to 0.235 (PA July 13 vs. SD-1). The earliest PA collection (June 30) was not
significantly different from the early collection dates in MN-R, MI, and WI with Fst values of
0.031, 0.034 and 0.050 respectively. The PCA of the East and Midwest populations shows some
clustering on the first axis (58.2% of the variation) with the PA, NY, and VA populations in a
group separate from the majority of the Midwest populations (Figure 3-4). Of the Midwest
populations, the early South Dakota one is separate from both groups. The second axis (20.6% of
the variation) does not appear to separate the populations, except early collection from South
Dakota.
For 2010, we analyzed aphids collected from Pennsylvania, Quebec, Ontario and New
York using 17 SNPs. The first axis of the PCA of these populations explains 47.1% of the
54
variation and indicates some level of differentiation over a large spatial scale with the Canadian
populations in a group separate from the US populations (Figure 3-3). This separation was
supported by the Fst values which were significantly different between PA and the Canadian
samples (Table 3-6, 5 of 17 comparisons with P < 0.05) and also on the whole larger than the Fst
values between PA populations (0.336 Sherrington QC vs. Rock Springs Aug 30, 0.097 Rock
Springs Jul 5 vs. Rock Springs Aug 30).
We also compared the 2010 samples from the East (PA, Quebec, Ontario, NY) with the
2009 Midwest samples using 10 SNPs that were shared between both analyses. The PCA of this
grouping clearly shows the differentiation between these populations (Figure 3-5). The Eastern
populations are all to the left of zero on the first axis (69.7% of the variation) and the Midwest
populations are to the right. Within the Midwest group, they do separate temporally on the second
axis (12.8% of the variation) with the early collections on the edges of the group and the later
collections coalescing at the center. All of the Fst values between the East and the Midwest were
significantly different (P<0.05) and ranged from 0.090 to 0.213 (Table 3-5).
We looked at the number of unique (occurring only once) and matching (occurring more
than once) genotypes per population to get an assessment of genotypic diversity (Table 3-4 and 3-
8). In addition to the high genotypic diversity reported from the PA sites, genotypic diversity was
also high (0.81 to 0.97) for the non-PA sites.
In 2009, 60% (108) of the individuals were distinct clones, while 40% of the individuals
were not distinct clones (Table 3-4). Four matching clones were found in both PA and NY, one
clone was found in both PA and VA, and one clone was found in both NY and VA. The VA/PA
clone was present during all sampling dates in PA and was one of the two most common clones
(both had frequencies of 2.7% or 5 individuals). With the incorporation of SNPs for the 2010 data
(Table 3-8), the number of matching clones decreased. There was only one clone that was shared
55
over a long distance (between Ottawa and PA Airport), and one clone that was shared over a
shorter distance (between Rock Springs and Airport, ~15 miles).
Temporal
In 2009, a PCA of the microsatellite data suggested differentiation among collection
dates in PA along the second orthogonal axis (Fig. 3-2), with the earliest (Jun 30) collection
separating from the latest (Aug 31) collection, but Fst values ranged from 0 to 0.01 (Table 3-2)
and there was no significant differences (P > 0.05, Bonferroni corrected). In 2010, a PCA of the
SNP data (Fig 3-3) also shows some separation by collection date with early (Jul 2 and Jul 5)
samples separating from the late (Aug 30 and Aug 31) samples along a combination of both axes.
Fst values in 2010 were also low, but ranged up to 0.097, and they were significant (P < 0.05,
Bonferroni corrected) when comparing collections in PA from Jul 2 with Aug 30, and Jul 5 with
both Aug 2 and Aug 30 (Table 3-7).
Genotypic diversity among dates in PA samples was high in both years [>= 0.86 when
using the microsatellite data in 2009 (Table 3-4), and >= 0.81 when using the SNP data in 2010
(Table 3-7)]. The genotypic diversity decreased from 0.97 to 0.86 over time in 2009 (Table 3-4),
but the opposite trend (an increase from 0.81 to 1.00) occurred in 2010 (Table 3-8). Overall, for
both years regardless of temporal trend, genotypic diversity was high. In 2009, there were 16
clones found in the Rock Springs collections that were shared temporally. The 2010 Airport
samples had clones that were shared over time.
56
Aerobiology
When there were matching clones between different sites (e.g. PA and VA), we used
HYSPLIT to determine if recent weather systems could have been responsible for their
immigration. An example of this is shown in Figure 3-6. To obtain this figure, we ran forward
ensemble trajectories at 48 hour intervals for the month of July to see if there were any possible
weather patterns that could have deposited aphids between our study sites in PA and VA.
Ensemble trajectories are useful because the model initiates multiple trajectories from the same
point that are slightly offset resulting in a plume rather than a single path. Figure 3-6 (top left) is a
forward trajectory ensemble originating at Rock Springs, PA on July 13 and running for 48 hours
at 100 meters above ground level. Eighteen of 27 of the trajectories crossed the collection site in
Virginia, resulting in a score of 0.67 on a scale of 0 to 1. Additional examples are displayed in Fig
3-6 that represent a range of scores.
Discussion
Based on these results, we demonstrated that the genotypic diversity of soybean aphids in
Pennsylvania was very high (0.81 to 1.00). In 2009, diversity decreased over time, but in 2010 the
opposite occurred, therefore we could not define a consistent temporal trend in genotypic
diversity. Genotypic diversity in a field is influenced by how many different clones initially
colonize a field, how successful those clones are, and how many clones colonize the field as the
season progresses and their success. Our results showing high initial diversity indicate that we
have many clones colonizing our fields, and a few of them are present as the season progresses.
As the season progresses, some aphid clones are maintained, others are new migrants, and some
clones die or are not resampled. The asexual reproduction of the aphids on soybean has the
57
potential to saturate a field with successful clones, thus decreasing its diversity, but we did not
observe this.
On a spatial scale, it appears that we can use molecular markers to detect long distance
movements. As we increased our markers from 6 microsatellites to 17 SNPs, it became harder to
find shared clones over long distances. The aphids we collected in PA matched with NY and
Ottawa very few times. We did observe clone matching between PA and VA and NY and VA
(once for each), which suggests that we could use clone-matching to estimate the natal source of
VA aphids since the VA collection site is well outside the range of Rhamnus cathartica. The data
suggest that with comparisons to more populations we could successfully estimate the natal
source of soybean aphid populations in PA.
After including a dataset containing Midwest collections from 2009 and both
microsatellite and SNP information, we could form a more comprehensive picture of aphid
movement to PA. The Midwest data analyzed here was first used in Orantes et al (2012). In that
paper, the authors observed lower levels of genotypic diversity in the early season collections
(0.68 – 0.97), and overlapping but higher levels in the late season collections (0.87 – 1.00). The
genotypic diversity from the collections in PA, NY, Canada (2010) and VA (2009) more closely
resembled that of the late season Midwest collections (0.86 – 0.97 in 2009, 0.81 – 1.00 in 2010).
This high genetic diversity and the lack of genetic differentiation between populations sampled in
PA suggest high levels of aphid movement (colonization and recolonization of fields), and the
lack of a solid local population colonizing from surrounding buckthorn in the spring. Early season
aphid density in soybean fields was found to be best predicted by the amount of buckthorn in the
surrounding landscape in close proximity to the fields (Bahlai et al 2010). The low early season
colonization densities we observed, combined with the high genotypic diversity, would be
consistent with relatively rare colonization events expected from long distance migrants.
58
There are multiple avenues for future work necessary to successfully integrate molecular
identification techniques and aerobiological modeling into a useful management and risk
assessment tool. One would be the need to have a concerted effort to accurately represent the
geographic range of buckthorn, the primary host. Since buckthorn is the limiting factor in where
populations can overwinter, understanding its range is key to identifying local sources of the
aphid. There are many assumptions inherent to modeling and predicting long distance aphid
movement including the assumption that aphids actually enter the air column (demonstrated with
the suction trap network, Schmidt et al 2012) and are then deposited at some point along the way.
Programs like HYSPLIT give a good visualization of where air parcels are going and with further
investigation could be a useful forecasting tool.
The continued development of molecular techniques to identify aphid populations will be
of use with the emergence of soybean cultivars with aphid resistance traits, and the subsequent
aphid biotypes with resistance characters of their own. Also, if soybean aphid ever branches out
to use any of the other Rhamnus species present in the landscape, these tools could be used to
identify biotypes or subspecies. This merging of molecular techniques and aerobiology is not
limited to this system, and could be expanded to other economically important pests.
References
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buckthorn, Rhamnus cathartica L., near Saskatoon, Saskatchewan. Canadian Field
Naturalist 111:617-621.
Bahlai CA, Sikkema S, Hallett RH, et al. (2010) Modeling distribution and abundance of soybean
aphid in soybean fields using measurements from the surrounding landscape. Environmental
Entomology 39:50–6.
Bai X, Zhang W, Orantes L, et al. (2010) Combining next-generation sequencing strategies for
rapid molecular resource development from an invasive aphid species, Aphis glycines. PloS
One 5:e11370.
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Bodreau D (1992) Buckthorn research and control at Pipestone National Monument (Minnesota).
Restoration and Management Notes 10:94-95.
Brewer MJ, and Noma T (2010). Habitat affinity of resident natural enemies of the invasive Aphis
glycines (Hemiptera: Aphididae), on soybean, with comments on biological control. Journal
of Economic Entomology 103(3): 583-596.
Converse CK (1985) Element Stewardship Abstract for Rhamnus cathartica, Rhamnus frangula
(syn. Frangula alnus). The Nature Conservancy.
Costamagna AC, and Landis DA (2011) Lack of strong refuges allows top-down control of
soybean aphid by generalist natural enemies. Biological Control 57: 184-192.
Dieringer D, Schlštterer C (2003) Microsatellite analyser (MSA): a platform independent analysis
tool for large microsatellite data sets. Molecular Ecology Notes. 3 (1): 167-169.
