THE ROLE OF ALGAE IN THE INVASION ECOLOGY OF THE
MOSQUITO SPECIES AEDES JAPONICUS JAPONICUS (DIPTERA: CULICIDAE)
By
Amanda Rae Lorenz

A THESIS
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
MASTER OF SCIENCE
Entomology
2012

ABSTRACT
THE ROLE OF ALGAE IN THE INVASION ECOLOGY OF THE MOSQUITO SPECIES AEDES JAPONICUS
JAPONICUS (DIPTERA: CULICIDAE)
By
Amanda Rae Lorenz
Aedes (Finlaya) japonicus japonicus (Theobald) is invasive in the U.S., Canada, and
western and central Europe. This species breeds in water-filled container habitats where it
overlaps with local species including Aedes triseriatus and Culex pipiens. Because larvae of A. j.
japonicus often occur in sunlit containers with visible algal growth, we hypothesized that gravid
A. j. japonicus females would be more likely to deposit eggs in containers with algae, and that
larval production and competitive ability would be enhanced in these containers. Results of our
studies indicate that algae in larval habitats are not a major factor in oviposition choices of
adult A. j. japonicus females except when algal production is high enough to substantially alter
overall organic matter content cues. Additionally, our results indicate that algae do provide an
overall benefit to mosquito larvae by enhancing development and emergence rates, but do not
alter outcomes of resource competition. We observed substantial competitive asymmetry
between A. j. japonicus and C. pipiens, as no C. pipiens individuals survived the experiment
when in competition with A. j. japonicus, but not between A. j. japonicus and A. triseriatus.
Aedes j. japonicus survival rates were not affected by algae, and A. j. japonicus females
emerged faster than the other species regardless of algal density. Evidence from these studies
suggests that this species may be better able to tolerate low-resource conditions than its
competitors, and that habitat generalism may contribute to the success of A. j. japonicus as an
invader.

ACKNOWLEDGEMENTS
I would first like to offer sincere thanks to my advisor, Dr. Mike Kaufman, for the
constant support, humor, and guidance he has provided me during the course of my graduate
program. I truly could not have asked for a better mentor. I would also like to thank the
members of my guidance committee: Dr. Ned Walker, Dr. Richard Merritt, and Dr. R. Jan
Stevenson, for their mentorship and also for their generosity in allowing me to use their lab
materials. I have derived much benefit over the years from the advice of other graduate
students, both in professional and personal matters, in particular Julianne Heinlein, Stephanie
Miller, Emily Campbell, and Rachel Olson. I have also enjoyed working with and getting to know
my lab mates and appreciate the help and support I have received from them: Rebecca
Morningstar, Matthew Lundquist, Jen Sidge, Danielle Donovan, and Rob McCann. I could not
have completed my research without the assistance of many lab workers and friends, including
Brian Lovett, Craig Bateman, Blane Doyon, Lora Anderson, Marissa Cann, Angeline Kosnik, Liu
Yang, Sarah Willson Malakauskas, Geoff Grzesiak, Shicheng Chen, and Betsy Brouhard. I would
particularly like to thank my sister, Catherine Lorenz, for being cheerfully available to help me
with research tasks when I needed her. One of the most enjoyable and challenging aspects of
my graduate career has been my time spent teaching, and for that opportunity I would like to
offer sincere thanks to Drs. Gabe Ording and Rich Merritt, both for their mentorship and for
their example as passionate and talented instructors. I would also like to thank my fellow TA’s
for their encouragement and inspiring example: Rachel Olson, Sarah Smith, Danielle Donovan,
Emily Campbell, Lesley Schumacher-Lott, Sarah Willson-Malakauskas, Liu Yang, Emily Pastula,
Nick Barc, and Julie Adams.
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Finally, I would not have been able to accomplish anything without the love and support
of my family. Their unfailing encouragement and confidence in me has made me who I am
today.

iv

TABLE OF CONTENTS
LIST OF TABLES.…………………………………………………………………………………………………………page vii
LIST OF FIGURES………………………………………………………………………………………………………..page viii
CHAPTER 1
INTRODUCTION
Aedes japonicus japonicus Invasion Biology and Larval Ecology………………………page 2
Larval Mosquito Feeding Ecology…………………………………………………………………….page 5
Algae as Food for Larval Mosquitoes……………………………………………………………….page 7
Research Objectives and Rationale………………………………………………………………….page 9
Literature Cited……………………………………………………………………………………………….page 11
CHAPTER 2
EFFECTS OF ALGAL DENSITY ON OVIPOSITION HABITS OF AEDES J. JAPONICUS
Abstract…………………………………………………………………………………………………………..page 18
Introduction…………………………………………………………………………………………………….page 18
Methods………………………………………………………………………………………………………….page 22
Results…………………………………………………………………………………………………………….page 26
Discussion……………………………………………………………………………………………………….page 32
Literature Cited……………………………………………………………………………………………….page 38
CHAPTER 3
DO ALGAE AFFECT LARVAL COMPETITION BETWEEN THE INVASIVE MOSQUITO, AEDES
JAPONICUS JAPONICUS, AND NATIVE SPECIES?
Abstract…………………………………………………………………………………………………………..page 45
Introduction…………………………………………………………………………………………………….page 46
Methods………………………………………………………………………………………………………….page 49
Results…………………………………………………………………………………………………………….page 59
Discussion……………………………………………………………………………………………………….page 83
Literature Cited……………………………………………………………………………………………….page 91
CONCLUSIONS………………….…………………………………………………………………………………………page 97
APPENDIX I
RECORD OF DEPOSITION OF VOUCHER SPECIMENS…………………………………………………….page 101

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APPENDIX II
ALGAE OCCUR IN LARVAL HABITATS OF AEDES J. JAPONICUS……………………………………….page 103
APPENDIX III
PHOTOGRAPHS OF ALGAE FROM PCA FROM CHAPTER 3……………………………………………..page 113

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LIST OF TABLES

Table 2.1. Results from separate mixed-model ANOVA (df = 5,12) tests for effects of light
treatment on total number of eggs, total number of hatched larvae, and post-trial limnetic
chlorophyll a at each of our three field sites…………………………………………………………..page 27
Table 3.1. Primer pairs used in bacterial and fungal qPCR reactions (see Fierer et al. 2005).
……………………………………………………………………………………………………………………………….page 57
Table 3.2. ANOVA results from our mosquito production parameters of survivorship and the
proportion of adults that emerged from each microcosm (df = 1,6,84)…………………..page 59
Table 3.3. Kruskal-Wallis analysis of overall mosquito production parameters in laboratory
microcosms. Degrees of freedom are in parentheses……………………………………………..page 60
Table 3.4. ANOVA table for chlorophyll a in laboratory microcosms (df = 4, 60)…….page 66
Table 3.5. ANOVA results from our mosquito production parameters of survivorship and the
proportion of adults that emerged from each microcosm (df = 1,3,46)……………………page 70
Table 3.6. Kruskal-Wallis test results from remaining non-normal mosquito production
parameters. Degrees of freedom are in parentheses………………………………………………page 71
Table 3.7. ANOVA results from analysis of fungal and bacterial DNA concentrations (df =
1,3,47)……………………………………………………………………………………………………………………..page 79
Table A2.1. Geographical coordinates of survey sites……………………………………………..page 105
Table A2.2. Mean (± 1 SE) limnetic and benthic chlorophyll a for each habitat type sampled.
Containers without an error term had a sample size of 1. Table also includes the mean percent
of individuals from each species collected from each container type. Asterisks (*) indicate that
there were no larvae collected from the container(s)……………………………………………..page 108

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LIST OF FIGURES

Figure 1.1. Lateral view of an A. j. japonicus larva, showing the location of the lateral palatal
brushes………………………………………………………………………………………………………………………...page 6
Figure 2.1. Mean (± 1 SE) limnetic chlorophyll a (µg/L) per container, measured post-trial. Data
are combined from experimental trials 1 and 2 (n = 6)…………………………………………………page 28
Figure 2.2. Mean (± 1 SE) total number of hatched A. j. japonicus individuals per container (A)
and mean (±1 SE) total number of eggs laid per container (B), with experimental trials 1 and 2
combined (n = 6)…………………………………………………………………………………………………………..page 29
Figure 2.3. Scatter plots showing regressions for data from all sites combined (A) as well as for
the Toumey site alone (B). Data are combined from both trial 1 and trial 2…………………page 30
Figure 2.4. Representative temperature profiles of full sun and full shade experimental
container during the study periods. Asterisks (*) represent 12 noon…………………………..page 31
Figure 2.5. Average (± 1 SE) of pre- and post-trial benthic chlorophyll a (for trial 2 only, see
results section) (n = 3)………………………………………………………………………………………………….page 32
Figure 3.1. Summary of mosquito production parameters from our laboratory experiment:
Mean (± 1 SE) (A) proportion of individuals surviving, (B) proportion of emerged adults, (C)
female weight, (D) male weight, (E) female emergence day, and (F) male emergence day.
Columns without error bars represent a sample size of 1. Key to abbreviations: J (alone) = A. j.
japonicus (intraspecific), T (alone) = A. triseriatus (intra.), C (alone) = C. pipiens (intra.), J (with T)
= A. j. japonicus when in competition with C. pipiens, etc…………………………………………….page 61
Figure 3.2. Mean (± 1 SE) total from each treatment. Species from interspecific treatments are
combined. (Legend key: J = A. j. japonicus (intraspecific), T = A. triseriatus (intra.), C = C. pipiens
(intra.), JT = A. j. japonicus + A. triseriatus, JC = A. j. japonicus + C. pipiens)………………….page 65
Figure 3.3. Mean (± 1 SE) chlorophyll a sampled during the middle of our laboratory microcosm
experiment in the water column (A) and on suspended glass slides (B) (n = 7)…………….page 67
Figure 3.4. Bacterial production, measured as the rate of leucine incorporation. J = A. j.
japonicus (intraspecific), T = A. triseriatus (intra.), C = C. pipiens (intra.), JT = A. j. japonicus + A.
triseriatus, JC = A. j. japonicus + C. pipiens…………………………………………………………………….page 68
Figure 3.5. Mean (± 1 SE) total leaf mass lost from each treatment (n = 7). J = A. j. japonicus
(intraspecific), T = A. triseriatus (intra.), C = C. pipiens (intra.), JT = A. j. japonicus + A. triseriatus,
JC = A. j. japonicus + C. pipiens……………………………………………………………………………………..page 69

viii

Figure 3.6. Total (± 1 SE) number of mosquitoes which survived the duration of the experiment,
either as adults who emerged or larvae remaining when microcosms were dismantled in field
microcosms (A), total (± 1 SE) number of adults emerging from each treatment (B) in our field
microcosms, (C) mean weight (± 1 SE) of females and (D) males, mean emergence day (± 1 SE)
of females (E) and males (F). Key to abbreviations: J (alone) = A. j. japonicus (intraspecific), T
(alone) = A. triseriatus (intra.), J (with T) = A. j. japonicus when in competition with A. triseriatus,
etc……………………………………………………………………………………………………………………………….page 72
Figure 3.7. Mean (± 1 SE) adult weights in each treatment in field microcosms. (Legend key: J
= A. j. japonicus (intraspecific), T = A. triseriatus (intra.), and JT = A. j. japonicus + A. triseriatus).
……………………………………………………………………………………………………………………………………page 74
Figure 3.8. Mean chlorophyll a (± 1 SE) measured on leaf surfaces (A), in the water column (B)
and along the sides of each microcosm (C) in field microcosms during the estimated peak of
larval feeding activity (n = 7). J = A. j. japonicus (intraspecific), T = A. triseriatus (intra.), and JT =
A. j. japonicus + A. triseriatus……………………………………………………………………………………….page 76
Figure 3.9. Principle component analyses on leaf-associated algal communities in field
microcosms showing differences in algal communities between different larval treatments.
Values are mean scores for each axis plus one SE……………………………………………………….page 77
Figure 3.10. Principle component analysis of water column algal communities in field
microcosms showing differences in the algal communities encountered between microcosms
with and without larvae. Values are mean scores for each axis plus one SE………………page 78
Figure 3.11. Mean (± 1 SE) concentration of leaf- and water-associated bacterial (A and B,
respectively) and fungal leaf and water (C and D, respectively) DNA in field microcosms (n = 7).
J = A. j. japonicus (intraspecific), T = A. triseriatus (intra.), JT = A. j. japonicus + A. triseriatus, N =
No Larvae……………………………………………………………………………………………………………………page 80
Figure 3.12. Mean total leaf mass lost per microcosm (±1 SE) in field microcosms (n=7). J = A. j.
japonicus (intraspecific), T = A. triseriatus (intra.), JT = A. j. japonicus + A. triseriatus, N = No
Larvae……………………………………………………………………………………………………………………….page 81
Figure 3.13. Water temperature, measured with data loggers, was similar in sunlit (red) and
shaded (blue) microcosms during the field experiment. (For interpretation of the references to
color in this and all other features, the reader is referred to the electronic version of this thesis.)
………………………………………………………………………………………………………………………………….page 82
Figure A2.1. Locations of survey sites are marked with stars……………………………………page 104
Figure A2.2. Algal specimens typical of our sampled containers viewed at 40X magnification.
(A) Filamentous green alga, (B) Chlamydomonadaceae, (C) unidentified coccoid Chlorophyte,
and (D) cyanobacterial filament. Calibration marks measure 10 µm. (For interpretation of the
ix

references to color in this and all other figures, the reader is referred to the electronic version
of this thesis)…………………………………………………………………………………….………………………page 109
Figure A3.1 Unidentified cyanobacterium from leaf-associated algal communities, field
microcosm experiment, see chapter 3. Calibration mark measures 10 µm. (For interpretation
of the references to color in this and all other figures, the reader is referred to the electronic
version of this thesis)……………………………………………………………………………………………….page 114
Figure A3.2. Unidentified chlorophyte colony from leaf-associated algal communities, field
microcosm experiment, see chapter 3. (For interpretation of the references to color in this and
all other figures, the reader is referred to the electronic version of this thesis)………page 114
Figure A3.3. Unidentified oval chlorophyte from water column algal communities, field
microcosm experiment, see chapter 3. (For interpretation of the references to color in this and
all other figures, the reader is referred to the electronic version of this thesis)………page 115

x

CHAPTER ONE
INTRODUCTION

1

Mosquitoes as a group pose threats to human and animal health, but invasive mosquito
species represent novel risks. Invasive mosquitoes are of concern because they may alter
cycles of disease – either by introducing novel pathogens into a system, vectoring an existing
pathogen, or competing with native vectors (Juliano and Lounibos 2005). Container-breeding
mosquito species are especially likely to invade new areas due to their propensity to lay
dessication-resistant eggs in easily-transportable vehicles (such as used tires) and their ability to
utilize artificial containers associated with human habitation (Sanders et al. 2010). Once
established, larvae of invasive mosquito species often overlap with local species in container
habitats, which can lead to interspecific resource competition, among other interactions
(Juliano and Lounibos 2005), which can result in the displacement of native species due to an
invader (Juliano 1998).
The feeding patterns and behaviors of invasive mosquito species may contribute to the
outcomes of resource competition (Yee et al. 2004a, O’Donnell and Armbruster 2007); thus, it is
important to understand the feeding ecology of invasive species in order to better grasp the
repercussions of their establishment on native ecological systems. The following sections
explore the exploitation of microbial food sources by mosquito larvae, with an emphasis on the
invasive species Aedes (Finlaya) japonicus japonicus (Theobald) and the potential for its use of
algae as a food resource.
Aedes japonicus japonicus: Invasion Biology and Larval Ecology
Invasive species have the potential to offset the “balance” of an ecosystem and may
greatly alter the ecology of habitats upon which they intrude (Elton 1958). Human activity,
2

either purposeful or accidental, is most often the cause of modern-day invasions (Leprieur et al.
2008). In the case of mosquitoes, introductions often occur through shipping and trade
(Lounibos 2002). The global shipping of tires and bromeliads, both of which serve as reservoirs
for mosquito eggs, has contributed to the invasion of a number of mosquito species (Lounibos
2002).
Aedes j. japonicus is an invasive species of concern in the United States. In its native
range of Japan, Korea, and southern China, A. j. japonicus is known to be a competent vector of
Japanese encephalitis virus (Takashima and Rosen 1989). In North America, A. j. japonicus has
encountered a novel array of arboviruses that may increase its role as a vector of disease
(Juliano and Lounibos 2005). Laboratory assays with various pathogens have shown that A. j.
japonicus is a competent vector of West Nile virus, St. Louis encephalitis virus, LaCrosse
encephalitis virus, and Eastern Equine encephalitis virus (Sardelis and Turell 2001, Sardelis et al.
2002, Sardelis et al. 2002, Sardelis et al. 2003). This species, while not a voracious humanfeeder, has been observed to take blood meals primarily from mammals in the field (Molaei et
al. 2009) and is also known to feed on birds in laboratory settings (Williges et al. 2008). Thus, A.
j. japonicus is of special concern because it could potentially act as a “bridge vector” between
mammals and birds in the cycle of West Nile virus (Molaei et al. 2009). At this writing, the
impact of A. j. japonicus on disease cycles in its invasive range.
Aedes j. japonicus is invasive in the continental United States, Hawaii, and Canada, and
has recently been discovered in Western and Central Europe (Peyton et al. 1999, Larish and
Savage 2005, Thielman and Hunter 2006, Schaffner et al. 2009, Werner et al. 2012). First

3

identified in the U.S. from light traps in New York and New Jersey in 1998 (Peyton et al. 1999),
Fonseca et al. (2001, 2010) have shown that the actual entrance of A. j. japonicus to the U.S.
was not limited to a single event, but rather occurred in at least two separate introductions.
This species is postulated to have arrived in used tires (Peyton et al. 1999). Since its initial
invasion, A. j. japonicus has been steadily expanding its range west across North America.
Separate introductions have also occurred in Washington State and Hawaii (Roppo et al. 2004;
Larish & Savage 2005).
Aedes j. japonicus generally breeds in water-filled container habitats (Tanaka et al. 1979,
Scott et al. 2001, Scott 2003). In its native range, larvae are most commonly collected from
riverine rock pools, but are also known to occur in bamboo stumps and other artificial and
natural containers (Tanaka et al. 1979). In the U.S., larvae have been collected from a wide
variety of artificial and natural containers spanning a range of sizes, compositions, and contents
(Bevins 2007). Larvae of this species are known to co-occur in container habitats with larvae of
several native and invasive mosquito species, including Aedes atropalpus, Aedes triseriatus,
Aedes albopictus, Culex pipiens, and Culex restuans (Bevins 2007). There has been some
concern that A. j. japonicus larvae may out-compete native species for resources, similarly to A.
albopictus, which could in turn have consequences for disease cycles (Bevins 2008). A recent
survey of A. j. japonicus larvae in container habitats in Connecticut has shown that it may be
responsible for the local displacement of A. triseriatus, and rock pool surveys indicate that A.
atropalpus and C. restuans may also be displaced (Andreadis and Wolfe 2010). However, initial
studies of larval competition between A. j. japonicus and other native species show that it is not
a clearly stronger competitor (Armistead et al. 2008a, Armistead et al. 2008b, Alto 2011,
4

Hardstone and Andreadis 2012). More studies are necessary in order to determine both the
impacts of A. j. japonicus on native ecosystems and disease cycles, and to discover the
mechanisms behind these impacts.
Larval Mosquito Feeding Ecology
All mosquitoes are aquatic during the larval and pupal stages. The pupal stage does not
feed, but the larval stage is an important time for the accumulation of body mass (Schmolz and
Lamprecht 2000). Most mosquito larvae feed on fine particulate organic matter (FPOM)
(composed of microbes and organic detritus) by utilizing thick tufts of setae on their
mouthparts called “mouth brushes” or “lateral palatal brushes” (Merritt et al. 1992) (Fig. 1.1).
These brushes are used to generate water currents and collect particles from the water column
or submerged surfaces (Clements 1992). Thus, larval mosquitoes are generally restricted to
eating particles of certain sizes (up to 50 µm; Merritt et al. 1992) – that are capable of being
caught on their mouth brushes (Bates 1949). Specifically, mosquito larvae typically ingest a
combination of bacteria, organic detritus, algae, and protozoa (Walker et al. 1988).
The majority of larval mosquitoes can be classified as “collector-gatherers” and/or
“collector-filterers” in terms of the feeding mode they employ (Clements 1992, Merritt et al.
1992). Most mosquito larvae exhibit both feeding modes and feed across many habitat zones,
including submerged surfaces, the water column, and the air-water interface (Walker and
Merritt 1991, Yee et al. 2004b). Some mosquito species are also known to exhibit the feeding
modes of “scraping” (removal of securely-attached food from substrata) and “shredding”

5

(chewing small pieces off of larger objects), though these are not as common as collecting, and
usually occur in addition to that strategy (Merritt et al. 1992).

Figure 1.1. Lateral view of an A. j. japonicus larva, showing the location and configuration of
the lateral palatal brushes. Photo courtesy of Craig Bateman.

Though some species or groups of mosquitoes specialize on one or two particular food
groups as larvae, the majority of mosquito species simply consume what is available to them in
their habitat (Howland 1930, Merritt et al. 1992, Garros et al. 2008). Thus, habitat quality has a
great impact on the efficiency of mosquito growth and development through the quality and
quantity of foods that are present. Habitat quality also affects disease cycles, as habitats with
low quality of food resources usually yield stressed adults, which may exhibit increased
vectorial capacity (Grimstad and Walker 1991, Muturi et al. 2011).

6

Habitat quality interacts with the feeding behaviors of larvae to affect outcomes of
larval resource competition (Yee et al. 2004a). In the case of A. j. japonicus, the container
habitats where larvae make their living are generally small, discrete ecosystems relying mainly
on allochthonously-derived carbon sources (i.e. leaf litter) as a basal source of energy (Kitching
2001). Due to the discrete nature and consequently limited quantity of food and spatial
resources in container habitats, competition between organisms for available resources can be
severe. In a study of the foraging behaviors of A. j. japonicus and A. albopictus, O'Donnell and
Armbruster (2007) showed that A. j. japonicus is an extremely active forager both on leaf
surfaces and in the water column. While this did not enhance its competitive ability relative to
A. albopictus, increased foraging activity levels could potentially impact competitive outcomes
between A. j. japonicus and other species such as C. pipiens and A. triseriatus. Additionally,
preferential consumption of different foods, as well as assimilation efficiency, may also
contribute to competitive outcomes (Winters and Yee 2012). It is not currently known whether
A. j. japonicus preferentially feeds on any specific food sources (i.e. bacteria, fungi, algae), or
whether it differs from native species in this regard. Thus, the feeding habits and physiology of
this species merit further study, as they may be a component of its success as an invader.
Algae as Food for Larval Mosquitoes
Algae often compose a portion of the microbial diversity in container habitats (A.R.L.,
unpublished data), and may serve as high-quality supplemental food resources for larvae. Like
all organisms, larval mosquitoes require specific nutrients and compounds for successful
growth. Nucleotides and nucleic acids are important components of the diet, in addition to

7

dietary sterols (available from plant, algal, fungal, or animal matter) and long-chain (20- or 22carbon) polyunsaturated fatty acids (found mainly in animal tissues; Merritt et al. 1992). A diet
poor in or lacking one of these components will result in either the death of the larva or the
formation of a weakened adult (Merritt et al. 1992). Certain algal taxa contain these important
dietary components and may be superior in quality to bacteria, a common larval food source
(Merritt et al. 1992).
Mosquito larvae often feed on algae (Howland 1930, Wallace and Merritt 2004, Garros
et al. 2008, Brouard et al. 2011). Certain algal communities can serve as high-quality food
sources resulting in superior growth and production of mosquito larvae (Bond et al. 2005,
Kaufman et al. 2006). However, some algal taxa may harm larvae through the possession of
indigestible cell walls (Marten 1986) or the production of toxins (Kiviranta et al. 1993), though
in many cases the presence of indigestible algae is alleviated by the co-occurrence of more
digestible forms, and the production of algal toxins in most mosquito habitats is thought to be
rare or in low concentrations (Marten 2007). The impacts of algae in container habitats on
larval mosquito production must depend on the composition of the algal community.
Even if not directly ingested, algal cells may facilitate mosquito production by enhancing
the growth of other microbial food sources in larval habitats. Espeland et al. (2001) observed
increases in productivity, biomass, and biovolume of bacteria in the presence of algae. Algal
cells may positively affect bacterial growth through their well-known tendency to “leak” organic
carbon, and thus may provide bacteria and other microbes with a supplementary carbon source

8

(Bell and Kuparinen 1984). Additionally, algal cells may provide a substrate for bacterial growth
(Cole 1982).
Aedes j. japonicus has been observed to occur in containers with algae (Andreadis et al.
2001, Scott et al. 2001) and in containers in full sun (Joy and Sullivan 2005) that are likely to
have algae growing in them. Algal biomass in these containers may facilitate larval production,
either through direct ingestion or through effects on other microbial food groups. The role of
autotrophs in the ecology of container ecosystems has recently come under study (Brouard et
al. 2011) and deserves further attention.
Research Objectives and Rationale
The primary goal of the following work was to study the effects of a specific habitat
parameter – exposure to sunlight and resulting algal growth – on the successful production of
an invasive mosquito species. Another broad goal of this thesis was to shed light on the
interactions between different aquatic microbial groups (algae, bacteria, and fungi) in container
habitats and the invertebrates that feed on them. The theme of autotrophy in container
habitats has been relatively understudied, and this thesis is the first to our knowledge to
examine the impacts of natural or near-natural algal communities on Aedes mosquito
production. The specific goals for my thesis were to:
1. Investigate whether algae in container habitats acts as a stimulus for oviposition by
gravid A. j. japonicus females (Chapter 2).
2. Determine whether the utilization of algae as a food source in container habitats has the
potential to enhance larval production of A. j. japonicus larvae (Chapter 3).
9

3. Investigate whether the efficiency of A. j. japonicus as a competitor for resources
against larvae of local mosquito species is affected by the presence of algae (Chapter 3).
4. Determine whether algae naturally occur in A. j. japonicus larval habitats, and document
some of the types of algae present therein (Appendix II).