Dixon, AFG (1985) Aphid Ecology. Blackie & Son Ltd
Draxler RR, Rolph GD (2012) HYSPLIT (HYbrid Single-Particle Lagrangian Integrated
Trajectory) Model access via NOAA ARL READY Website
(http://ready.arl.noaa.gov/HYSPLIT.php). NOAA Air Resources Laboratory, Silver Spring,
MD.
Gildow FE, Shah DA, Sackett WM, et al. (2008) Transmission efficiency of Cucumber mosaic
virus by aphids associated with virus epidemics in snap bean. Phytopathology 98:1233-41.
Hodgson EW, McCornack BP, Tilmon K, Knodel JJ (2012) Management recommendations for
soybean aphid (Hemiptera: Aphididae) in the United States. Journal of Integrated Pest
Management 3:1–10.
Isard SA, Russo JM, and Ariatti A (2007) The integrated aerobiology modeling system applied to
the spread of soybean rust into the Ohio River valley during September 2006. Aerobiologia
23: 271-282.
Kurylo JS, Knight KS, Stewart JR, Endress AG (2007) Rhamnus cathartica: Native and
naturalized distribution and habitat preferences. Journal of the Torrey Botanitcal Society
134:420-430.
Michel AP, Zhang W, Jung JK, et al. (2009a) Cross-species amplification and polymorphism of
microsatellite loci in the soybean aphid, Aphis glycines. Journal of Economic Entomology
102:1389–92.
Michel AP, Zhang W, Kyo Jung J, et al. (2009b) Population genetic structure of Aphis glycines.
Environmental Entomology 38:1301–11.
Müller CB, Williams IS, Hardie J (2001) The role of nutrition, crowding and interspecific
interactions in the development of winged aphids. Ecological Entomology 26:330–340.
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Myers JA, Vellend M, Gardescu S, Marks PL (2004) Seed dispersal by white-tailed deer:
implications for long-distance dispersal, invasion, and migration of plants in eastern North
America. Oecologia 139:35-44.
Nault BA, Shah DA, Straight KE, et al. (2009) Modeling temporal trends in aphid vector
dispersal and cucumber mosaic virus epidemics in snap bean. Environmental Entomology
38:1347-59.
Orantes LC, Zhang W, Mian MAR, Michel AP (2012) Maintaining genetic diversity and
population panmixia through dispersal and not gene flow in a holocyclic heteroecious aphid
species. Heredity 1-8.
Peakall R, Smouse PE (2006) GENALEX 6: genetic analysis in Excel. Population genetic
software for teaching and research. Molecular Ecology Notes. 6: 288-295.
Ragsdale DW, Landis DA, Brodeur J, et al. (2011) Ecology and management of the soybean
aphid in North America. Annual Review of Entomology 56:375–99.
Ragsdale DW, Voegtlin DJ, O’Neil RJ (2004) Soybean Aphid Biology in North America. Annals
of the Entomological Society of America 97:204–208.
Rice ME and O’Neal ME (2008) Soybean aphid management field guide 2008.University
Extension, Iowa State University.
Schmidt NP, O’Neal ME, Anderson PF et al (2012) Spatial distribution of Aphis glycines
(Hemiptera: Aphididae): a summary of the suction trap network. Journal of Economic
Entomology 105(1): 259-271.
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Soybean Management and Research Technology, Michigan State University Extension.
Song F, Swinton SM, Difonzo C, et al. (2006) Profitability analysis of soybean aphid control
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Stunder BJB, Heffter JL, and Draxler RR (2007) Airborne volcanic ash forecast area reliability.
Weather and Forecasting 22: 1132-1139.
Tilmon KJ, Hodgson EW, O’Neal ME, Ragsdale DW (2011) Biology of the soybean aphid, Aphis
glycines (Hemiptera: Aphididae) in the United States. Journal of Integrated Pest
Management 2:1–7.
Voegtlin DJ, O’Neil RJ, Graves WR (2004) Tests of suitability of overwintering hosts of Aphis
glycines: identification of a new host association with Rhamnus alnifolia L’Héritier.”
Annals of the Entomological Society of America 97(2): 233-234.
Voegtlin DJ, O’Neil RJ, Graves WR, et al. (2005) Potential winter hosts of soybean aphid.
Annals of the Entomological Society of America 98:690-693.
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Wang Y, Stein AF, Draxler RR et al (2011) Global sand and dust storms in 2008: Observation
and HYSPLIT model verification. Atmospheric Environment 45(35): 6388-6381.
Yoo HJS, O’Neil RJ, Voegtlin DJ, Graves WR (2005) Host plant suitability of Rhamnaceae for
soybean aphid (Homoptera: Aphididae). Annals of the Entomological Society of America
98:926-930.
Zhang W and Swinton SM (2009) Incorporating natural enemies in an economic threshold for
dynamically optimal pest management. Ecological Modelling 220: 1315-1324.
62
Figures and Tables
Table 3-1. Aphid collection dates and locations for 2009 and 2010.
Location Year Date(s) GPS coordinates
PA – Rock Springs 2009 Jun. 30, Jul. 13, Aug. 10 + 31 40.71. -77.94
NY 2009 Aug. 6 42.89, -77.92
VA 2009 Aug. 12 37.50, -76.60
PA – Rock Springs 2010 Jul. 5, Aug 2 + 30 40.71. -77.94
PA – Airport 2010 Jul. 2, Aug. 2 + 31 40.85, -77.84
NY 2010 Jul. 29 43.17, -78.14
Ottawa, ON 2010 Sept. 1 45.39, -75.72
Hungry Bay, QC 2010 Aug. 26 45.19, -74.15
Sherrington, QC 2010 Aug. 26 45.10, -73.53
Table 3-2. Fst values for Pennsylvania, Virginia and New York 2009. None of the Fst values
were significant at P < 0.05.
VA NY PA Jun 30 PA Jul 13 PA Aug 10 PA Aug 31
VA -- 0 0.011 0.004 0.024 0.003
NY -- 0.016 0.012 0.045 0.015
PA Jun 30 -- 0 0.002 0.014
PA Jul 13 -- 0 0.002
PA Aug 10 -- 0.010
PA Aug 31 --
Table 3-3. Fst values for Pennsylvania (all collection dates combined), Virginia and New York
2009. Significant values (P < 0.05) indicated in bold.
VA NY PA
VA -- 0 0.008
NY -- 0.020
PA --
63
Table 3-4. Genotypic diversity for aphid populations collected in Pennsylvania, Virginia, and
New York 2009.
Population No. matching genotypes
No. unique genotypes
Total no. genotypes
Genotypic Diversity*
VA 4 24 28 0.93
NY 8 18 26 0.86
PA Jun 30 13 16 29 0.97
PA Jul 13 11 17 28 0.93
PA Aug 10 10 16 26 0.86
PA Aug 30 9 17 26 0.86
*Genotypic diversity = # genotypes – 1/N – 1
Table 3-5. Fst values for Pennsylvania, Virginia, New York and Midwest sites 2009. Significant values (P < 0.05) indicated in bold.
Abbreviations: MN-R, Minnesota – Rosemont; OH-G, Ohio-Wooster; MI, Michigan; SD, South Dakota; OH-T, Ohio-Cortland; ON, Ontario; WI, Wisconsin. 1 indicates
early collection, 2 indicates late collection.
65
Table 3-6. Fst values for Pennsylvania, Canada, New York (2010) and Midwest sites 2009. Significant values (P < 0.05) indicated in bold.
Abbreviations: MN-R, Minnesota – Rosemont; OH-G, Ohio-Wooster; MI, Michigan; SD, South Dakota; OH-T, Ohio-Cortland; ON, Ontario; WI, Wisconsin; RS, Rock
Springs; AP, Airport; NY, New York; SHER, Sherrington; HGY, Hungry Bay. 1 indicates early collection, 2 indicates late collection.
Table 3-7. Fst values for PA, NY, and sites in Canada from 2010 collections. Significant values
(P < 0.05) indicated in bold.
Table 3-8. Genotypic diversity for aphid populations collected in Pennsylvania, Canada, and New
York 2010.
Population No. Matching genotypes
No. unique genotypes
Total no. genotypes
Total no. samples
Genotypic diversity*
Airport Jul 2 10 16 26 32 0.81
Airport Aug 2 7 22 29 30 0.97
Airport Aug 31 6 19 25 31 0.81
Rock Springs Jul 5 5 21 26 30 0.87
Rock Springs Aug 2 1 28 29 30 0.97
Rock Springs Aug 30 0 32 32 32 1.00
Sherrington Quebec 4 22 26 32 0.81
Ottawa 2 28 30 31 0.97
Hungry Bay Quebec 2 25 27 32 0.84
New York 1 30 31 32 0.97
*Genotypic diversity = # genotypes – 1/N – 1
67
Table 3-9. Score from 0 to 1 of forward trajectories that cross the target location from the
HYSPLIT maps. 142 date/location scenarios were evaluated and dates where none of the
trajectories crossed the target location (score of 0) are not shown on this table. Maps were
generated from July 1-31 2009 for PA to VA and PA to NY, and July 1 – August 9 2009 for NY
to VA and NY to PA.
Start location Target end location Map date Score
PA VA
7/5/2009 0.07
7/7/2009 0.04
7/8/2009 0.37
7/13/2009 0.67
7/14/2009 0.11
NY VA 7/8/2009 0.11
8/9/2009 0.07
NY PA 7/8/2009 0.04
8/6/2009 0.04
PA NY
7/1/2009 0.04
7/10/2009 0.52
7/11/2009 0.52
7/22/2009 0.15
7/23/2009 0.30
7/25/2009 0.52
Figure 3-1. Map of A. glycines collection locations. Sites with a black circle were used in 2009. Sites with a black diamond were used in
2010. Sites with a black star are 2009 collections from Orantes et al (2012). Rock Springs was used in both years.
Figure 3-2. Principal component analysis based on Fst of the aphid populations collected in
Pennsylvania, New York, and Virginia 2009 showing spatial (primary axis) and temporal
(secondary axis) differentiation. The dotted lines group the populations that separated spatially.