10

LITERATURE CITED

11

Literature Cited
Alto, B. W. 2011. Interspecific larval competition between invasive Aedes japonicus and native
Aedes triseriatus (Diptera: Culicidae) and adult longevity. J. Med. Entomol. 48: 232-242.
Alto, B. W., L. P. Lounibos, C. N. Mores and M. H. Reiskind. 2008. Larval competition alters
susceptibility of adult Aedes mosquitoes to dengue infection. Proc. R. Soc. B. 275: 463-471.
Andreadis, T. G., J. F. Anderson, L. E. Munstermann, R. J. Wolfe and D. A. Florin. 2001.
Discovery, distribution, and abundance of the newly introduced mosquito Ochlerotatus
japonicus (Diptera : Culicidae) in Connecticut, USA. J. Med. Entomol. 38: 774-779.
Andreadis, T. G. and B. J. Wolfe. 2010. Evidence for reduction of native mosquitoes with
increased expansion of invasive Ochlerotatus japonicus japonicus (Diptera: Culicidae) in the
Northeastern United States. J. Med. Entomol. 47: 43-52.
Armistead, J. S., J. R. Arias, N. Nishimura and L. P. Lounibos. 2008a. Interspecific larval
competition between Aedes albopictus and Aedes japonicus (Diptera : Culicidae) in northern
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Armistead, J. S., N. Nishimura, R. L. Escher and L. P. Lounibos. 2008b. Larval competition
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Thielman, A. and F. F. Hunter. 2006. Establishment of Ochlerotatus japonicus (Diptera :
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species of container mosquitoes. J. Vec. Ecol. 29: 315-322.

16

CHAPTER TWO
EFFECTS OF ALGAL DENSITY ON OVIPOSITION HABITS OF AEDES JAPONICUS JAPONICUS

17

Abstract
Aedes (Finlaya) japonicus japonicus (Theobald) (Diptera: Culicidae) is recently invasive in
the United States. From its original points of introduction on the East Coast and Washington
State, this species has rapidly migrated across the country. Throughout its native and expanded
range, A. j. japonicus larvae are commonly observed in many types of natural and artificial
water-filled containers that vary in organic matter content and exposure to sunlight. Larvae are
most often found in containers with decaying leaf material or algae, and we postulated that
algae could be an important food source for larvae and oviposition attractant to adult A. j.
japonicus. We tested this hypothesis by placing plastic containers with varied levels of shading
to manipulate algal density in the field and monitoring oviposition by natural populations of A.
j. japonicus. Over 99% of larvae hatching from eggs laid on the walls of our containers were A.
j. japonicus, indicating that this species is a dominant colonizer of artificial containers in the
study areas. Although full shading treatments effectively reduced algal biomass (significant
reduction in chlorophyll a levels), at only one site did this affect A. j. japonicus oviposition. We
conclude that algae in larval habitats are not a major factor in oviposition choices of adult A. j.
japonicus females except when algal production is high enough to substantially alter overall
organic matter content cues.

Introduction
Aedes (Finlaya) japonicus japonicus (Theobald) (Diptera: Culicidae) is native to Japan,
China, and Korea (Tanaka et al. 1979) and has recently invaded and expanded into parts of the
United States, Canada, and Europe (Peyton et al. 1999, Thielman and Hunter 2006, Schaffner et
18

al. 2009, Versteirt et al. 2009). A. j. japonicus is a species of medical importance because adults
are competent vectors of a suite of encephalitis viruses, including Japanese encephalitis virus
(Takashima and Rosen 1989) St. Louis encephalitis virus (Sardelis et al. 2003), Eastern Equine
encephalitis virus (Sardelis et al. 2002), LaCrosse encephalitis virus (Sardelis et al. 2002), and
West Nile virus (Sardelis and Turell 2001). This species is of special concern because it often
breeds near human dwellings and has been implicated as a potential “bridge vector” of West
Nile virus due to its tendency to blood feed on both mammals and birds (Williges et al. 2008,
Molaei et al. 2009).
Larvae of A. j. japonicus are known to occur in a variety of natural and artificial waterfilled containers spanning a wide range of compositions, sizes, and contents. Such diverse
locations can include rock pools, tree holes, discarded tires, tarps, buckets, and cemetery vases,
among others (Tanaka et al. 1979, Andreadis et al. 2001, Scott et al. 2001, Schaffner et al.
2009). Larval surveys in the United States indicate that A. j. japonicus colonizes containers
spanning gradients of sunlight (Oliver et al. 2003, Joy and Sullivan 2005), urban and rural
settings (Joy and Sullivan 2005), and differing amounts and types of detritus content (Bevins
2007). The degree to which any specific habitat characters affect distribution of larvae is
unclear, however, some authors have made note of A. j. japonicus larvae being particularly
common in containers with algae or decaying leaves (Andreadis et al. 2001, Scott et al. 2001,
Versteirt et al. 2009).
The availability of food resources is an important factor in determining the quality of
larval habitats. Mosquito larvae are known to feed on heterotrophic microorganisms
associated with decaying vegetation, as well as animal detritus, microcrustacea, rotifers, other
19

larvae, and algae (Merritt et al. 1992). Algae are commonly found in the dissected gut contents
of mosquito larvae and are generally thought to be consumed in proportion to their abundance
in the habitat (Howland 1930, Garros et al. 2008). Many algal taxa contain nutritious
compounds that are required by mosquito larvae for growth, such as sterols and long-chain
polyunsaturated fatty acids (Merritt et al. 1992). In addition, most algae are in the correct size
range (1-50 µm) for ingestion by larval mosquitoes, and, as algae grow both in the water
column and on submerged surfaces, are readily accessible through the different feeding modes
employed by larvae (e.g. browsing, filtering, etc.; Merritt et al. 1992). Studies have noted
associations between visible algae and increased mosquito larval density in many aquatic
habitats (Bond et al. 2005, Barrera et al. 2006). We have observed algal blooms to be common
in some containers utilized by A. j. japonicus larvae, thus algae may represent a primary or
supplemental diet for this species.
The majority of studies of A. j. japonicus in its invasive range to date have addressed
larval survival (i.e. presence and densities within surveyed habitats), with few examining
oviposition preferences (Scott 2003). Oviposition is emphasized as being primarily driven by
female preferences based on cues of habitat suitability (Reiskind and Wilson 2004), and occurs
in response to the representation of habitat quality indicated by the physical and chemical
stimuli that characterize a habitat (Bentley and Day 1989). Apart from perceiving the physical
properties of the habitat (e.g. size, shape), female mosquitoes are able to detect compounds
from aquatic habitats that indicate the presence of conspecifics, predatory threats to larvae,
and the availability of larval food resources (Edgerly et al. 1998, Reiskind and Wilson 2004).
Female responses to conspecific larvae and eggs tend to vary seasonally, with females generally
20

avoiding laying eggs in habitats with conspecific larvae early in the season, and preferentially
laying more eggs in habitats with conspecific larvae late in the season (Edgerly et al. 1998).
Females tend to respond negatively to the presence of predators in larval habitats (Silberbush
et al. 2010). Additionally, females tend to lay more eggs in containers with organic matter, a
category that includes algae, than in containers with plain water (Blaustein and Kotler 1993,
Reiskind and Wilson 2004, but see Wong et al. 2011).
Water-filled container habitats are often considered to be mainly heterotrophic systems
with allochthonously-derived plant and animal matter characterizing the resource base (see
Kitching 2001). However, algae are present in many types of containers, comprise a portion of
the available foods for larvae, and presumably could contribute to ovipositional cues (Brouard
et al. 2011). This has been demonstrated for other mosquito species that are known to utilize
algal volatiles as an ovipositional attractant. For example, Torres-Estrada et al. (2007) showed
that Anopheles pseudopunctipennis females could distinguish between real algal filaments and
analogs in the laboratory, and preferred the real algae for oviposition.
In this study we postulated that gravid female A. j. japonicus are attracted to algae when
searching for sites in which to oviposit. We predicted that females would deposit greater
numbers of eggs in habitats with algae when offered a choice between containers with dense
algal growth and containers with low or no algae. To test our hypothesis, we manipulated algal
densities in oviposition containers in the field by altering light levels, and measured the
ovipositional response of natural A. j. japonicus populations in the area.

21

Materials and Methods
Study Sites. The experiment was conducted at three field sites near the campus of
Michigan State University (East Lansing, MI). The first site was located in a grassy field adjacent
to Toumey Woodlot on the MSU’s southern campus. The second site was located on the
grounds of the Lower River Lab, part of the North Central Regional Aquaculture Center at MSU.
The facility contains a large lawn with an experimental pond and small adjoining woodland, and
is situated along the Red Cedar River. Our third field site was at Potter Park Zoo in Lansing, MI.
The Potter Park Zoo is situated along the Red Cedar River about 5 km to the west of MSU. All
three sites featured a relatively open canopy and received direct sunlight during most of the
day. Each site was found to have water-filled containers with A. j. japonicus larvae present
within 30 meters of the experimental plots. Experimental trials were conducted from June 27July 4 and repeated from August 12-August 19 during the summer of 2010.
Experimental Design. Oviposition containers consisted of shallow plastic buckets
measuring 31 cm long × 20 cm wide × 17 cm tall. The buckets were dark green in color, which
prevented most light infiltration from the sides of the container and limited light to entering
the container from above. To manipulate algal abundance we constructed canopies that
consisted of a hardware cloth frame covered by a combination of plastic sheeting and shade
cloth (Gempler’s®, Madison, WI). Each canopy measured 91 × 61 cm, with a 7-cm lip along
each side, covering the containers completely and extending 20-30 cm beyond. Canopies were
propped above each container and secured by a ground stake at each corner. Three light
treatments were used in the experiment: full sun (canopy consisted of a hardware cloth frame
covered by clear plastic sheeting), partial shade (frame covered by Gempler’s® 60% light
22

reduction cloth and clear plastic sheeting), and full shade (frame covered by Gempler’s ® 80%
light reduction cloth and two layers of black plastic). Each light treatment was replicated 3
times at each of the three field sites, for a total of 27 containers. At each field site,
experimental containers were situated into 3 groups of 3 containers, with one container of
each treatment in each group. Each group was arranged in a line along an edge (border of the
lawn against trees, shrubs, or a fence) to minimize human disturbance. The order of the
treatments within the lines was randomized.
Each container included 4 L of distilled water and 3 g of dried mixed oak and beech litter
collected from the floor of Hudson Woodland, MSU Campus. In addition, 40 mL (1:100
concentration) of a solution of inorganic nutrients and glucose (5 ppm nitrate-N, 0.5 ppm
phosphate-P, 5 ppm sulfate-S, and 20 ppm glucose; Kaufman et al. 2002), were added in order
to encourage the development of biofilms. Three unglazed ceramic tiles measuring 6.35 × 6.35
cm were also placed in each container for later quantification of benthic algal growth.
Containers were inoculated with 4 mL of an algal inoculum collected from a water-filled scrap
tire in Hudson Woodland. The algal inoculum was composed primarily of filamentous
Chlorophytes and Scenedesmus. The containers were then covered with white tulle to prevent
mosquito access and allowed to incubate under their respective light treatments for a period of
10 days. After that time, the white tulle was removed and egg collections began. To determine
the degree to which the shade treatments altered water temperature within the containers,
the temperature was monitored in one full sun and one full shade container during each

23

experimental trial using temperature loggers (TidbiT v2 Temperature Logger, Onset ®, Bourne,
MA).
Egg Collection and Processing. Ovipositional activity of A. j. japonicus females is
greatest during the hours just after dusk and just before dawn (Scott 2003). To avoid any
confounding effects of shading by the canopies themselves on oviposition behavior, canopies
were removed at dusk and replaced at dawn during experimental trials. The inside of each
container was lined with seed germination paper to facilitate egg collection, as A. j. japonicus
oviposits on vertical surfaces just above the water line. At dawn on each day of each
experimental trial, egg-laden papers from the previous night were collected and canopies were
replaced. At dusk, canopies were removed and the sides of the containers were carefully
checked for any eggs that had been laid during the day, before new oviposition papers were
added to the containers. Any eggs that had been laid during the daytime were collected,
placed onto damp paper towels in ziplock bags, and transported back to the laboratory. Upon
collection, egg papers were placed into ziplock bags and returned to the laboratory. All eggs
were counted under a dissecting microscope and stored for 10 days at 25 ± 1ºC under a 16L:8D
photoperiod regime before being hatched in broth composed of distilled water, yeast, and
Proflo (Dulmage 1971). Larvae were reared to the 3

rd

th

or 4 instar, then killed and preserved in

a solution of 70% ethanol. All larvae were later identified to species according to Darsie and
Ward (2005) and counted under a dissecting microscope.
Algal Sampling and Processing. Benthic and limnetic algae were sampled for biomass
estimates (chlorophyll a analysis) on two dates during each experimental trial: once on the first

24

day egg papers were collected, and once on the day after the last egg papers had been
collected. On each date, one ceramic tile was removed at random from each container and
placed into individual ziplock bags. Tiles were stored on ice under dark conditions until they
could be processed in the laboratory. Biofilms were removed from each tile by scraping with a
razor blade followed by a toothbrush, and brought up to a known volume with deionized water.
The sample was filtered onto a glass fiber filter (Pall Life Sciences, Ann Arbor, MI) using a 60-mL
syringe with a filter attached. The filter was immediately frozen at -20ºC and stored under dark
conditions until later extraction and quantification of chlorophyll a could be performed.
Limnetic algae were collected by submerging a 50-mL centrifuge tube in the center of each
container an inch below the water’s surface. Limnetic samples were stored on ice under dark
conditions until they could be processed in the laboratory. Each sample was poured into a
large petri dish and gently mixed to homogenize. A small amount of sample (3-10 mL) was
vacuum-filtered onto a glass fiber filter using a 12-well sampling manifold. The filter was
immediately frozen at -20ºC and stored under dark conditions for later analysis of chlorophyll a.
Frozen filters were submerged in 90% ethanol and extracted overnight at 4ºC under dark
conditions, before being quantified on a TD-700 fluorometer (Turner Designs, Sunnyvale, CA).
Samples were acidified and fluorescence measured again to correct for pheophytin (protocol
adapted from Arar and Collins 1997).
Statistical Analysis. We log-transformed our data on total number of eggs, total
number of A. j. japonicus hatched, and post-trial limnetic chlorophyll a to achieve normality.
We then performed an initial MANOVA to test for interactions between the factors of light
treatment, site, and date on the dependent variables total number of eggs, total number of A. j.
25

japonicus hatched, and post-trial limnetic chlorophyll a. Because there was a significant
interaction between site and treatment (see below), we followed our MANOVA with individual
mixed-model ANOVA tests for each field site, testing treatment as the main effect and including
date as a random effect (Potvin 2001). We also applied regression analysis to examine the
overall relationship between chlorophyll a values and oviposition/hatching variables. We
applied sequential Bonferroni adjustments to significance levels of p-values in tablewise
comparisons (Rice 1989). Statistical analyses were performed with SAS and JMP software
(www.sas.com, www.jmp.com, SAS Institute, Cary, NC).

Results
MANOVA showed (Table 2.1) significant effects of light treatment and site, primarily
related to differences in chlorophyll (Fig. 2.1). Chlorophyll a values from the first trial were
measured only from post-trial limnetic samples due to equipment failure and therefore these
were the only category used in statistical comparisons. Though not used in our statistical
analysis, an average of pre-and post-trial benthic chlorophyll a quantities is shown in Fig. 2.5.
We also saw a significant interaction between light treatment and site, indicating that A. j.
japonicus oviposition or algal growth responded differently to our light treatments at each site
and revealing that our sites could not be considered random effects. Using mixed model
ANOVA, we found significant effects of light treatment on chlorophyll a at all sites, indicating
that our manipulation of algal biomass was successful (see Fig. 2.1). Light treatment
significantly affected total number of eggs and total numbers of A. j. japonicus hatched at the
Toumey Woodlot site, but not at the River Lab or Potter Park Zoo sites (Fig. 2.2). Using data
26

from all sites, regression analyses showed no relationship between log-limnetic chlorophyll a
2

and log-total A. j. japonicus hatched (r = 0.054, p = 0.091) (Fig. 2.3A). There was also no
2

relationship in the regression between limnetic chlorophyll a and total number of eggs (r =
0.038, p = 0.159) (Fig. 2.3A).
In addition, we performed separate regression analyses for Toumey Woodlot in light of
the significant effect of light treatment on both log-total number of eggs and total number of A.
j. japonicus hatched. Our analysis showed a positive relationship between limnetic chlorophyll
2

2

and transformed total number of eggs (r = 0.365, p = 0.008) and hatched individuals (r =
0.277, p = 0.025) (see Fig. 2.3B).

Table 2.1. Results from separate mixed-model ANOVA (df = 5,12) tests for effects of light
treatment on total number of eggs, total number of hatched larvae, and post-trial limnetic
chlorophyll a at each of our three field sites.
Source
River Lab

F

P

Log Total # Eggs
Log Total # A. j. japonicus Hatched
Log Post-trial Limn. Chlorophyll a

0.0914
0.6377
18.9485

0.9132
0.5432
0.0001

Log Total # Eggs
Log Total # A. j. japonicus Hatched
Log Post-trial Limn. Chlorophyll a

7.202
6.2051
1118.623

0.0071
0.0118
< 0.0001

Log Total # Eggs
Log Total # A. j. japonicus Hatched
Log Post-trial Limn. Chlorophyll a

0.311
0.5281
60.1585

0.7377
0.601
< 0.0001

Toumey

Zoo

27

Log Post-trial Limnetic Chlorophyll a (µg/L)

2.5

Toumey
River Lab
Potter Park Zoo

2.0

1.5

1.0

0.5

0.0

Full Sun

Partial Shade

Full Shade

Figure 2.1. Mean (± 1 SE) limnetic chlorophyll a (µg/L) per container, measured post-trial. Data
are combined from experimental trials 1 and 2 (n = 6).

28

Mean Total Number Hatched

3500
3000
2500

A

Toumey
River Lab
Potter Park Zoo

2000
1500
1000
500
0

Mean Total Number Eggs

6000

B

5000

4000

3000

2000

1000

0

Full Sun

Partial Shade

Full Shade

Figure 2.2. Mean (± 1 SE) total number of hatched A. j. japonicus individuals per container (A)
and mean (±1 SE) total number of eggs laid per container (B), with experimental trials 1 and 2
combined (n = 6).

29

6000

Chlorophyll vs. Hatched Individuals
Chlorophyll vs. Eggs
Chlorophyll vs. Hatched Individuals
Chlorophyll vs. Eggs

Number of Individuals

5000
4000

A

3000
2000
1000
0

3500

B

Post-trial Limnetic Chlorophyll a (µg/L)

Number of Individuals

3000
2500
2000
1500
1000
500
0
0

50

100

150

200

Post-trial Limnetic Chlorophyll a (µg/L)
Figure 2.3. Scatter plots showing regressions for data from all sites combined (A) as well as for
the Toumey site alone (B). Data are combined from both trial 1 and trial 2.

30

40

Temperature (ºC)

35

30
25
20

Full Sun
Sun

15

Full Shade
Shade

10
5

*

*

*

7/1/2010

7/2/2010

*

0:04
3:04
6:04
9:04
12:04
15:04
18:04
21:04
0:04
3:04
6:04
9:04
12:04
15:04
18:04
21:04
0:04
3:04
6:04
9:04
12:04
15:04
18:04
21:04
0:04
3:04
6:04
9:04
12:04
15:04
18:04
21:04

0

June 6/30/2010
30

July 3

7/3/2010

40

Temperature (ºC)

35

30
25
20

Sun
Series1

15

Series2

10

Shade

5

*

*

*

8/19/2010

8/20/2010

*

0:18
3:18
6:18
9:18
12:18
15:18
18:18
21:18
0:18
3:18
6:18
9:18
12:18
15:18
18:18
21:18
0:18
3:18
6:18
9:18
12:18
15:18
18:18
21:18
0:18
3:18
6:18
9:18
12:18
15:18
18:18
21:18

0

August 18
8/18/2010

August 21
8/21/2010

Figure 2.4. Representative temperature profiles of full sun and full shade experimental
container during the study periods. Asterisks (*) represent 12 noon.

31

2
Benthic Chlorophyll a (µg/cm )

2.0

Toumey
River Lab
Potter Park Zoo

1.5

1.0

0.5

0.0

Full Sun

Partial Shade

Full Shade

Figure 2.5. Average (± 1 SE) of pre- and post-trial benthic chlorophyll a (for trial 2 only, see
results section) (n = 3).

Discussion
Invasive mosquito species are unlike the majority of invasive species in that they have
the potential to alter vector-borne disease cycles (Juliano and Lounibos 2005). They may do
this either directly by vectoring the disease or indirectly through competition with native
disease-carrying mosquitoes (Bevins 2008). In the U.S. many other disease vectors such as A.
triseriatus, Culex pipiens, and Culex restuans occur in containers with A. j. japonicus.
Competition for food and spatial resources in container habitats is often severe due to the
discrete nature of the habitats and the limited amount of microbial food resources available.
32

Algae are an understudied component of the microbial food base in container habitats and this
study addressed their potential role in influencing the local distribution of A. j. japonicus.
Within an area, the distribution of larvae of a given mosquito species is determined
largely by oviposition habits of gravid females. These oviposition preferences are also
important in determining the success of larval production (Blaustein and Kotler 1993). In
container habitats that are exposed to sunlight, primary production in the form of algal biomass
may enhance food levels and lessen the stresses of resource competition. Algae are known to
be important food resources during the larval phase for some species (Merritt et al. 1992,
Kaufman et al. 2006), but have received little attention in studies of container breeders. In
both field and laboratory experiments, A. j. japonicus have been found to emerge quicker and
in higher numbers from microcosms with abundant algal growth compared to microcosms with
little algal growth (A.R.L., unpublished data). Although the results of our experiment showed
no overall relationship between algal density and A. j. japonicus oviposition rates, at one of our
field sites (Toumey) sunlight exposure and algal biomass did appear to influence oviposition
activity. A prominent difference between the Toumey site and the other two sites was relative
algal biomass levels in sunlit (full sun and partial shade) treatments and the fully shaded
treatment. At all three sites, algal biomass in the full shade treatment was near zero, but at
Toumey, algal biomass in both the full sun and partial shade treatments was more than twice as
high as algal biomass at the River Lab and Zoo field sites (Figs. 2.1 and 2.5). It is unclear why
such a large difference in algal growth occurred between our sites, since all received the same
inoculum at the beginning of the experiment. We did observe, however, that there seemed to
be more invertebrate (mainly slug) mortalities within our containers at Toumey than at the
33

other sites. This influx of invertebrate carcasses could have raised nutrient levels within the
containers at Toumey and subsequently enhanced primary production. Since Toumey was the
only location at which we saw differences in oviposition with treatment, we suggest that algal
biomass at high levels may influence oviposition of A. j. japonicus, but the exact mechanisms
are undetermined. It is unclear whether females preferred large amounts of algae specifically
or were merely reacting to higher levels of organic matter in general. These results also
indicate that in most urban containers, where algal production is likely not as high as measured
at Toumey, algae are probably not a consistent positive cue for oviposition by A. j. japonicus.
This is supported by data from previous studies of artificial container habitats showing no
association between Aedes mosquito abundance and algal growth (Beier et al. 1983, Kling et al.
2007, Yee et al. 2010). Studies which quantitatively measured algal biomass in artificial
containers obtained chlorophyll a values which were lower than what we measured at Toumey,
but similar to our chlorophyll a measurements at our other sites (Kling et al. 2007, Yee et al.
2010). However, in habitats where detrital resources are very limited, the presence of algae
may compose a greater proportion of available organic matter food sources and consequently
may ultimately be influencing oviposition.
One potentially confounding variable in our study was temperature. Containers
exposed to full sun exhibited higher water temperatures than containers in partial or full shade
(Fig. 2.4) and this may have directly or indirectly influenced A. j. japonicus oviposition. There is
evidence that A. j. japonicus larvae are developmentally inhibited by water temperatures
greater than 30ºC (Scott 2003, Andreadis and Wolfe 2010) and some avoidance of fully exposed
containers might be expected. Our results suggest that there is little discrimination by
34