VA
NY
PA Jun. 30
PA Jul. 13 PA Aug. 10
PA Aug. 31
-0.060
-0.040
-0.020
0.000
0.020
0.040
0.060
-0.080 -0.060 -0.040 -0.020 0.000 0.020 0.040 0.060 0.080
PC
2:
22
.93
% o
f va
riat
ion
PC 1: 60.61% of variation
70
Figure 3-3. Principal component analysis based on Fst of the aphid populations collected in
Pennsylvania, New York, and Canada in 2010 showing spatial (primary axis) and temporal
(secondary axis) differentiation. The dotted lines group the populations that separated spatially.
Rock Springs Jul. 5
Airport Jul. 2
Rock Springs Aug. 30
Airport Aug. 31 New York
Rock Springs Aug. 2
Airport Aug. 2
Sh Quebec
HB Quebec
Ottawa
-0.050
-0.040
-0.030
-0.020
-0.010
0.000
0.010
0.020
0.030
-0.060 -0.040 -0.020 0.000 0.020 0.040 0.060
PC
2:
18
.08
% o
f va
riat
ion
PC 1: 47.12% of variation
71
Figure 3-4. Principal component analysis based on Fst of populations collected in Pennsylvania,
New York, Virginia, and the Midwest in 2009 showing spatial (primary axis) and temporal
(secondary axis) differentiation. The dotted lines group the populations that separated spatially.
MN-R1
OH-G1
MN-L1
MI-1
SD-1
OH-T1
ON-1
WI-1
MI-2
WI-2 OH-T2
MN-R2
MN-L2
ON-2
OH-G2 SD-2
VA
NY PA Jun. 30
PA Jul. 13
PA Aug. 10
PA Aug. 31
-0.100
-0.080
-0.060
-0.040
-0.020
0.000
0.020
0.040
0.060
-0.100 -0.050 0.000 0.050 0.100 0.150
PC
2:
20
.6 %
var
iati
on
PC 1: 58.2 % variation
72
Figure 3-5. Principal component analysis based on Fst of populations collected in Pennsylvania,
New York and Canada in 2010 and the Midwest sites 2009 showing spatial (primary axis) and
temporal (secondary axis) differentiation. The dotted lines group the populations that separated
spatially.
MN-R1
OH-G1
MN-L1
MI-1
SD-1
OH-T1
ON-1
WI-1
MI-2
WI-2
OH-T2
MN-R2
MN-L2 ON-2
OH-G2
SD-2
RS-EAR
RS-MID
RS-LT
AP-EAR
AP-MID AP-LT
NY SHERQC
HGYQC
Ontario
-0.100
-0.080
-0.060
-0.040
-0.020
0.000
0.020
0.040
0.060
0.080
-0.150 -0.100 -0.050 0.000 0.050 0.100 0.150
PC
2:
12
.75
% v
aria
tio
n
PC 1: 69.71% variation
73
Figure 3-6. Examples of HYSPLIT forward trajectory maps over a 48 hour time period,
clockwise from top left; PA to VA 7/13/2009 (score 0.67), NY to VA 7/8/2009 (score 0.11), PA
to NY 7/10/2009 (score of 0.52), and PA 7/12/2009 (score of 0).
Chapter 4
Speciation and population structure of Therioaphis trifolii
Introduction
Therioaphis trifolii (the spotted alfalfa aphid) is an aphid species that was introduced to
the United States and became important because of its abundance and preference for a legume
crop (alfalfa). Like A. glycines, it is a competent vector of CMV in snap bean (Gildow et al. 2008,
Nault et al. 2009). The spotted alfalfa aphid (referred to as SAA and formerly of the species
Therioaphis maculata) was first observed in the United States in 1954, in New Mexico on alfalfa.
It spread rapidly through the Southwest and on to the Eastern states (Dickson 1958). Its
introduction and subsequent unexpected damage to crops mirrors that of A. glycines. Since it has
been in the U.S. for over 50 years, its populations have equilibrated and it is no longer the cause
of extreme economic loss in its host crops of alfalfa and clover. T. trifolii can serve as a model
invasive aphid species and give insight into the future of A. glycines.
Recognition of T. trifolii as a single species is in flux. In 1958, T. trifolii was indicated as
the yellow clover aphid (YCA), and SAA was considered a different species (T. maculata).
Examination of morphological characters in Dickson (1958) resulted in the conclusion that SAA
populations probably came from a single or small colony introduction. The population in 1958
had also developed pesticide resistance in approximately four generations. Host plant differences
between YCA (in clones from red clover) and SAA (in clones from alfalfa) were also observed
(Dickson 1958, Manglitz and Russell 1974). The initial observations of SAA populations
described it as anholocyclic, but holocyclic (producing sexual morphs and overwintering as eggs
instead of asexual adults) populations were observed in the northern part of its new range
75
(Manglitz 1966). SAA was believed to be introduced from the Mediterranean region where sexual
morphs did not occur, but sexual morphs do occur in colder regions of the Old World range.
Current classifications consider both SAA and YCA to be in the same species, T. trifolii
(Blackman and Eastop 2000, 2006, Carver 1978).
We set out to compare T. trifolii samples from Pennsylvania and Serbia to see if there
was any evident population structure that would reflect the species’ history of introduction.
Methods
We collected at least 30 aphids from two sites in Pennsylvania (the Russell E. Larsen
Reasearch Farm in Centre County, and the Kretschman farm near Pittsburgh) and obtained aphids
collected by Olivera Petrovic-Obradovic in Serbia. Aphids were collected from alfalfa fields and
individuals were preserved singly in 70% EtOH. DNA was extracted using the E.Z.N.A. Tissue
DNA kit (Omega Bio-tek, Norcross, GA), and run in a PCR with one of three general insect
mitochondrial primers (LR, NS, and SR).
The PCR product was sequenced, and the resulting sequences were aligned in BioEdit
(Hall 1999), and TCS (Clement et al 2000) was used to create a haplotype web.
Results
Two of the primers (LR, SR) produced useable sequences. The haplotype web from TCS
for each of the primers resulted in seqences obtained from aphids in Pennsylvania being the
ancestral haplotype for this collection (Figures 4-1 and 4-2).
76
Discussion
These results represent a summary of the literature and a preliminary attempt to use
genetic tools to elucidate the relationships between T. trifolii in the Old and New World. Our
samples were from a limited geographic range, and did not indicate clear population-level
differences.
Since T. trifolii was introduced on separate occasions and geographically distinct
locations, and then flourished for decades it serves as an interesting potential model for the use of
genetic tools to identify population change in an introduced aphid species. There is speculation
that A. glycines is moving to new primary hosts which could result in sympatric speciation over
time, especially since sexual reproduction is only occurring on the primary host. Future work
should include more intensive sampling on clover and alfalfa as well as a broader range of
geographic regions, and the development and use of species-specific SNPs or microsatellites.
References
Blackman R, Eastop V (2000) Aphids on the World’s Crops: An Identification and Information
Guide, 2nd ed. Wiley, Chichester.
Blackman R, Eastop V (2006) Aphids on the World’s Herbaceous Plants and Shrubs: An
Identification Guide. Wiley, Chichester.
Carver M (1978) The scientific nomenclature of the spotted alfalfa aphid (Homoptera:
Aphididae). Journal of the Australian Entomological Society 17: 287-288.
Clement M, Posada D, Crandall K. (2000) TCS: a computer program to estimate gene
genealogies. Molecular Ecology 9(10): 1657-1660
Dickson RC (1959) On the identity of the spotted alfalfa aphid in North America. Annals of the
Entomological Society of America 52:63-68.
Gildow FE, Shah DA, Sackett WM, et al. (2008) Transmission efficiency of Cucumber mosaic
virus by aphids associated with virus epidemics in snap bean. Phytopathology 98:1233-41.
77
Hall, TA (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis
program for Windows 95/98/NT. Nucleic Acids Symposium Series 41: 95–98.
Librado P, Rozas, J (2009) DnaSP v5: a software for comprehensive analysis of DNA
polymorphism data. Bioinformatics 25: 1451-1452
Manglitz G, Calkins C, Walstrom R, et al. (1966) Holocyclic strain of the spotted alfalfa aphid in
Nebraska and adjacent states. Journal of Economic Entomology 59:636-639.
Manglitz GR, Russell LM (1974) Cross matings between Therioaphis maculata (Buckton) and T.
trifolii (Monell) (Hemiptera: Homoptera: Aphididae) and their implications in regard to the
taxonomic status of the insects. Proceedings of the Entomological Society of Washington
76(3): 290-296.
Nault BA, Shah DA, Straight KE, et al. (2009) Modeling temporal trends in aphid vector
dispersal and cucumber mosaic virus epidemics in snap bean. Environmental Entomology
38:1347-59.
78
Figures and Tables
Figure 4-1. The relationship between haplotypes derived using the LR primer for T. trifolii
collected in Pennsylvania and Serbia. In the web, each line as well as each small dot is one base
pair of difference.
79
Figure 4-2. The relationship between haplotypes derived using the SR primer for T. trifolii
collected in Pennsylvania and Serbia. In the web, each line as well as each small dot is one base
pair of difference.
Chapter 5
Phenology model validation of pests of cucurbits
Introduction
Members of the plant family Cucurbitaceae are widely distributed and well-represented
in global agriculture (Bates 1990). In 2007, over ten-thousand acres of cucurbit crops were
harvested in Pennsylvania (USDA NASS, 2009). Of these crops, pumpkins take up the most
acreage and were valued at over $14.5 million in 2011 (USDA NASS, 2011).
Growing cucurbits is challenging due to the presence of three specialist insect pests;
striped cucumber beetle (Acalymma vittatum Fabricius), squash bug (Anasa tristis DeGeer) and
squash vine borer (Melittia cucurbitae Harris). Not only do these pests cause direct feeding
damage to the plants, but striped cucumber beetle and squash bug each vector bacterial
pathogens.
The striped cucumber beetle is important during the growing season because of its direct
feeding damage to vines and fruit as well as its ability to vector E. tracheiphila, the causal agent
of bacterial wilt. E. tracheiphila is a bacterium that proliferates in the xylem which blocks water
flow in the plant causing the characteristic wilting on the leaves on the infected stem. Eventually,
the bacteria will spread through the entire plant and kill it. Systemic and foliar pesticide sprays
are used in conventional systems against the adults. Larval mortality can be increased by growing
cucurbits on black plastic with drip irrigation and introducing entomopathogenic nematodes
through the drip line (Ellers-Kirk et al 2000).
The squash bug is an important pest of Cucurbita but not Cucumis species (Bonjour and
Fargo 1989). Squash bugs inflict feeding damage on vines and fruit, and can vector Serratia
81
marcescens, the causal agent of cucurbit yellow vine disease, another bacterial cucurbit disease
(Bruton et al 2003). S. marcescens causes yellowing of the leaves, phloem discoloration and a
general decline in plant vigor. During the growing season, the lifecycle occurs above ground,
making it easier to observe egg masses, juvenile instars, and subsequent adult generations.
Common cucurbit cultivation practices include the use of mulch (usually plastic), which provides
harborage for squash bug adults and nymphs, thus increasing pest pressure (Cartwright et al.
1990).
Squash vine borer adults do not damage cucurbit crops, but the larvae burrow into the
cucurbit vine where they feed. Larvae can survive well on Cucurbita pepo and C. maxima
cultivars (Howe and Rhodes 1973), and short of growing unsuitable host plants the most effective
way to control this insect is to apply foliar insecticides when first instars are present before they
enter the vine. These insects overwinter as pupa, and emerging adults can be monitored with
pheromone traps. The pheromones in the lures overlap with those of other Sessiid species (Van
Wychen Bennett et al, 2011) sometimes resulting in non-target captures.
Striped cucumber beetle and squash bug overwinter as adults in leaf litter and/or crop
residue and resume activity in the spring. When they become active again is not well defined.
Radin and Drummond (1994) suggest that the striped cucumber beetles can be active on any day
with an average temperature above 12 C (53. 6 F) in Maine, and Lewis et al. 1990 found beetle
activity on flats of C. maxima cv. Blue Hubbard when temperatures were above 18 C (64.4 F).
Previous trapping efforts in Pennsylvania at Rock Springs caught beetles in emergence cages in
mid-May (Fleischer, unpublished). The cue that causes these beetles to enter diapause in the fall
is unknown. Following striped cucumber beetle adult immigration, we projected the timing of
emergence of the first field generation (F1) of adults as 793.6 degree-days base 55 F (Ellers-Kirk
and Fleischer 2006). The timing of emergence for subsequent field generations adds the 204.88
82
DD base 55 F preoviposition period to the egg to adult development time (Ellers-Kirk and
Fleischer 2006).
Although it is currently unknown how squash bugs terminate diapause, it is known that
the bugs enter diapause when the critical photoperiod decreases between 14:10 (L:D) and
14.5:9.5 (Decker and Yeargan 2008, Nechols 1988). Egg to adult development was determined to
be 725.1 degree-days base 60 F (Fargo and Bonjour 1988).
Squash vine borer overwinters as a late instar larva or pupae and has an extended
emergence through the growing season. It requires 1687.5 degree days base 50 F to complete
development (Canhilal et al. 2006).
In the case of these cucurbit pests, predicting early season activity can be useful for
optimizing planting time to give the plants an opportunityto grow past their most susceptible
growth stages without pest pressure. For striped cucumber beetle and squash bug, we are defining
early season activity as the recruitment of the pest to a flat comprised of cucurbit seedlings. These
two pests are active in the spring before crops are planted, and monitoring their presence can
estimate the intensity of early-season pest pressure. Once cucurbit crops are planted, in-field
monitoring identifies the time of colonization (the biofix) for each pest. Using degree day
developmental requirements from the literature for each pest, we can use the biofix, forecast air
temperatures, and 30-year climatology records to estimate the emergence of subsequent field
generations. Since cucurbits require the services of pollinators, predicting pest levels in the
growing season is important for timing pest control measures while still allowing for adequate
pollination.
For this research, we used air temperature degree days from January 1 as a starting point,
because they are well monitored and accessible through meteorological databases. We are using
weather station data since it is widely available and part of several established networks that will
exist beyond the length of this study. This longevity will allow a successful model and website to
83
operate in subsequent years without the deployment of additional monitoring devices such as
independent temperature data loggers.
We set out to monitor the early season activity and growing season phenology of these
pests on land that is transitioning to organic production on research farms in Pennsylvania, Iowa
and Kentucky on two commercially important cucurbit crops (muskmelon – Cucumis melo, and
butternut squash – Cucurbita moschata) to better inform organic management practices.
For each of these insects, knowledge of pest phenology will allow growers to more
effectively deploy organic and cultural controls such as approved pesticides, row covers, and
adjust planting and transplanting dates to times when they will be most effective.
Methods
Early season activity
To monitor the activity of the insects before the field planting of cucurbit crops, we
planted smaller containers with cucurbit seeds. The resulting flats containing seedlings and young
plants were placed on research farms with a history of growing cucurbit crops on some portion of
their land. The goal was to be able to attract and count striped cucumber beetles and squash bugs
that were active early in the season prior to the establishment of commercial cucurbit fields.
Each trap flat consisted of one standard 1020 greenhouse flat (28 by 53 cm) planted with
20 – 30 Cucurbita maxima seeds (2010 seeds from Rupp var. ‘Blue Hubbard,’ and Johnny’s
Selected Seeds ‘var. Blue Ballet,’ 2011 seeds from Johnny’s Selected Seeds var. ‘Blue
Hubbard’). These cultivars were selected for the trap flats because of their demonstrated
attractiveness as a trap crop for striped cucumber beetles (Adler and Hazzard, 2009). Flats were
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grown in a greenhouse under ambient light and watered regularly. Traps were deployed 2-3
weeks after seeding, when plants were in the first through third leaf stage.
Ten trap flats were placed in pairs around the Russell E. Larson Research and Education
Center, Pennsylvania Furnace, PA (colloquially referred to as Rock Springs), within
approximately 0.5 km of fields that were planted in cucurbits within the last year. In Kentucky
and Iowa, trap flats were placed at the University of Kentucky Organic Farming Research and
Extension Unit at the UK Horticulture Research Farm in Lexington KY and the Iowa State
University Horticulture Farm in Gilbert IA, respectively (Table 5-1). In addition to the plants,
each flat also had a 7.6 by 12.7 cm yellow sticky card (RSTRIP Gempler’s, Madison, WI)
mounted on a 30 cm wooden stake. Flats were checked three times per week for striped cucumber
beetle and squash bug activity until a research plot of cucurbits was planted, which was also the
typical timing of established commercial cucurbit fields in that area (see Table 5-1 for monitoring
duration). Trap flats were replaced weekly or earlier if they were damaged by frost or lack of
water. Any striped cucumber beetles or squash bugs found were counted and removed from the
flat.
We took the cumulative number of captured beetles to represent the timing of spring
emergence activity, and regressed cumulative capture, scaled from 0 to 1, against degree-days
base 55 Fahrenheit, using a Gompertz equation (Winsor 1932) calculated with JMP v.8 (JMP
2009). The Gompertz equation is a good model for early season
recruitment, because once a beetle is found on a trap flat, additional beetle accumulation happens
rapidly (Smyth and Hoffman 2003). For our analysis with JMP the constants k, a, and b are
represented as θ1, θ2, and θ3 respectively. In the equation, θ1 is the maximum value that y
approaches. θ2 determines the placement of the curve’s first inflection point on the x-axis and θ3
influences the steepness of the slope as the curve approaches the value of θ1. We used both
85
calendar day and cumulative degree day as the independent variable (x) and y is the proportion of
the total pest population observed.
In-season phenology
In order to monitor insect pest populations during the growing season, we planted 30.5
row meters each of Cucumis melo var. ‘Strike’ and Cucurbita moschata var. ‘Betternut’ on black
plastic with drip irrigation. Planting dates followed local production practices and ranged from
mid-May to early June (Table 5-1). Five plants per row were monitored weekly (biweekly if
insect pressure was high) for striped cucumber beetle and squash bug using visual inspection of
whole plants. Striped cucumber beetle and squash bug adults were counted in addition to the
number of squash bug juvenile instars and egg masses. A biofix for each site was defined as two
consecutive observations with non-zero counts. Plants were replaced if they died and the plot was
not sprayed to control insects.
Two wire cone Harstack traps were set up at each research farm in fields planted with
muskmelon and butternut, and baited with squash vine borer pheromone lures (Great Lakes IPM).
Lures were changed every other week. The traps were checked weekly for squash vine borer and
caught moths were removed and frozen.
Adult immigration into the phenology plots was estimated in two ways. First, we used the
model of spring activity, described above, which essentially is defined by heat unit accumulations
in the spring. Secondly, we used the sampling data within the plots to determine when striped
cucumber beetles or squash bugs were first present, defined as a mean of >0 counts per plant for
two consecutive sampling events. Following adult immigration, we projected the timing of
emergence of the first field generation (F1) of striped cucumber beetle adults as 793.6 degree-
days base 55 F (Ellers-Kirk and Fleischer 2006). The timing of emergence for subsequent field
86
generations adds the 204.88 DD base 55 F preoviposition period to the egg to adult development
time (Eller-Kirk and Fleischer 2006).