ovipositing females in this regard and that prior observations of lower A. j. japonicus densities
in sun-exposed habitats are a consequence of larval mortality at higher temperatures. This
indicates that a trade-off between algal density and temperature may exist for A. j. japonicus
larvae such that containers exposed to high levels of sunlight, supporting higher algal
production, could be attractive to gravid females but are ultimately not suitable for larvae.
Larvae may be optimally suited to containers with intermediate shade levels that still support
some algal growth but do not reach lethal maximum temperatures. Regardless, the large
numbers of eggs laid in all of our experimental containers demonstrate that A. j. japonicus
females are clearly willing to deposit eggs in open, sun-exposed containers and risk larval
mortality. Resultant larval distribution characteristics likely result from post-hatch mortality
and not necessarily from female choice.
Another potentially confounding factor comes from the algal community composition in
our containers. We inoculated our containers at the beginning of the experiment with a culture
of mixed algal taxa. This was augmented during the trials by natural algal colonization from the
surrounding environment (i.e., air-borne inocula). Freshwater algal communities are often
highly diverse and reflect light and nutrient inputs, as well as physiochemical conditions such as
pH and temperature (Wehr and Sheath 2003). In our containers, we documented the presence
of Scenedesmus, Oedogonium, and many unidentifiable coccoid Chlorophytes, and some of
these algae may not have been ideal foods for larvae. Marten (1986, 2007) documented the
occurrence of certain algal taxa in mosquito habitats that are indigestible to larvae due to the
presence of the carotenoid sporopollenin in their cell walls. In addition, certain taxa of algae,
especially the Cyanobacteria, are known to produce toxins that are damaging or lethal to
35

mosquito larvae (Dhillon et al. 1982, Kiviranta et al. 1993). Though we believe that our algal
communities were representative of those occurring naturally in container habitats, some of
the algae colonizing our containers may not have been good larval food resources and
potentially less attractive as stimuli for oviposition. The effect of larval habitat microbial
community composition on oviposition by container breeding mosquitoes is only beginning to
be examined and algal communities may play a direct role by influencing groups such as
bacteria (Espeland et al. 2001).
We documented a small amount of outside oviposition from mosquito species other
than A. j. japonicus occurring in our containers. The species with the largest contribution to this
outside ovipositional activity was A. triseriatus. These contributions were extremely minimal,
representing less than 1% of the total number of larvae hatched. Some ovipositional activity by
Anopheles quadrimaculatus and C. restuans was also observed in our containers, but we
estimate that the two species together made up less than 0.5% of the potential larval hatch in
our containers during the study period. These hatch rates confirm that A. j. japonicus is a
dominant artificial container-breeding mosquito in our area. Similar results were obtained by
Andreadis and Wolfe (2010), who showed in larval surveys in Connecticut that A. triseriatus and
Aedes atropalpus were displaced to a significant percentage by A. j. japonicus in many container
habitats.
From our study, we conclude that at low concentrations typical of shaded container
habitats, algae do not affect oviposition choices of A. j. japonicus. At higher concentrations,
however, algae can have a significant positive impact on A. j. japonicus oviposition. The precise
role of autotrophs in the ecology of water-filled container systems remains enigmatic, and we
36

believe the theme of autotrophy in detrital-based mosquito habitats necessitates further
exploration. Since A. j. japonicus has become a dominant container-breeder in mid-Michigan
and elsewhere, further studies into the ecology of this species and its impacts on native
communities are clearly necessary. A. j. japonicus has the potential to alter disease cycles
through interaction with local mosquito species, and its success is related to how well it exploits
larval habitats.

37

LITERATURE CITED

38

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42

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mosquito species and stages in discarded vehicle tires. J. Med. Entomol. 47: 53-62.

43

CHAPTER THREE
DO ALGAE AFFECT LARVAL COMPETITION BETWEEN THE INVASIVE MOSQUITO, AEDES
JAPONICUS JAPONICUS, AND NATIVE SPECIES?

44

Abstract
Aedes (Finlaya) japonicus japonicus (Theobald) is an invasive mosquito species of
medical concern in the U.S. This species breeds in container habitats and may be responsible
for the displacement of native mosquitoes. Field surveys of larval habitats have noted that
larvae of A. j. japonicus occur in a wide range of containers including sunlit habitats. We
postulated that the utilization of algae as an additional food resource in sunlit habitats would
enhance production of A. j. japonicus larvae and provide a competitive advantage over native
mosquito species. We conducted two microcosm experiments, one in the laboratory and one
in the field, where we tested performance of A. j. japonicus under sunlit and shaded conditions,
both alone and in competition with two local species, Aedes triseriatus, and Culex pipiens. Our
experimental results indicate that algae do provide an overall benefit to mosquito larvae by
enhancing emergence rates, but do not alter outcomes of resource competition between these
species. Of the three species, C. pipiens showed the strongest reliance on algal foods. Aedes j.
japonicus and A. triseriatus performed similarly under sunlit and shaded conditions and
affected algal and other microbial groups similarly. We observed substantial competitive
asymmetry between A. j. japonicus and C. pipiens, as no C. pipiens individuals survived the
experiment when in competition with A. j. japonicus. We postulate that at low resource
conditions such as those found in our microcosms, A. j. japonicus is a superior competitor over
C. pipiens. In contrast, we observed no strong evidence of competitive asymmetry between A.
j. japonicus and A. triseriatus; however, A. j. japonicus females emerged quicker and in greater
numbers than A. triseriatus females in both experiments. In addition, unlike the other two
species, A. j. japonicus survival rates were not affected by algae in either experiment. Along
45

with the faster emergence of females under both sunlit and shaded conditions, this evidence
suggests that this species may be better able to tolerate low-resource conditions than its
competitors. We propose that habitat generalism may contribute to the success of A. j.
japonicus as an invader.

Introduction
Mosquitoes are of human concern as vectors of disease but invasive mosquito species
are especially problematic. These organisms can potentially introduce new diseases into a
system or alter existing disease cycles (reviewed by Juliano and Lounibos (2005)). The mosquito
species Aedes (Finlaya) japonicus japonicus (Theobald) is invasive in the United States, Canada,
and Western Europe (Peyton et al. 1999, Thielman and Hunter 2006, Schaffner et al. 2009,
Versteirt et al. 2009). This species is of medical concern in its invaded range because it is a
competent vector of a suite of arboviruses that occur in those areas, including Japanese
encephalitis virus (Takashima and Rosen 1989), LaCrosse encephalitis virus (Sardelis et al.
2002), St. Louis encephalitis virus (Sardelis et al. 2003), eastern equine encephalitis virus
(Sardelis et al. 2002), and West Nile encephalitis virus (Sardelis and Turell 2001). It has been
suggested that A. j. japonicus may even act as a bridge vector in cycles of West Nile virus, and
though this has not yet been confirmed in the field (Molaei et al. 2009), adult field surveys in
the U.S. have collected A. j. japonicus females infected with West Nile virus (CDC 2009). In
addition to its direct role as a vector, larvae of A. j. japonicus may affect disease cycles by
competing with other vector species (Juliano and Lounibos 2005). It is important to examine

46

the role of A. j. japonicus as a competitor and its effects on the production of native species, in
order to better understand and predict repercussions of its invasion.
Larvae of A. j. japonicus occur primarily in water-filled container habitats (Tanaka et al.
1979, Bevins 2007). The discrete and usually isolated nature of these habitats limits the
availability of food and spatial resources for larvae. As a result, competition for existing
resources is often severe, and resource competition between two species may result in the
dominance of one species to the detriment of the other (reviewed by Juliano (2009)). The
status of A. j. japonicus as an effective competitor is unclear (Armistead et al. 2008, Armistead
et al. 2008, Alto 2011, Hardstone and Andreadis 2012), however, there is some evidence for the
displacement of certain native mosquito species in containers in areas where A. j. japonicus is
abundant (Andreadis and Wolfe 2010).
Rather than superiority in competitive interactions, the versatility of A. j. japonicus
larvae in field containers may be partially responsible for its abundance. Aedes j. japonicus
larvae have been observed to be very tolerant compared to other mosquito species in terms of
resource levels and habitat types which can support larval growth (Andreadis et al. 2001, Bevins
2007). This apparent high level of tolerance exhibited by A. j. japonicus larvae may be
especially important to its invasive success by allowing it to exploit a greater variety of habitats
in peridomestic settings (Juliano and Lounibos 2005). Joy and Sullivan (2005) showed that A. j.
japonicus larvae exploited a wider range of habitat conditions than native competitor Aedes
triseriatus, a species which often occurs in container habitats with A. j. japonicus (Thielman and
Hunter 2006, Bevins 2007). Aedes j. japonicus was found to occur in more sunlit and urban
containers than A. triseriatus, which was restricted to shaded woody areas. We have observed
47

A. j. japonicus oviposition occurring extensively in field containers exposed to full sunlight
(A.R.L., unpublished data). A wider range of tolerance in terms of sunlight and shade
specifically may benefit larvae of A. j. japonicus by allowing them to occupy habitats where
competitors are absent, as well as granting them access to algae as an additional food resource.
Though much attention has been given to the use of toxic and indigestible species of
algae as mechanisms for mosquito control, some of these studies have also documented
positive effects of algae on growth of larvae (Marten 1986, Rey et al. 2009). In a review of
larval mosquito foods and feeding behavior, Merritt et al. (1992) noted that algae contain lipids
and proteins essential for mosquito growth which are absent in certain other common food
sources such as bacteria. Larval mosquitoes are known to also ingest fungi, protozoa, and fine
particulate detritus which also provide high-quality nutrition, however, the addition of algae in
sunlit habitats may provide an additional advantage.
Many species of mosquito are known to rely on algae to some degree. Bond et al.
(2005) demonstrated that adult Anopheles pseudopunctipennis prefer to deposit eggs on mats
of filamentous algae, and larvae which were fed exclusively on the algae developed at the same
rate and achieved the same size as larvae fed a high-quality lab diet. The presence of algae in
larval habitats has been noted to increase pupation rates of larval An. gambiae (Kaufman et al.
2006b). In addition, abundance of A. aegypti larvae was positively correlated with the presence
of algae and other organic material in a survey of larval habitats in Salinas, Puerto Rico (Barrera
et al. 2006).
In sunlit containers which support mosquito larvae, A. j. japonicus may develop faster
and survive in greater numbers when compared to larvae in shaded habitats. In addition, for
48

this reason A. j. japonicus may have the greatest potential for growth during the spring and
autumn of the year, when sunlight is at a maximum due to the lack of deciduous tree leaves,
and temperatures are moderate. Because A. j. japonicus are known to occupy sunlit habitats,
they may be more adapted for eating algae than other species that are typically shade-dwellers,
such as A. triseriatus. In partially-sunlit habitats where the two species overlap, A. j. japonicus
may be better able to consume and digest algae, and so gain a competitive advantage over A.
triseriatus. In the following experiments, we tested the hypotheses that (a) A. j. japonicus
derives an advantage from utilization of algal foods, and (b) increased ability to exploit algal
foods provides A. j. japonicus with a competitive advantage over the local species A. triseriatus
and Culex pipiens. We tested these hypotheses in two separate microcosm experiments, one
conducted in the laboratory and the other under field conditions.