For squash bugs, we projected the timing of emergence of the first field generation (F1) of
adults as 725.1 degree-days base 60 F (Fargo and Bonjour 1988). After a 140 – 200 DD
preoviposition period (Nechols 1987), the F1 adult females were predicted to lay eggs for the next
field generation. There is also a photoperiod component to squash bug phenology, with eggs that
hatch and nymphs that reach adulthood after the critical photoperiod (14:10 (L:D) and 14.5:9.5
(Decker and Yeargan 2008, Nechols 1988)) preparing for diapause instead of reproduction.
Squash Vine Borer
Using the life table for squash vine borer recorded in Canhilal et al. 2006, we converted
the growth chamber data to degree-days base 50 F in order to estimate a predicted adult spring
emergence of 754.2 DD base 50 F (Table 5-2), and compared it to observed captures in
pheromone traps.
Meteorological Data
Degree days for each location were obtained by the Center for Environmental
Informatics, Penn State University, from meteorological station and model data. The station data
came from FAA ASOS (Federal Aviation Administration Automated Surface Observing System)
and COOP (Cooperative Observer Program) sites depending on which was closer to the farm
location. Both types of data came from NOAA (National Oceanic and Atmospheric
Administration) datastreams. The FAA data consisted of hourly or sub-hourly reports of air
temperature, dew point, precipitation, wind speed, wind direction, cloud cover, visibility, and
87
present weather from mainly airport weather stations with a day being from midnight to midnight.
The National Weather Service (NWS), FAA and Department of Defense operate the 967 stations
across the United States. The data was averaged into hourly values, which were then averaged
from midnight to midnight into daily values.
The COOP data comes from the NOAA datastream as either daily averages or daily
maximum and minimum temperatures, which were averaged to get daily temperatures (each day
begins at 7 AM). The COOP, started in 1890 under the Organic Act, which created the Weather
Bureau, is now operated by the National Weather Service (NWS). It was formed to provide
observational meteorological data that usually includes daily temperature maximums and
minimums, snowfall, and 24 hour accumulated precipitation. The goal of the COOP program is to
define the climate of the U.S. and to help measure long-term climate change. The observations,
usually recorded by volunteers, send monthly temperature and precipitation data to the National
Climatic Data Center (NCDC), where it is digitized, checked, and archived. There are over
11,000 volunteers taking observations across the United States.
The model data were from the NARR (North American Regional Reanalysis) through a
NOAA ftp server. The NCEP (National Centers for Environmental Prediction) NARR is a long-
term, dynamically consistent, high-resolution, high-frequency, atmospheric and land surface
hydrology dataset for the North American domain. The NARR model uses the very high
resolution NCEP Eta Model (32km/45 layer) together with the Regional Data Assimilation
System (RDAS) which, significantly, assimilates precipitation values along with other variables.
Examples of other variables included in the NARR dataset are: air temperature, dew point, wind
speed, specific humidity, soil temperature, soil moisture, surface downward longwave and
shortwave radiation, and surface evaporation. The output analyses are 3-hourly. We used a
program that finds the gridpoint closest to the desired latitude/longitude coordinates of the farm,
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and uses that value for the desired location. These data are then averaged into daily temperatures.
The NARR is not run in real-time (about a month delayed).
To calculate degree days, we converted the daily averages from degrees Celsius to
degrees Fahrenheit and subtracted the appropriate degree threshold (50, 55, or 60) if the daily
average was greater than the threshold depending on the insect species.
Results
Striped Cucumber Beetle
Striped cucumber beetles were first detected on average across all three states on calendar
day 127.0 (+/- 18.4 days) (Table 5-3). KY reported the beetles the earliest, on calendar day 112.3
(+/- 15.5 days), and the first capture date in IA was the most variable (133.5 +/- 24.8 days).
The average cumulative degree day base 55 F of first detection was 224.7 (+/- 62.9) over
all three states. Average cumulative degree day was most consistent for KY, with an error of +/-
33.6 degree days (Table 5-3). The variation of 59.2 degree days in PA is about 5-10 calendar days
based on 30 year climate averages.
Time of first detection on the in-season phenology plot was dependent on the date of
planting, with the beetles arriving on average 9.5 +/- 3.5 and 40.0 +/- 22.6 days after planting in
PA and IA respectively (Table 5-4).
The values for the regression coefficients from the Gompertz equations were more
consistent when degree day was used as the independent variable [standard errors ranging from
0.0013 to 0.4486 using cumulative degree day (Table 5-6) versus 0.0481 to 6.8221using calendar
day (Table 5-7)]. For calendar day, a curve calculated for each state individually gave a more
89
accurate representation of early season recruitment for that state than a curve based on all
locations combined (Figures 5-2 and 5-3).
Looking at the in-season phenology for each state, it appears that the season-long degree
day accumulations are only enough to support the development and emergence of one field
generation (Figures 5-4, 5-5, and 5-6).
The observed numbers of striped cucumber beetles per plant in the in-season phenology
plots did not show clear peaks defining a separation between the overwintered adults and the first
field generation. To further elucidate the seasonal dynamics, we divided the data based on the
biofix and egg-adult development time (Figures 5-10, 5-11, 5-12). Of the 5 state/years observed,
only one did not appear to have a second generation (Iowa 2011, Figure 5-11)
Squash Bug
Squash bugs were caught during the trap flat sampling, but not in high enough numbers
to model their activity. Squash bugs were first detected after 138.5 calendar days (+/- 12.3) and
176.2 (+/- 81.6) cumulative degree days base 60 F (Table 5-3). Calendar day at first detection was
the most variable in IA (+/- 24.8 days) and the least variable in PA (+/- 4.0 days). Degree days at
first detection were the greatest in KY (241.3) and the least in PA (125.7).
Much like the striped cucumber beetles, in-season phenology plot initial detection was
dependent on planting date with the bugs arriving on average 18.0 +/- 4.2 and 35.0 +/- 4.2 days
after planting in PA and IA respectively (Table 5-4).
The season-long degree day accumulations for each state are enough to allow for the
development of one field generation of squash bugs in Pennsylvania and Iowa (Figures 5-7 and 5-
9), and come close to allowing two field generations in Kentucky (Figure 5-8). In Kentucky, eggs
hatching after the critical photoperiod would go into diapause as adults instead of reproducing.
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Squash Vine Borer
Squash vine borer was first captured later in the spring for all locations at calendar day
169.5 and cumulative degree day base 50 F 1080.2 (Table 5.5).
Discussion
There have been several previous attempts at predicting the timing of and identifying the
cues that initiate striped cucumber beetle activity in the spring before commercially cultivated
cucurbits are present (Elsey 1988, Lewis et al. 1990, Pair 1997, Radin and Drummond 1994).
This study focused on advancing our ability to estimate early season activity using air
temperature degree days. Across a three state region, when cumulative degree days base 55 F was
used as the predictor, the first inflection point of a curve modeling recruitment to seedlings used
as traps was approximately 150 degree days, and ranged from 140 to160. Looking at the 30-year
climate and assuming that beetles begin their activity between 100 and 200 cumulative degree
days, we can estimate that as a range of approximately 11 days for PA and 10 days for IA and KY
occurring in mid to late May.
The pattern of rapid recruitment of striped cucumber beetle on the trap flats which
occurred prior to commercial transplanting on farms was consistent with the findings from Smyth
and Hoffman (2003) who measured recruitment during the summer field season. They used male
and female beetles and plants with high or low levels of cucurbitacin in different combinations to
determine ecological mechanisms of attraction. They found that the beetles oriented upwind,
especially on traps containing pioneer males (Smyth and Hoffman 2003). In our study, we had
low initial counts on the trap flats, but once beetles were found on a flat, subsequent samplings
had much higher counts (some flats had hundreds, especially in Kentucky). The pattern of the
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Gompertz equation represents this aggregative activity well, by increasing more rapidly than a
simple logistic curve (Winsor 1932).
Predicting early season activity of an insect that overwinters as an adult is challenging
without knowing the cues that are inducing the end of diapause and factors that affect spring
activity. We are using a single variable to attempt to model the start of spring activity for these
pests, and using recruitment to a trap flat as a surrogate for measuring early season activity. There
are other variables (soil temperature, day length, appearance of other food sources) that could
give more precise results once the mechanism for exiting diapause and initiating spring activity is
known.
This study also reinforced the trap flat methods used in other studies (Lewis et al. 1990
and Radin and Drummond 1994). Our use of ‘Blue Hubbard’ trap flats well distributed on farms
with a history of cucurbit plantings was successful in attracting striped cucumber beetle and
squash bug in KY and PA. The lack of trap flat success in Iowa probably stems from the fact that
there is less cucurbit pest pressure on that farm and they experienced cold winters before the start
of the study.
During the season, the beetle was quite active on the phenology plots. Using the degree
day data we have and the information on development time, we can estimate that there is one
field generation of striped cucumber beetle in PA. We are assuming that the initial biofix beetles
have already completed their preoviposition period, and that overwintered females are capable of
ovipositing by the time we observe beetles on commercial plantings. Supporting this assumption,
striped cucumber beetles that were dissected over the winter months were found to have
undeveloped ovaries, but the percent of females with developing ovaries was high in April in
South Carolina (Elsey 1988).
We could not observe a clean delineation between the field generations or the
overwintered adults. This generational overlap makes it a challenge to predict a gap between
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generations as a safe time to specify management options that require the absence of adults, such
as removing row covers for pollination. Adult females are also long-lived, with the potential to
survive over 100 days post-emergence and continue laying eggs during that time (Ellers-Kirk and
Fleischer 2006). For management purposes, it may be more accurate to think of the striped
cucumber beetle population as a single cohort, comprised of overwintering and field generated
adults.
Squash bug appears to have one field generation per year in PA, KY and IA. KY is the
state that comes the closest to having the degree days necessary for a second field generation, but
that potential is limited by the critical photoperiod that induces diapause. The critical photoperiod
of 14.5: 9.5h to 14:10h (L: D) occurs from July 26/27 to August 9/10 for Rock Springs PA, July
17 to August 4 for Lexington KY, and July 29/30 to August 11/12 for Gilbert IA (visualized by
vertical lines in Figures 5-7, 5-8, and 5-9). Squash bugs that hatch after those dates in each
location will not lay eggs and will diapause if they make it to adulthood (Nechols 1988). This pest
also had a period of extended emergence and activity with no clear peaks between generations,
but there was a large spike in nymph numbers in July that would be particularly injurious to
crops.
Both the bug and the beetle vector plant diseases, thus making any effort to control them
dually important for the control of the diseases. Some of the previous papers on both pests
suggest clean cultivation as a way to reduce numbers, and this may not mesh with all organic
farming practices. A move away from black plastic mulch would be good thing for growers
concerned about squash bug because of how the bugs use the plastic as harborages, as it can
increase their survival (Cartwright et al. 1990). Planting a trap crop of a more attractive variety
may lure the insects away from the main field, but the trap crop must then be destroyed to remove
the pest population.
93
Squash vine borer had the lowest densities of this pest assemblage, but our results give
more information into its life history in geographic areas not well-represented in the current
literature. This pest also has an extended emergence and adults were captured in the pheromone
traps from June through September in South Carolina (Jackson et al 2005). In our study we
stopped monitoring the traps after harvest, and had low per night catches in IA and PA.
Especially at our site in PA, the population of squash vine borer might be so low that it is below
the detection threshold of our trapping method.This work shows some consistency in time of first
capture using degree days (1080.2 +/- 151.6) and calendar days (169.5 +/- 5.5), however our first
capture was over 300 degree days later than that reported in Canhilal et al (2006). Our work also
took the developmental work in Canhilal et al. 2006 and converted it from days to degree days
(base 50 F), which will help to standardize future efforts.
Assaying the population of striped cucumber beetle to identify beetles that are in
diapause is an important area for future work. The Gompertz curve was an effective model for
representing striped cucumber beetle recruitment to trap flats and population increases after a
biofix.
Phenology models are complicated by the fact that they require the estimation or
discovery of what environmental cues the insects are using in their development and the impact
of each cue. Other factors of biological importance that could influence phenology include, but
are not limited to, photoperiod, soil temperature, and soil moisture, all of which could be
investigated in future work.
94
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Jackson DM, Canhilal R, Carner GR (2005) Trap monitoring squash vine borers in Cucurbits.
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Figures and Tables
Table 5-1: Early season recruitment monitoring dates and locations for 2010 - 2012 in KY, IA,
and PA.
State Location GPS coordinates
Year Start Date End Date In-season phenology planting
PA Pennsylvania Furnace
40.714 2010 May 3 June 18 June 1
-77.948 2011 May 2 June 14 June 8
2012 April 13 June 11 June 4
KY Lexington 37.974 2010 April 26 June 4 --
-84.535 2011 April 3 May 25 --
IA Gilbert 42.108 2010 May 3 June 11 May 25
-93.589 2011 April 20 June 10 May 17
Table 5-2: Summary of life history parameters for Acalymma vittatum, Anasa tristis and Melittia
cucurbitae from the literature (see footnotes) and this study (*).
Acalymma vittatum Anasa tristis Melittia cucurbitae
Spring life stage of interest
Adult Adult Late stage pupae
Degree-day base 55 F 60 F 50 F
Degree days to first detection
132.9 +/- 17.3* 141.7 +/- 9.0* 745.2 DD 1
Egg – Adult Development Time (Fahrenheit DD)
793.6 DD 2 725.1 DD 3 1687.5 DD 1
Preoviposition Period
204.8 DD 2 140 – 200 DD 4 --
1Canhilal et al 2006
2 Ellers-Kirk and Fleischer 2006
3Fargo and Bonjour 1988
4Nechols 1987
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Table 5-3. Mean calendar day and cumulative degree day of first capture for Acalymma vittatum
(SCB) and Anasa tristis (SB) on trap flats. N is the number of years.
Pest State n Calendar Day Cumulative Degree Day*
SCB
PA 3 137.3 +/- 10.7 187.7 +/- 59.2
KY 3 112.3 +/- 15.5 216.3 +/- 33.6
IA 2 133.5 +/- 24.8 292.8 +/- 69.4
All 8 127.0 +/- 18.4 224.7 +/- 62.9
SB
PA 3 146.3 +/- 4.0 125.7 +/- 59.1
KY 3 134.0 +/- 7.9 241.3 +/- 87.6
IA 2 133.5 +/- 24.8 154.4 +/- 54.5
All 8 138.5 +/- 12.3 176.2 +/- 81.6
* Cumulative Degree Day base 55 F for SCB, and base 60 F for SB
Table 5-4. Mean calendar day and cumulative degree day of first capture for Acalymma vittatum
(SCB) and Anasa tristis (SB) in the phenology plot. N is the number of years.
Pest State n Calendar Day Cumulative Degree
Day* Days after
transplanting Degree Days after
transplanting
SCB
PA 2 167.0 +/- 5.7 523.45 +/- 41.8 9.5 +/- 3.5 555.6 +/- 218.6
KY 2 150.5 +/- 6.4 542.6 +/- 156.1 -- --
IA 2 181.0 +/- 17.0 973.3 +/- 262.1 40.0 +/- 22.6 264.0 +/- 109.0
All 6 166.2 +/- 16.1 679.8 +/- 265.9 24.8 +/- 22.0 409.8 +/- 219.6
SB
PA 1 174 353.7 18.0 +/- 4.2 317.3 +/- 135.4
KY 2 157.5 +/- 16.3 428.1 +/- 275.6 -- --
IA 2 176.0 +/- 1.4 564.3 +/- 91.6 35.0 +/- 4.2 133.1 +/- 42.1
All 5 168.2 +/- 12.8 467.7 +/- 172.6 26.5 +/- 10.4 225.2 +/- 134.2
* Cumulative Degree Day base 55 F for SCB, and base 60 F for SB
Table 5-5. Mean calendar day and cumulative degree day base 50 F of first capture for Melittia
cucurbitae
State n Calendar Day Cumulative Degree Day
PA 2 175.0 +/- 4.2 975.3 +/- 41.8
KY 2 165.0 +/- 5.7 1213.9 +/- 191.1
IA 2 168.5 +/- 0.7 1051.4 +/- 131.0
All 6 169.5 +/- 5.5 1080.2 +/- 151.6
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Table 5-6. Parameter estimates for the Gompertz equations modeling early season recruitment of
Acalymma vittatum to trap flats using degree day.
x-variable Scenario Parameter Estimate StdError LowerCL UpperCL
Degree Day
PA and KY 2010 - 12
θ1 0.9913 0.0481 0.9065 1.0938
θ2 3.3906 0.4486 2.6487 4.3904
θ3 0.0140 0.0020 0.0107 0.0185
PA and KY 2010 – 12, θ1
forced to 1
θ1 1 0
θ2 3.3415 0.3324 2.7491 4.0802
θ3 0.0138 0.0013 0.0115 0.0167
Table 5-7. Parameter estimates for the Gompertz equations modeling early season recruitment of
Acalymma vittatum to trap flats using calendar day.
x-variable Scenario Parameter Estimate StdError LowerCL UpperCL
Calendar Day
KY 2010 -12 θ1 1.3350 0.2824 0.9774 3.1930
θ2 7.9811 2.1841 4.0417 14.3457
θ3 0.0624 0.0189 0.0266 0.1155
PA 2010 -12 θ1 0.9870 0.0784 0.8541 1.2515
θ2 24.7061 6.8221 12.5093 54.3958
θ3 0.1732 0.0481 0.0868 0.3799
Figure 5-1. Gompertz curve showing early season recruitment predictions for PA and KY 2010 – 11 using cumulative degree days base 55 F
for Acalymma vittatum.
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Figure 5-2. Gompertz curve showing early season recruitment predictions for PA 2010 - 2012 using calendar day for Acalymma vittatum.
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Figure 5-3. Gompertz curve showing early season recruitment predictions for KY 2010 - 2012 using calendar day for Acalymma vittatum.
Figure 5-4. Average number of A. vittatum per plant during the growing season for PA 2011 with
overlay showing the biofix and projected development times for the first and second field
generations
biofix 1st field
generation emerges
2nd field generation
emerges oviposition
6/23 7/17 8/4 9/4
Figure 5-5. Average number of A. vittatum per plant during the growing season for Kentucky
with overlay showing the biofix and projected development
biofix 1st field
generation emerges
2nd field generation
emerges oviposition
biofix 1st field
generation emerges
2nd field generation
emerges oviposition
6/1 6/21 7/8 7/25
5/24 6/14 7/6 7/24 8/9
8/31
8/11
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Figure 5-6. Average number of A. vittatum per plant during the growing season for Iowa with
overlay showing the biofix and projected development.
biofix 1st field
generation emerges
2nd field generation
emerges oviposition
biofix 1st field
generation emerges
2nd field generation
emerges oviposition
6/8 6/28 7/16 8/2 8/19
6/2 6/19 7/16 7/19 7/31
Figure 5-7. Average number of squash bug adults, juveniles and egg masses per plant during the
growing season for Pennsylvania. The horizontal bar shows the biofix and projected egg to adult
development time, and the solid vertical lines indicate the critical photperiod for diapause
induction.
egg biofix
1st field generation
emerges
7/1 7/15 7/25 8/10 9/8
Figure 5-8. Average number of squash bug adults, juveniles and egg masses per plant during the
growing season for Kentucky. The horizontal bar shows the biofix and projected egg to adult
development time, and the two vertical lines indicate the timing of the critical photperiod for
diapause induction.