Materials and Methods
Laboratory Experiment
Experimental Design. We designed a laboratory microcosm experiment to examine the
effects of algae on survival, growth rates, and competitive performance of A. j. japonicus and
the local mosquito species A. triseriatus and C. pipiens. We chose these two local species
because their larvae commonly co-occur with larvae of A. j. japonicus in container habitats
(personal observation). The experiment was conducted in August and September of 2011. We
constructed two types of microcosm using pint glass Mason jars. “Clear” microcosms were jars
left unaltered. “Shaded” microcosm jars were wrapped in a layer of aluminum foil in order to
prevent light infiltration. Each microcosm was covered with fine mesh to prevent adult
49

mosquitoes from escaping, and then further covered with a lid constructed of either clear
plastic (for “clear” microcosms) or black plastic (for “shaded” microcosms), which was gently
propped above the top of the microcosm to allow air flow to occur. We added 300 mL of deionized water, 3 mL of nutrient solution (5 ppm nitrate-N, 0.5 ppm phosphate-P, 5 ppm sulfateS, and 20 ppm glucose; Kaufman et al. 2002), and 0.5 g of dried mixed oak and beech leaf litter
collected from Hudson Woodland (Michigan State University Campus) to each microcosm to
encourage microbial growth. We also tethered 4 glass microscope slides in each microcosm for
later quantification of benthic algal biomass. We then added 8 mL of slurried leaves and water
collected from a container in the field which held abundant algae (mainly Scenedesmus spp.).
Microcosms were positioned at random in a Percival incubator (Model I41VLC8, Percival
Scientific, Inc., Perry, IA, USA) at 25ºC on a 16:8 light:dark cycle.
Microbial communities were left to develop for 5 days, after which time each
microcosm received 30 first-instar mosquito larvae. Larvae were added to microcosms in the
following 5 treatment combinations: (1) 30 A. j. japonicus, (2) 30 A. triseriatus, (3) 30 C. pipiens,
(4) 15:15 A. j. japonicus/A. triseriatus, (5) 15:15 A. j. japonicus/C. pipiens. The experiment
included 7 replicates of each larval treatment in clear microcosms, and 7 replicates of each
larval treatment in shaded microcosms. After the addition of larvae, microcosms were replaced
in the incubator and rotated approximately every 4 days.
th

Once the majority of the larvae had reached the 4 instar, we sampled algal biomass
for in all microcosms using the technique described below. In addition to sampling for algae,
we also took water samples to estimate bacterial productivity. To quantify bacterial
production, we removed 2 mL of water from each microcosm, and measured the absorption
50

3

into bacterial biomass of [ H]-leucine (50 Ci/mmol, Life Science, Boston, MA, USA) (see PelzStelinski et al. 2010 for a more detailed protocol).
Microcosms were checked daily for adult emergence. Adults were collected from the
microcosms as they emerged and immediately frozen, and later were lyophilized and weighed.
After the bulk of the mosquitoes had emerged the experiment was dismantled. All remaining
larvae were counted and identified to species. Remaining leaf material was dried and weighed
in order to estimate the amount of decomposition that had occurred.
Mosquitoes. We hatched eggs from three species of mosquito for our experiment: A. j.
japonicus, A. triseriatus, and C. pipiens. Aedes j. japonicus eggs were purchased from the
Fonseca lab at Rutgers University (see Williges et al. 2008) and A. triseriatus eggs were taken
from our laboratory colony at Michigan State University. The evening before adding larvae to
our microcosms, A. j. japonicus eggs and A. triseriatus eggs were flooded with a high-organicmatter broth and left to hatch overnight. Culex pipiens egg rafts were collected from the field
early the morning of the day of larval addition, and left on a sunny windowsill to hatch.
Algal Sampling and Quantification. From each microcosm, we removed 15 mL of water
and filtered onto a glass fiber filter, which was promptly wrapped in aluminum foil and frozen
for later chlorophyll a quantification. We also removed 2 glass slides from each microcosm.
These were placed in a Petri dish where all adhering material was removed by scraping with a
razor blade. The scrapings were brought up to 15 mL with de-ionized water, after which 5 mL
were filtered onto a glass fiber filter, wrapped in foil, and frozen. Chorophyll a samples were
extracted in 90% ethanol overnight under dark conditions, and quantified on a TD-700
fluorometer (Turner Designs, Sunnyvale, CA, USA). After initial quantification, samples were
51

acidified with 6 drops of 0.1N hydrochloric acid (HCl) and quantified again to account for
pheophytin (protocol adapted from Arar and Collins 1997).
Statistical Analysis. We performed a two-factor ANOVA to test for effects of our light
and larval treatments on log-transformed chlorophyll a measurements. We also used twofactor ANOVA to test for effects of light and larval treatment on bacterial production as well as
total amount of leaf mass loss. We observed significant interaction between light and larval
treatment in the amount of leaf mass loss, and so performed subsequent ANOVAs on sunlit and
shaded treatments separately.
The mosquito production parameters of proportion surviving and proportion of
emerged adults were arcsine square root transformed in order to meet the assumptions of
normality, then tested for effects of light and larval treatments using ANOVA. The other
mosquito production parameters measured, including mean weights, total weights, and
emergence times, did not meet the assumptions of normality even when transformed, and so
were tested using non-parametrics (Kruskal-Wallis). Statistics were run using SYSTAT statistical
software (Version 11, Systat Software, Inc., Chicago, IL, USA).

Field Experiment
Experimental Design. We designed a similar microcosm experiment to our laboratory
experiment in order to determine the effects of algae on the survival, growth, and competitive
ability of A. j. japonicus larvae compared to A. triseriatus larvae under more natural field
conditions. This experiment was conducted in August 2011 on the grounds of the North Central
Regional Aquaculture Facility at MSU. This location was ideal for our study because it was open
52

to full sunlight and close to natural populations of A. j. japonicus and A. triseriatus. Microcosms
were constructed from white plastic Sweetheart® cups (900 mL capacity). We constructed two
types of microcosm: “light” microcosms were open to sunlight and intended to facilitate algal
growth, and “dark” microcosms were completely covered to discourage algal growth. Dark
microcosms were covered on the sides with black plastic to prevent light infiltration. Light
microcosms were not covered.
To ensure that water temperatures would not exceed the lethal range for larvae (Scott
2003), we covered the tops of all microcosms with a layer of aluminum foil to reflect heat. The
foil coverings on light microcosms were perforated in order to allow for sunlight penetration.
Shaded microcosms were covered with un-perforated foil. Each microcosm received 550 mL of
deionized water, 1 g of dried oak litter collected from the floor of Hudson Woodland on MSU’s
campus, and 5.5 mL (1:100 concentration) of a nutrient solution designed to mimic natural field
conditions (5 ppm nitrate-N, 0.5 ppm phosphate-P, 5 ppm sulfate-S) developed by Kaufman et
al. (2002). We also added an inoculum of algae collected from containers in the field which
supported A. j. japonicus. To facilitate later quantification of chlorophyll a, we placed 4 pre-cut
leaf cores in each microcosm. Leaf cores were attached with pins to a rubber stopper and
submerged in each microcosm. In addition, we placed 2 pre-cut strips of the same plastic
material which composed the microcosms to the inside of each microcosm. Throughout the
duration of the experiment, water levels of the microcosms were checked regularly and
evaporated water was promptly replaced with distilled water. Infiltration of rainwater into
microcosms was minimal.

53

Microcosms were allowed to incubate for 7 days in order to allow for the development
of microbial communities. First-instar mosquito larvae were added to the microcosms on the
eighth day. We utilized four larval treatments for this study: (1) 40 A. j. japonicus larvae, (2) 40
A. triseriatus larvae, (3) 20:20 A. j. japonicus: A. triseriatus larvae, and (4) no larvae. Larval
th

growth was monitored, and once larvae reached the 4 instar, microcosms were checked for
emerged adults once daily. All adults were collected with a mouth aspirator and frozen before
being lyophilized and weighed. We sampled the microbial community in all microcosms once
adults had started to emerge – that timepoint signifying that feeding by mature larvae had
been occurring for some time. The experiment was dismantled in September when
temperatures had started to drop significantly, and remaining larvae were collected, identified,
and frozen. Remaining leaf material was subsequently dried and weighed in order to determine
the amount of leaf mass loss.
Mosquitoes. Eggs of A. j. japonicus were purchased from the Fonseca lab at Rutgers
University, and eggs from A. triseriatus were hatched from our laboratory colony. For both
species, eggs were flooded the night before larval addition with nutrient broth, and left to
st

hatch overnight. Individual 1 -instar larvae were counted into microcentrifuge tubes in the
laboratory before being added to microcosms in the field.
Microbial Sampling. To sample microorganisms in the water column, we collected 70
mL of water from each microcosm. Water samples were placed on ice in darkness for transport
to the laboratory. To sample benthic and container wall microorganisms, we collected all of our

54

pre-cut leaf cores and one plastic strip from each microcosm. These were placed in plastic bags
and stored on ice in darkness until returned to the lab.
In the lab, water samples were partitioned, with 5 mL filtered onto a glass fiber filter,
wrapped in foil, and frozen for later chlorophyll a quantification, and 3 mL preserved in 4%
formalin and stored in darkness at 4ºC. The remaining volume of water was recorded and
centrifuged at 4000 rpm for 30 minutes at 4ºC. The supernatant was discarded and the pellet
suspended in 5 mL of HPLC-grade methanol for later DNA extraction. Plastic strips were
scraped with a razor blade to remove all adhering material. Scrapings were brought up to a
known volume with de-ionized water and filtered onto a glass fiber filter which was promptly
wrapped in foil and frozen for chlorophyll a quantification. Leaf cores were removed from their
rubber stopper and placed into a clean plastic bag. A small amount of de-ionized water was
added to the bag and leaf cores were gently rubbed between fingers to dislodge looselyadhering material. This water was placed into a 15-mL centrifuge tube and stored on ice in
darkness temporarily. The leaf cores were removed from the bag and placed into a scintillation
vial holding 2 mL of de-ionized water. Leaf cores were then placed in a sonicating bath on ice
(Model 2510, Branson Ultrasonics, Danbury, CT, USA) for 5 minutes to remove additional
microorganisms. This was brought up to 15 mL with de-ionized water. Three mL of water were
preserved in 4% formalin for later algal community analysis. Four milliliters of water were
centrifuged at 13,000 rpm at 4ºC for 5 minutes. The supernatant was discarded and the pellet
re-suspended in 2 mL of HPLC-grade methanol and stored at -20ºC for later DNA extraction.
The remaining water was filtered onto a glass fiber filter, wrapped in foil, and frozen for later
analysis of chlorophyll a.
55

Algal Quantification. We measured chlorophyll a concentration to estimate algal
biomass. Frozen filters were placed in 10 mL of 90% ethanol and refrigerated (4°C) overnight in
darkness to extract chlorophyll a. Samples were quantified on a TD-700 fluorometer (Turner
Designs, Sunnyvale, CA, USA). We added 0.01N HCl to acidify the samples, and quantified on
the fluorometer again to correct for pheophytin (protocol adapted from Arar and Collins 1997).
Algal Community Composition. In order to detect differences in algal community
composition, formalin-preserved leaf and water samples were counted in a Palmer cell under a
light microscope at 40X magnification. We counted an average of 300 cells for each sample.
Where possible, taxa were identified to the lowest level possible using Wehr and Sheath (2003).
However, many of the taxa observed in our samples were not identifiable – these we grouped
together by similarity (e.g. small green circular cells similar in size were grouped together). The
proportion of different taxa in each sample was determined from the total.
Bacterial and Fungal Abundance. Abundance of bacteria and fungi in water and leaf
samples was determined using real-time quantitative PCR (qPCR). Methanol-preserved samples
from leaves and water were extracted using an UltraClean® Soil DNA Isolation Kit (MO BIO
Laboratories, Inc., Carlsbad, CA, USA). Extracted DNA samples were diluted with sterilized
water at a ratio of 1:10 prior to analysis due to high concentrations. Because we were
interested in quantifying bacteria and fungi without further taxonomic resolution, we utilized
highly conserved small-subunit ribosomal DNA regions for our qPCR reactions (Fierer et al.
2005). The 16S rRNA region was amplified for bacteria, and the internal transcribed spacer (ITS)
region was amplified for fungi (Table 3.1). We used the fluorescent dye SYBR® Green I (Bio-Rad

56

Laboratories, Hercules, CA) to quantify DNA during our qPCR. Each reaction was conducted in
triplicate (positive and negative controls included), with 14.5 µL iQ™SYBR® Green Supermix
(Bio-Rad), 2.5 µL Bovine Serum Albumin (New England Biolabs, Ipswich, MA), 1.25 µL each of
forward and reverse primer, 1.5 µL sterilized water, and 5 µL of our diluted DNA sample. All
reactions were conducted in 96-well PCR plates sealed with film, and cycled on a Bio-Rad iQ™ 5
iCycler.
Table 3.1. Primer pairs used in bacterial and fungal qPCR reactions (see Fierer et al. 2005).
Name

Annealing

Sequence

Amplified

Approximate

Temp. (ºC)

Target

Region

Length (bp)

Bacteria

EUB 338

ACTCCTACGGGAGGCAGCAG

53

16S

200

Bacteria

EUB 518

ATTACCGCGGCTGCTGG

53

16S

200

Fungi

ITSF1

TCCGTAGGTGAACCTGCGG

53

ITS

300

Fungi

5.8s

CGCTGCGTTCTTCATCG

53

ITS

300

Statistical Analysis. The mosquito production parameters of proportion surviving and
proportion of emerged adults were arcsine square root transformed, and the parameter of
total weights was log-transformed, in order to meet assumptions of normality. We performed
a MANOVA to test for overall effects of our light and larval treatments on the mosquito
production parameters of survivorship, total weights, and mean time-to-emergence, followed
by separate ANOVA tests or Kruskal-Wallis tests on individual mosquito production parameters.
For our interspecific competition treatments, we separated each species before analyzing the

57

parameters of survivorship, proportion emerged, mean weight, and time to emergence. Total
weights were analyzed from combined species data.
We log-transformed our chlorophyll a data in order to meet the assumptions of
normality, and conducted a MANOVA to test for effects of light and larval treatments on
chlorophyll a associated with leaf surfaces, microcosm surfaces, and the water column. These
were followed with individual ANOVA tests for each zone. To examine algal community
composition, we conducted principle component analyses (PCA) on leaf-associated and water
column-associated algal taxa utilizing log-ratio transformed percentages using the covariance
matrix (Kaufman et al. 1999). A MANOVA was run on the resulting PCA scores using light and
treatment as factors, and if significant, followed by individual ANOVA tests on each principle
component (1, 2, 3). Additional ANOVA tests were conducted on variables showing high
loading values for PC axes that showed significant treatment effects (see Results section) (JMP,
SAS Institute, Inc., Cary, NC, USA). Concentrations of bacterial and fungal DNA were also logtransformed to meet the assumptions of normality. We first conducted a MANOVA to
determine the effects of light and larval treatment on bacterial and fungal DNA on leaf surfaces
and in the water column. Individual ANOVA tests on each parameter followed significant
MANOVA. To examine the effects of light and larval treatment on leaf decomposition, we
conducted a two-factor ANOVA test on the amount of leaf mass loss. ANOVA and MANOVA
tests were conducted using SYSTAT statistical software (Version 11, Systat Software, Inc,
Chicago, IL, USA).