1st field generation
emerges
egg biofix
egg biofix
1st field generation
emerges 5/29
6/17 7/5 7/21 8/6
5/31
6/22 7/12 7/28 8/15
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Figure 5-9. Average number of A. tristis adults, juveniles and egg masses per plant during the
growing season for Iowa. The horizontal bar shows the biofix and projected egg to adult
development time, and the two vertical lines indicate the timing of the critical photperiod for
diapause induction.
adult biofix
1st field generation
emerges
1st field generation
emerges egg biofix 7/1
7/21 9/4 8/8
6/25
7/18 8/31 8/8
Figure 5-10. Observed and predicted accumulation of A. vittatum in Pennsylvania during the
growing season. Predicted lines were generated using the equation y = 0.9913*exp(-exp(3.3906 –
0.014*x)) where x is cumulative degree days base 55 F. The division between first and second
field generation was estimated as 793.6 cumulative degree days base 55 F after the biofix.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
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po
rtio
n S
CB
cumulative degree days base 55 F
2011
1st gen observed
2nd gen observed
1st gen predicted
2nd gen predicted
6/23
7/30
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Figure 5-11. Observed and predicted accumulation of A. vittatum in Iowa during the growing
season. Predicted lines were generated using the equation y = 0.9913*exp(-exp(3.3906 –
0.014*x)) where x is cumulative degree days base 55 F. The division between first and second
field generation was estimated as 793.6 dd55 after the biofix.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 250 500 750 1000 1250 1500 1750 2000 2250 2500
pro
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rtio
n S
CB
2010
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2nd gen observed
1st gen predicted
2nd gen predicted
0.0
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0 250 500 750 1000 1250 1500 1750 2000 2250 2500
pro
po
rtio
n S
CB
2011 1st gen observed 1st gen predicted
6/16 7/27
7/16
110
Figure 5-12. Observed and predicted accumulation of A. vittatum in Kentucky during the growing
season. Predicted lines were generated using the equation y = 0.9913*exp(-exp(3.3906 –
0.014*x)) where x is cumulative degree days base 55 F. The division between first and second
field generation was estimated as 793.6 dd55 after the biofix.
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 500 1000 1500 2000 2500
pro
po
rtio
n S
CB
2010
1st gen observed
2nd gen observed
1st gen predicted
2nd gen predicted
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 500 1000 1500 2000 2500
pro
po
rtio
n S
CB
2011
1st gen observed
2nd gen observed
1st gen predicted
2nd gen predicted
6/10 7/13
5/30 7/9
Chapter 6
Conclusions
The diverse agroecosystem in Pennsylvania presents a challenge to growers to manage
the endemic and introduced pests and their associated diseases. Looking at this problem over a
large scale, both temporally and spatially, invites the use of new techniques to investigate
population movement and phenology.
As identified in Chapter 2, the aphid community in Pennsylvania is diverse even when
sampled in an agricultural field. In addition to describing the alate aphid community in PA snap
beans, I further investigated the overall biodiversity of aphids in PA by generating a
comprehensive review of the aphid species present using the results of the pan trapping study,
Wallis et al (2005), and Pepper (1965).
Sampling only in one habitat, snap bean fields, yielded a surprisingly high percentage of
the over 400 species present throughout PA and NY (~14% and ~18% respectively). Although
our sampling method was concentrated on one habitat, we did intercept aphids moving from the
surrounding forests and hedgerows. The high degree of landscape heterogeneity and crop
diversity in the trapping areas includes plants that serve as hosts for many of the species that
represented less than 1% of the total capture (Pfleeger et al. 2006). These aphids were captured in
very small numbers (mostly singletons), and are not important contributers to the plant virus
epidemics reported by Nault et al (2009).
Of the aphids we captured, two species were especially notable; T. trifolii which
comprised 31.8% of the identified aphids, and A. glycines which represented 18.2 % of the
identified aphids. Both of these aphids were introduced to North America (A. glycines from Asia
and T. trifolii from Europe) and were quite destructive to crops immediately after their
112
introduction (soybean and alfalfa, respectively). While not known to colonize Phaseoulus spp.,
both species were determined to be competent vectors of the legume strain of CMV (Gildow et al
2008).
The Pepper (1965) aphid list in addition to the Pepper slide collection allowed us to
compile a comprehensive list of the aphids present in PA, but the nomenclature was in need of
updating. Our efforts to update the nomenclature, and incorporate our more recent sampling
efforts resulted in a modern list of aphids of PA that includes recently introduced species.
The intermittent appearance of CMV in central Pennsylvania snap bean crops could be a
result of our unique agricultural landscape. Our agricultural fields are located in valleys bordered
by the low, but steep, forested ridges of the Appalachian Mountains. Our ridge and valley system
might be acting like a filter, keeping CMV out for most of the season. We did not search for a
CMV reservoir outside of testing a few alfalfa fields, which were also negative for CMV. It is
possible, that much like our A. glycines population, legume strains of CMV are also a migrant
species. If this is the case, migrating aphids may be scrubbed of virions when they land in one of
our many bordering forests containing many non-host plants.
The species assemblage in an area is always changing with the introduction of new
species. The U.S. saw the introduction of soybean aphid in 2000, and it was eventually found in
Pennsylvania. It is relevant because of its ability to transmit plant viruses as well as causing
feeding damage on soybean in high numbers. Because of the absences of large quantities of the
primary host in PA, I hypothesized that the soybean aphid population found in PA during the
growing season is largely influenced by migrants. I used genetic tools and air-flow trajectory
models to investigate the natal sources of A. glycines in Pennsylvania.
Using microsatellite and SNP data, we demonstrated that the genotypic diversity of
soybean aphids in Pennsylvania was very high (0.81 to 1.00). In 2009, diversity decreased over
time, but in 2010 the opposite occurred, therefore we could not define a consistent temporal trend
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in genotypic diversity. Genotypic diversity in a field is influenced by how many different clones
initially colonize a field, how successful those clones are, and how many clones colonize the field
as the season progresses and their success. Our results showing high initial diversity indicate that
we have many clones colonizing our fields, and a few of them are present as the season
progresses. As the season progresses, some aphid clones are maintained and others are new
migrants or die off or are not resampled.
On a spatial scale, it appears that we can use molecular markers to detect long distance
movements. As we increased our markers from 6 microsatellites to 17 SNPs, it became harder to
find shared clones over long distances. The aphids we collected in PA matched with NY and
Ottawa very few times, but the data suggest that with comparisons to more populations we could
successfully estimate the natal source of soybean aphid populations in PA.
After including a dataset containing Midwest collections from 2009 and both
microsatellite and SNP information, we could form a more comprehensive picture of aphid
movement to PA. The Midwest data analyzed here was first used in Orantes et al (2012). In that
paper, the authors observed lower levels of genotypic diversity in the early season collections
(0.68 – 0.97), and higher levels in the late season collections (0.87 – 1.00). The genotypic
diversity from the collections in PA, NY, Canada (2010) and VA (2009) more closely resembled
that of the late season Midwest collections (0.86 – 0.97 in 2009, 0.81 – 1.00 in 2010). This high
genetic diversity and the lack of genetic differentiation between populations sampled in PA
suggest high levels of aphid movement, and the lack of a solid local population colonizing from
surrounding buckthorn in the spring. Early season aphid density in soybean fields was found to be
best predicted by the amount of buckthorn in the surrounding landscape in close proximity to the
fields (Bahlai et al 2010). The low early-season colonization densities we observed combined
with the high genotypic diversity would be consistent with relatively rare colonization events
expected from long distance migration.
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There are multiple avenues for future work necessary to successfully integrate molecular
identification techniques and aerobiological modeling into a useful management and risk
assessment tool. One would be the need to have a concerted effort to accurately represent the
range of buckthorn, the primary host. Since buckthorn is the limiting factor in where populations
can overwinter, understanding its range is key to identifying local sources of the aphid. There are
many assumptions inherent to modeling and predicting long distance aphid movement including
the assumption that aphids actually getting into the air column (demonstrated with the suction
trap network, Schmidt et al 2012) and then being deposited at some point along the way.
Programs like HYSPLIT give a good visualization of where air parcels are going and with further
investigation could be a useful forecasting tool.
The continued development of molecular techniques to identify aphid populations will be
of use with the emergence of soybean with aphid resistance traits and the subsequent aphid
biotypes with resistance characters of their own. Also, if soybean aphid ever branches out to use
any of the other Rhamnus species present in the landscape, these tools could be used to identify
biotypes or subspecies. This merging of molecular techniques and aerobiology is not limited to
this system, and could be expanded to other economically important pests.
In Chapter 4, I used general insect primers to ask questions about the possible origin of T.
trifolii in the United States. The results represent a summary of the literature and a preliminary
attempt to use genetic tools to elucidate the relationships between T. trifolii in the Old and New
World. Our samples were from a limited geographic range, and did not indicate clear population-
level differences.
In Chapter 5 we set out to monitor the early season activity and growing season
phenology of three pests on land that is transitioning to organic production on research farms in
Pennsylvania, Iowa and Kentucky on two commercially important cucurbit crops (muskmelon –
Cucumis melo and butternut squash – Cucurbita moschata) to better inform organic management
115
practices. We also set out to create accurate phenology models of these pests using air-
temperature degree-days from weather stations and development times from the existing
literature.
There have been several previous attempts at predicting the timing of and identifying the
cues that initiate striped cucumber beetle activity in the spring before commercially cultivated
cucurbits are present (Elsey 1988, Lewis et al. 1990, Pair 1997, Radin and Drummond 1994).
This study focused on advancing our ability to estimate early season activity using air
temperature degree days. When cumulative degree days base 55 F was used as the predictor, the
first inflection point of a curve modeling recruitement to trap seedlings was approximately 150
degree days, and ranged from 140 to160. Looking at the 30-year climatology and assuming that
beetles begin their activity between 100 and 200 cumulative degree days, we can estimate that as
a range of approximately 11 days for PA and 10 days for IA and KY occurring in mid to late
May.