58

Results
Laboratory Experiment
Mosquito Production. Our MANOVA testing the effects of light and larval treatments
on the mosquito production parameters of total adult weight, proportion surviving, and
proportion emerged revealed significant effects of light (MANOVA; df = 3,82; p = < 0.0001) and
larval treatment (MANOVA; df = 18,252; p < 0.0001), as well as a significant interaction effect
(MANOVA; df = 18,252; p < 0.0001). Results from our ANOVA and Kruskal-Wallis tests on
individual mosquito production parameters revealed significant effects of both light and larval
treatments on the proportion of individuals surviving the duration of the experiment as well as
the proportion of adults emerged from each microcosm (see Table 3.2 and Figure 3.1). We
observed significant effects of light treatment on male total weights (species from interspecific
treatments not separated) (Figure 3.2), as well as mean male weights (species from interspecific
treatments were separated) (Table 3.2 and see Figure 3.1). We were unable to test female
parameters due to low emergence rates. Interestingly, no C. pipiens survived from interspecific
treatments with A. j. japonicus.

Table 3.2. ANOVA results from our mosquito production parameters of survivorship and the
proportion of adults that emerged from each microcosm (df = 1,6,84).
Parameter
Proportion Surviving

Proportion Emerged

Source
Light
Larvae
Light x Larvae
Light
Larvae
Light x Larvae

59

F
19.612

P
<0.001

35.863
11.409
23.568
5.343
1.328

<0.001
<0.001
<0.001
<0.001
0.254

Table 3.3. Kruskal-Wallis analysis of overall mosquito production parameters in laboratory
microcosms. Degrees of freedom are in parentheses.

Source
Light
Larvae

Mean
Mean Male
Total Adult
Male
Emergence
Weight
Weight
Day
32.327(1)** 5.232(1)* 1.305(1)ns
1.911(4)ns 11.599(5)* 10.925(5)ns

* p < 0.05; ** p < 0.01; ns, p > 0.05.

60

Figure 3.1.

61
th J)

C (wi
th J)

C (alo
ne)

T (wi

ne)

T (alo

h C)

J (wit

h T)

C (wi

th J)

ne)

th J)

ne)

C (alo

T (wi

T (alo

h C)

J (wit

h T)

J (wit

ne)

J (alo

Proportion Surviving
0.4

J (wit

J (alo
ne)

Proportion of Emerged Adults
0.5

Sunlit
Shaded

A

0.3

0.2

0.1

0.0

0.25

B

0.20

0.15

0.10

0.05

0.00

62

Figure 3.1. (con’d)

C (wi

th J)

ne)

th J)

C (alo

T (wi

ne)

T (alo

h C)

J (wit

h T)

J (wit

ne)

h T)

C (wi

th J)

ne)

th J)

C (alo

T (wi

T (alo
ne)

h C)

J (wit

J (wit

ne)

J (alo

Mean Weight (mg) (Females)
0.4

J (alo

Mean Weight (mg) (Males)
0.5

Sunlit
Shaded

C

0.3

0.2

0.1

0.0

0.8

D

0.6

0.4

0.2

0.0

63

Figure 3.1. (con’d).

C (wi
th J)

ne)

th J)

C (alo

T (wi

ne)

T (alo

h C)

J (wit

h T)

J (wit

ne)

J (alo

Mean Emergence Day (Males)

th J)

C (wi

th J)

C (alo
ne)

T (wi

ne)

h C)

h T)

T (alo

J (wit

J (wit

ne)

J (alo

Mean Emergence Day (Females)
40

Sunlit
Shaded

E

30

20

10

0

50

F

40

30

20

10

0

Figure 3.1. (con’d) Summary of mosquito production parameters from our laboratory
experiment: Mean (± 1 SE) (A) proportion of individuals surviving, (B) proportion of emerged
adults, (C) female weight, (D) male weight, (E) female emergence day, and (F) male emergence
day. Columns without error bars represent a sample size of 1. Key to abbreviations: J (alone) =
A. j. japonicus (intraspecific), T (alone) = A. triseriatus (intra.), C (alone) = C. pipiens (intra.), J
(with T) = A. j. japonicus when in competition with C. pipiens, etc.

64

Figure 3.2. Mean (± 1 SE) total from each treatment. Species from interspecific treatments are
combined. (Legend key: J = A. j. japonicus (intraspecific), T = A. triseriatus (intra.), C = C. pipiens
(intra.), JT = A. j. japonicus + A. triseriatus, JC = A. j. japonicus + C. pipiens).

Microbial Parameters. Our clear microcosms exhibited significantly higher limnetic and
benthic algal biomasses than our shaded microcosms, indicating that our light treatments were
successful in the manipulation of algae (see Figure 3.3). We also saw a significant effect of
larval treatment on limnetic chlorophyll a, as well as a significant interaction between light
treatment and larval treatment. Benthic chlorophyll was not affected by larval treatment and
there was no interaction between light and larval treatment (see Table 3.4). Our
measurements of bacterial production in the water column showed a significant effect of light
treatment (ANOVA; F = 469.089; p < 0.0001) and larval treatment (ANOVA; F = 3.323; p = 0.016)
65

on leucine incorporation rates, but no significant interaction (ANOVA; F = 0.785; p = 0.539) (see
Figure 3.4).

Table 3.4. ANOVA table for chlorophyll a in laboratory microcosms (df = 4, 60).
Parameter

Limnetic Chlorophyll a

Benthic Chlorophyll a

Source
Light
Larvae
Light × Larvae
Light
Larvae
Light × Larvae

66

F
1834.238
5.951
3.396
479.242
0.522
0.743

P
< 0.0001
< 0.0001
0.014
< 0.0001
0.72
0.566

Log Limnetic Chlorophyll a (µg/L)

3.5

A

A. j. japonicus
A. triseriatus
C. pipiens
A. j. japonicus + A. triseriatus
A. j. japonicus + C. pipiens

3.0

2.5

2.0

1.5

1.0

0.5

Log Benthic Chlorophyll a (µg/cm2)

0.0
1.6

B

1.4
1.2
1.0
0.8
0.6
0.4
0.2
0.0

Sunlit

Shaded

Figure 3.3. Mean (± 1 SE) chlorophyll a sampled during the middle of our laboratory microcosm
experiment in the water column (A) and on suspended glass slides (B) (n = 7).

67

6

pmol/mL/hr.

5

J
T
C
JT
JC

4

3

2

1

0

Sunlit

Shaded

Figure 3.4. Bacterial production, measured as the rate of leucine incorporation. J = A. j.
japonicus (intraspecific), T = A. triseriatus (intra.), C = C. pipiens (intra.), JT = A. j. japonicus + A.
triseriatus, JC = A. j. japonicus + C. pipiens.

68

Leaf Decomposition. The log-transformed amount of decomposition, measured in
terms of quantity of leaf mass loss during the experiment, overall was significantly affected by
light (ANOVA, F = 126.250, p < 0.0001), but not by larval treatment (ANOVA, F = 1.945, p =
0.1115), though there was a significant interaction effect between light and larval treatment
(ANOVA, F = 2.775, p = 0.035) (see Figure 3.5). Because the interaction effect was significant,
we then analyzed leaf mass loss in light and dark microcosms separately. We noticed a
significant effect of larval treatment on log-transformed leaf mass loss in light microcosms
(ANOVA, F = 3.296, p = 0.024) but not dark microcosms (ANOVA, F = 1.880 , p = 0.140).

Leaf Mass Lost (g)

0.18

0.16

J
T
C
JT
JC

0.14

0.12

0.10

Sunlit

Shaded

Figure 3.5. Mean (± 1 SE) total leaf mass lost from each treatment (n = 7). J = A. j. japonicus
(intraspecific), T = A. triseriatus (intra.), C = C. pipiens (intra.), JT = A. j. japonicus + A. triseriatus,
JC = A. j. japonicus + C. pipiens.
69

Field Experiment
Mosquito Production. Our initial MANOVA testing effects of treatment on the
mosquito parameters of total adult weight, proportion surviving, and proportion emerged
revealed a significant effect of both light (MANOVA; df = 3,32; p < 0.0001) but not larval
(MANOVA; df = 6,66; p = 0.539) treatment, and no significant interactions between the two
(MANOVA; df = 6,66; p = 0.447). We then performed individual ANOVA tests or Kruskal-Wallis
tests to further examine these results (Tables 3.5 and 3.6). Our tests indicated that there were
no significant effects of either light or larval treatments on total survival, however, we did see
significantly greater numbers of total emerged adults for both species in light microcosms
relative to dark (Figure 3.6). We were unable to test results of female mosquito production for
all larval treatments due to overall low emergence rates of A. triseriatus, however, we did test
emergence rates of female A. j. japonicus and found a significant positive effect of sunlight
(ANOVA, p < 0.0001). Male weights were not significantly affected by either light or larvae, but
male time to emergence was significantly influenced by both light and larval treatment (Figure
3.6). Total adult weights were significantly affected by light but not larval treatments (Table
3.7).

Table 3.5. ANOVA results from our mosquito production parameters of survivorship and the
proportion of adults that emerged from each microcosm (df = 1,3,46).
Parameter
Proportion Surviving

Proportion Emerged

Source
Light
Larvae
Light x Larvae
Light
Larvae
Light x Larvae
70

F
0.446
1.631
0.216
15.450
0.515
0.575

P
0.508
0.195
0.885
<0.0001
0.674
0.634

Table 3.6. Kruskal-Wallis test results from remaining non-normal mosquito production
parameters. Degrees of freedom are in parentheses.

Source

Total Adult
Weight

Mean Male
Weight

Mean Male
Emergence
Day

Light

9.301(1)*

0.497(1)ns

4.351(1)*

Larvae

2.271(2)ns

4.603(3)ns

22.236(3)**

* p < 0.05; ** p < 0.01; ns, p > 0.05.

71

ne)

70

E

50

40

30

20

10

0

Figure 3.6.

72

th J)

T (wi

th J)

0.0

T (wi

0.1

ne)

0.2

T (alo

0.3

ne)

0.4

T (alo

0.5

th J)

T (wi

T (alo
ne)

h T)

J (alo
ne)
J (wit

0.0

h T)

0.1

J (wit

0.2

Proportion of Individuals Emerging

0.3

h T)

ne)

C
Mean Weights (mg) (Males)

th J)

T (wi

T (alo
ne)

h T)

J (wit

J (alo
ne)

Proportion Surviving

A

J (wit

60

J (alo

0.6

ne)

th J)

0.7

Mean Emergence Day (Males)

T (wi

T (alo
ne)

h T)

J (wit

J (alo
ne)

Mean Weight (mg) Females

Sunlit
Shaded

J (alo

th J)

T (wi

ne)

T (alo

h T)

J (wit

J (alo

Mean Emergence Day (Females)
0.4
0.3

B

0.2

0.1

0.0

0.25

D

0.20

0.15

0.10

0.05

0.00

60

50

F

40

30

20

10

0

Figure 3.6 (con’d). Total (± 1 SE) number of mosquitoes which survived the duration of the
experiment, either as adults who emerged or larvae remaining when microcosms were
dismantled in field microcosms (A), total (± 1 SE) number of adults emerging from each
treatment (B) in our field microcosms, (C) mean weight (± 1 SE) of females and (D) males, mean
emergence day (± 1 SE) of females (E) and males (F). Key to abbreviations: J (alone) = A. j.
japonicus (intraspecific), T (alone) = A. triseriatus (intra.), J (with T) = A. j. japonicus when in
competition with A. triseriatus, etc.