The pattern of recruitment of striped cucumber beetle on the trap flats which occurred
prior to commercial transplanting on farms was consistent with the findings from Smyth and
Hoffman (2003) who measured recruitment during the summer field season. They used male and
female beetles and plants with high or low levels of cucurbitacin in different combinations to
determine ecological mechanisms of attraction. They found that the beetles oriented upwind,
especially on traps containing pioneer males (Smyth and Hoffman 2003). In our study, we had
low initial counts on the trap flats, but once beetles were found on a flat, subsequent samplings
had much higher counts (some flats had hundreds, especially in Kentucky). The pattern of the
Gompertz equation represents this aggregative activity well, by increasing more rapidly than a
simple logistic curve (Winsor 1932).
Predicting early season activity of an insect that overwinters as an adult is challenging
without knowing the cues that are inducing the end of diapause and factors that affect spring
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activity. We are using a single variable to attempt to model the start of spring activity for these
pests, but what we are really modeling is the recruitment to a trap flat. There are other variables
(soil temp, day length, appearance of other food sources etc) that could give more precise results
once the mechanism for exiting diapause and initiating spring activity is known.
This study also reinforced the trap flat methods used in other studies (Lewis et al. 1990
and Radin and Drummond 1994). Our use of Blue Hubbard trap flats well distributed on farms
with a history of cucurbit plantings was successful in attracting striped cucumber beetle and
squash bug in KY and PA. The lack of trap flat success in Iowa probably stems from the fact that
there is less cucurbit pest pressure on that farm and they experienced cold winters before the start
of the study.
During the season, the beetle was quite active on the phenology plots. Using the degree
day data we have and the information on development time, we can estimate that there is one
field generation of striped cucumber beetle in PA. We are assuming that the initial biofix beetles
have already completed their preoviposition period, and that overwintered females are capable of
ovipositing by the time we observe beetles on commercial plantings. Supporting this assumption,
striped cucumber beetles that were dissected over the winter months were found to have
undeveloped ovaries, but the percent of females with developing ovaries was high in April in
South Carolina (Elsey 1988).
We could not observe a clean delineation between the field generations or the
overwintered adults. This generational overlap makes it a challenge to predict a gap between
generations as a safe time to specify management options that require the absence of adults, such
as removing row covers for pollination. Adult females are also long-lived, with the potential to
survive over 100 days post-emergence and continue laying eggs during that time (Ellers-Kirk and
Fleischer 2006). For management purposes, it may be more accurate to think of the striped
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cucumber beetle population as a single cohort, comprised of overwintering and field generated
adults.
Squash bug appears to have one field generation per year in PA, KY and IA. KY is the
state that comes the closest to having the degree days necessary for a second field generation, but
that potential is limited by the critical photoperiod that induces diapause. The critical photoperiod
of 14.5: 9.5 to 14:10 (L: D) occurs from July 26/27 to August 9/10 for Rock Springs PA, July 17
to August 4 for Lexington KY, and July 29/30 to August 11/12 for Gilbert IA (visualized by
vertical lines in Figures 5-7, 5-8, and 5-9). Squash bugs that hatch after those dates in each
location will not lay eggs and will diapause if they make it to adulthood (Nechols 1988). This pest
also had a period of extended emergence and activity with no clear peaks between generations,
but there was a large spike in nymph numbers in July that would be particularly injurious to
crops.
Both the bug and the beetle vector plant diseases, thus making any effort to control them
dually important for the control of the diseases. Some of the previous papers on both pests
suggest clean cultivation as a way to reduce numbers, and this may not mesh with all organic
farming practices. A move away from black plastic mulch, which is used to raise soil temperature
and help control weeds, would be beneficial for growers concerned about squash bug because of
how the bugs use the plastic as harborages, increasing their survival (Cartwright et al. 1990).
Planting a trap crop of a more attractive variety may lure the insects away from the main field,
but the trap crop must then be destroyed to remove the pest population.
Squash vine borer had the lowest densities of this pest assemblage, but our results give
more information into its life history in geographic areas not well-represented in the current
literature. This pest also has an extended emergence and adults were captured in the pheromone
traps from June through September in South Carolina (Jackson et al 2005). In our study we
stopped monitoring the traps after harvest, and had low per night catches in IA and PA.
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Especially at our site in PA, the population of squash vine borer might be so low that it is below
the detection threshold of our trapping method.This work shows some consistency in time of first
capture using degree days (1080.2 +/- 151.6) and calendar days (169.5 +/- 5.5). Our work also
took the developmental work in Canhilal et al. 2006 and converted it from days to degree days
(base 50 F), which will help to standardize future work.
Assaying the population of striped cucumber beetle to identify beetles that are in
diapause is an important area for future work. The Gompertz curve was an effective model for
representing striped cucumber beetle recruitment to trap flats and population increases after a
biofix.
Phenology models are complicated by the fact that they require the estimation or
discovery of what environmental cues the insects are using in their development and the impact
of each cue. Other factors of biological importance that could influence phenology include, but
are not limited to, photoperiod, soil temperature, and soil moisture, all of which could be
investigated in future work.
These chapters sought to use population structure and phenology to expand the
knowledge of a handful of pest species, both endemic and migratory in nature that impact
vegetable crop production in Pennsylvania. The alate aphid community in Pennsylvania snap
bean fields is diverse and contains members that are efficient vectors of economically important
plant viruses. One of these aphids, A. glycines, is largely present in the state as a result of
migration from areas with high densities of its overwintering host, R. cathartica. A. glycines
populations found in Pennsylvania had high levels of genotypic diversity, which was indicative of
being sourced from many natal populations, and they were genetically similar to some
populations in the Midwest. Matching A. glycines clones were found between PA, NY and VA
indicating some level of long distance movement, which we attempted to model using air-flow
trajectories. The work with T. trifolii was not conclusive, but it provides the basis for an avenue
119
of future research into the population structure of a recently introduced aphid species that could
help to answer additional questions about A. glycines. Modeling the phenology of the striped
cucumber beetle and squash bug was challenging due to the fact that they overwinter as adults.
We were able to demonstrate a successful early season activity monitoring tool, and use
development data from previous studies on both insects to estimate the number of field
generations and discuss challenges for their control in three states. We also described the
phenology of the squash vine borer in geographic areas not represented in previous studies. These
results can be integrated into management strategies on both conventional and organic cucurbit-
growing farms.
References
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(Coleoptera: Chrysomelidae), a vector of Erwinia tracheiphila in Cucurbits. Environmental
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Nault BA, Shah DA, Straight KE, et al. (2009) Modeling temporal trends in aphid vector
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Appendix
HYSPLIT Screenshots
On the next screen, we entered the starting location (one of our fields where we found an
aphid with a genotype that matched to another location) in decimal degrees.
We used the EDAS 40km data and selected the archived data we needed (i.e. jul09.01 is
the first 15 days of July 2009).
122
After the archived weather data is selected, we set up the model paraments.We changed
the following from the default settings: total run time was 48 hours, starting height was 100 m
AGL (above ground level), yes to ‘plot color trajectories,’ and label interval of 24 hours. Once
these parameters are set, click ‘request trajectory.’
123
124
Once the computations are complete, click the link for the GIF image and save it. After saving the
GIF, return to the results screen and click ‘modify this scenario and rerun the model.’ This returns
to the model parameters screen and we can select the next day and rerun the model.
We continued doing this for all of the dates in the month before the last matching aphid
was collected. When we reached the 15th of the month, we went back to the beginning and
selected the data file for the last half of the month (e.g. jul09.02 is from July 16 to 31 2009).
VITA Amanda C. Bachmann
Education Ph.D. 2012, Entomology, Pennsylvania State University B.S. 2006, Biology, Case Western Reserve University Publications Albro, S.L., S.M. Petersen, A.C. Bachmann, and P.B. Drewa. 2008. Effects of fragmentation on
juvenile morphology of Acer saccharum Marsh. (sugar maple) in temperate forests of northeastern Ohio, USA. Forest Ecology and Management. Vol. 254: 233-238.
Nault, B.A., D.A. Shah, K.E. Straight, A.C. Bachmann, W.M. Sackett, H.R. Dillard, S.J. Fleischer,
and F.E. Gildow. 2009. Modeling temporal trends in aphid vector dispersal and Cucumber mosaic virus epidemics in snap bean. Environmental Entomology. Vol. 38: 1347-1359.
Bachmann, A., S.J. Fleischer, and W.S. Smiles. Evaluation of foliar insecticides for the control of
Lepidopterans, 2009. 2010. Arthropod Management Tests. Vol 35. Teaching and Extension Invited Speaker: Penn State Extension Master Gardeners of Centre County Monthly Meeting. March 21,
2011. Quebec Horticulture Days. Dec. 3, 2009. Saint Remi, Quebec. Ohio Agricultural Research and Development Center/Ohio State University, Department of
Entomology seminar. Sept. 29, 2009. Wooster, OH. Potter County Crops Day. March 11, 2009. Ulysses, PA. Extension Education meeting for processing snap beans for Hanover Foods. Feb. 23, 2007.
Centre Hall, PA. Teaching Assistant: ENT 497A Evolution of Insects – Fall 2011 ENT 202 Insect Connections – Spring 2008, Fall 2008 Instructor: ENT 316 Field Crop Entomology – Spring 2009 Assistant coach, State College Area High School Science Olympiad team. 2006 – 2012. Grants and Awards Penn State College of Agricultural Sciences Graduate Student Grant. April 1 – June 30 2010. Evans Family Award for Graduate Student Extension Achievement. Penn State College of
Agricultural Sciences. Spring 2010. Lloyd E. Adams Memorial Grant-in-Aid. Penn State College of Agricultural Sciences. Fall 2009.