73

Mean Total Adult Weight (mg)

2.0

Sunlit
Shaded
1.5

1.0

0.5

0.0

J

T

JT

Figure 3.7. Mean (± 1 SE) adult weights in each treatment in field microcosms. (Legend key: J
= A. j. japonicus (intraspecific), T = A. triseriatus (intra.), and JT = A. j. japonicus + A. triseriatus).

Microbial Parameters. We performed a MANOVA to examine the effects of light
treatment and larval treatment on chlorophyll a measured on leaf surfaces, in the water
column, and along the sides of our microcosms. We saw a significant positive effect of light
(MANOVA; df = 3, 44; p < 0.0001) and larval treatment (MANOVA; df = 3,46; p = 0.0002), but no
significant interaction (MANOVA; df = 3,46; p = 0.0519) (Figure 4.7). These results indicate that
our light treatments were successful in the manipulation of algal biomass and that larval
presence generally reduced chlorophyll a (Figure 3.8). However, the effects of larvae on algal
biomass were most obvious in our shaded treatments (see Figure 3.8).

74

Principle component analysis of algal communities in the water column and on leaf
surfaces showed differences between treatments where larvae were present or absent (see
Figures 3.9 and 3.10). Our initial MANOVA on PCA scores for leaf-associated algal communities
was highly significant (MANOVA; df = 3,23; p < 0.0001). Subsequent ANOVA tests on each PC
(1,2,3) revealed significant effects of larval treatment on PC3 only (ANOVA; F = 11.8301; p <
0.0001). Individual tests on the loading values of each factor on PC3 showed significant impacts
of larvae on Cyanobacteria (Kruskal-Wallis = 19.353; df = 3; p = 0.0002) and an unidentified
Chlorophyte colony (Kruskal-Wallis = 8.403; df =3; p = 0.0384) (Figure 3.9). For water columnassociated algal communities, our MANOVA test on PCA scores was significant (MANOVA; df =
3,22; p = 0.0018), and subsequent ANOVAs showed significant effects of larval treatment on
PC2 only (ANOVA; F = 5.7284; p = 0.0047). Individual tests of loading values on PC2 factors
revealed significant effects of larval treatment on oval-shaped unidentified Chloropytes
(ANOVA; F = 6.379; p = 0.0028) (Figure 3.10). Please see Appendix III for photographs of our
unidentified Cyanobacteria, Chlorophyte colony, and oval Chlorophytes.
Our MANOVA on leaf and water bacterial and fungal DNA concentrations yielded
significant effects of light (MANOVA; df = 4,43; p < 0.0001) and larval (MANOVA, df = 12,135; p
< 0.0001) treatments, as well as a significant interaction effect (MANOVA, df = 12,135; p =
0.016). Subsequent ANOVA test results are displayed in Table 3.7. We observed strong feeding
effects on leaf surfaces for both bacteria and fungi in sunlit microcosms, but not in shaded
microcosms. In the water column, we observed strong feeding effects on bacteria under both
sunlit and shaded conditions, and on fungi under shaded conditions (see Figure 3.11).

75

0.008

J
T
JT
N

0.006

0.004

0.002

0.000

Sunlit

Microcosm Plastic-Associated Log-Chlorophyll a (µg/cm2)

A

Water Column Log-Chlorophyll a (µg/L)

Leaf-Associated Log-Chlorophyll a (µg/L)

0.010

Shaded

B

2.0

1.5

1.0

0.5

0.0

Sunlit

Shaded

C

0.025

0.020

0.015

0.010

0.005

0.000

Sunlit

Shaded

Figure 3.8. Mean chlorophyll a (± 1 SE) measured on leaf surfaces (A), in the water column (B)
and along the sides of each microcosm (C) in field microcosms during the estimated peak of
larval feeding activity (n = 7). J = A. j. japonicus (intraspecific), T = A. triseriatus (intra.), and JT =
A. j. japonicus + A. triseriatus

76

Figure 3.9. Principle component analyses on leaf-associated algal communities in field
microcosms showing differences in algal communities between different larval treatments.
Values are mean scores for each axis plus one SE.

77

Figure 3.10. Principle component analysis of water column algal communities in field
microcosms showing differences in the algal communities encountered between microcosms
with and without larvae. Values are mean scores for each axis plus one SE.

78

Table 3.7. ANOVA results from analysis of fungal and bacterial DNA concentrations (df =
1,3,47).
Parameter
Leaf Fungi

Water Fungi

Leaf Bacteria

Water Bacteria

Source
Light
Larvae
Light × Larvae
Light
Larvae
Light × Larvae
Light
Larvae
Light × Larvae
Light
Larvae
Light × Larvae

79

F
4.074
5.122
0.792
24.562
7.759
1.153
1.028
6.640
1.885
55.229
15.799
1.694

P
0.049
0.004
0.505
< 0.0001
< 0.0001
0.338
0.316
0.001
0.145
< 0.0001
< 0.0001
0.181

Bacterial DNA Concentration

0.5

0.4

A

J
T
JT
N

Water Bacterial DNA Conc. (ng/mL)

Leaf Bacterial DNA Conc. (ng/cm2)

0.6

0.3

0.2

0.1

0.0

B
4

3

2

1

0

Sunlit

Shaded

Sunlit

Shaded

Fungal DNA Concentration
0.10

C
0.06

0.04

0.02

Water Fungal DNA Conc. (ng/mL)

Leaf Fungal DNA Conc. (ng/cm2)

0.08

0.00

D
0.08

0.06

0.04

0.02

0.00

Sunlit

Shaded

Sunlit

Shaded

Figure 3.11. Mean (± 1 SE) concentration of leaf- and water-associated bacterial (A and B,
respectively) and fungal leaf and water (C and D, respectively) DNA in field microcosms (n = 7).
J = A. j. japonicus (intraspecific), T = A. triseriatus (intra.), JT = A. j. japonicus + A. triseriatus, N =
No Larvae.

80

Leaf Decomposition. We also measured the amount of leaf mass lost in each treatment
in order to quantify decomposition (Figure 3.12). We observed a significant positive effect of
light on the quantity of leaf mass lost by the close of the experiment (ANOVA; F = 24.298; p <
0.0001), but no effects of larval treatment (ANOVA; F = 1.812; p = 0.179), and no significant
interaction between light and larval treatment (ANOVA; F = 1.493; p = 0.239).

0.34

Leaf Mass Lost (g)

0.32

J
T
JT
N

0.30
0.28
0.26
0.24
0.22
0.20

Sunlit

Shaded

Figure 3.12. Mean total leaf mass lost per microcosm (±1 SE) in field microcosms (n=7). J = A. j.
japonicus (intraspecific), T = A. triseriatus (intra.), JT = A. j. japonicus + A. triseriatus, N = No
Larvae.

81

Figure 3.13. Water temperature, measured with data loggers, was similar in sunlit (red) and shaded (blue) microcosms during the
field experiment. (For interpretation of the references to color in this and all other features, the reader is referred to the electronic
version of this thesis.)

82

DISCUSSION
Primarily of concern as disease vectors, invasive mosquito species may also impact an
ecosystem through larval competition with native mosquito species (Juliano and Lounibos
2005). Examining the outcomes of competition between invasive and local species may help in
predicting potential effects of the invasion on existing disease cycles (Juliano and Lounibos
2005). Aedes j. japonicus is thought to be versatile in terms of its larval habitats (e.g. Joy and
Sullivan 2005, Bevins 2007), but it is unclear whether any specific environmental parameters
facilitate its establishment or success in interactions with native mosquito species.
We postulated that A. j. japonicus larval production would be enhanced in sunlit or
partially-sunlit containers because of the availability of algae as an additional food source
within these habitats. Further, we hypothesized that utilization of algal resources might explain
how this species has successfully integrated into container habitat communities in its expanded
range. Although algae have been shown to be a valuable food source for larvae of several
mosquito species (see Bond et al. 2005, Kaufman et al. 2006b) and elevated algal biomass levels
did increase production of A. j. japonicus in our studies, we found no evidence that algae
provided any distinct benefits to this invasive species. Increased algal biomass, either directly
or indirectly, was beneficial to the growth of all three mosquitoes tested, and outcomes of
competitive interactions with A. j. japonicus were not altered with high or low algal levels.
Larvae of all three mosquito species readily fed on algae in both experiments. We
observed a particularly strong reduction of chlorophyll a in the water column by C. pipiens
during our laboratory experiment. This effect corresponded with the higher survivorship
observed for C. pipiens in sunlit microcosms relative to shaded. Overall performance of C.
83

pipiens was poor in shaded microcosms where algal densities were low, indicating that limnetic
algal biomass may be a particularly important food source for this species. A. j. japonicus and
A. triseriatus affected chlorophyll a levels similarly in our experiments, both on leaf surfaces
and in the water column, though not to the same extent as C. pipiens. In our field experiment,
however, we observed a substantial effect of larval feeding by A. j. japonicus and A. triseriatus
on chlorophyll a levels in our shaded field microcosms. This was not apparent in our sunlit
treatment, indicating that larvae may have foraged more vigorously under low-resource
conditions (that would be more characteristic of our shaded microcosms) (Workman and
Walton 2003).
Though we did not observe a strong effect of larval presence on chlorophyll a levels in
sunlit microcosms, we did observe an effect of larval foraging on algal community composition
(see Figures 3.9 and 3.10). This is similar to but less-pronounced than the results of Gimnig et
al. (2002), who documented decreases in abundance of most algal groups in response to larval
presence. The change in algal community structure with larvae may reflect algal responses to
nutrient changes induced by larval activity (though evidence suggests that this was not the case
in Gimnig et al. 2002), or to the effects of ingestion/digestion (Elser and Goldman 1991).
Similar to chlorophyll a levels, there was no evidence that A. j. japonicus harvested particular
algal groups better than A. triseriatus, or changed algal community structure in a distinctive
way.
Sunlight and larval presence also affected bacteria and fungi in our microcosms.
Measures of water column bacterial productivity (leucine incorporation rates) in our laboratory
experiment revealed consistently higher production from sunlit microcosms. This corresponds
84

with similar results obtained by Espeland et al. (2001) and may be due to stimulation of
extracellular enzyme activity by algal cells (Rier et al. 2007). Algal cells are known to “leak”
labile organic carbon, and this tendency may provide bacteria with increased resources when
algae are present (e.g. Larsson and Hagström 1982). Our estimates of water column bacterial
DNA concentration, measured during our field experiment, also show generally higher
quantities in sunlit relative to shaded microcosms. Impacts of larval treatment on bacterial
DNA concentrations in our experiment confirm the widely-observed point that bacteria are
important food sources for mosquito larvae (Merritt et al. 1992, Kaufman et al. 2001), as we
observed a profound effect of larval feeding on water column bacteria under both sunlit and
shaded conditions, as well as leaf-associated bacteria under sunlit conditions. Estimates of
fungal DNA concentration in the water column showed similar strong feeding effects under
both sunlit and shaded conditions. Additionally, in the absence of larvae, fungal DNA
concentration measured in the water column was greater in sunlight than in shade. This does
not correspond with certain previous studies which have observed negative effects of light on
fungal growth (Newsham et al. 1997, Albariño et al. 2008). However, it is possible that our
primers, designed for prominent fungal groups, also amplified heterotrophic protist DNA. In
general, it is unclear how microbial DNA abundance translates into microbial biomass
(Christensen et al. 1993, Manerkar et al. 2008), and qPCR results are best used for relative
comparisons of similar microbial communities.
Because we observed feeding effects on both bacterial and fungal DNA concentration
on leaf surfaces in sunlit, but not shaded, microcosms, we postulate that larvae may increase
feeding on leaf surfaces in sunlit habitats due to a negative phototactic response (Christophers
85

1960). Thus, in sunlit containers leaf surfaces may be a more important food source than in
shaded containers. Alternatively, increased larval foraging for algae on leaf surfaces would also
impact surface-associated heterotrophs in the same biofilm. It is thought that larvae are
indiscriminate in their grazing activities, so it is somewhat surprising that there was no
pronounced reduction of fungi on leaf surfaces associated with larvae in the dark treatments.
Previous studies have indicated that submerged surfaces are subject to intense browsing by A.
triseriatus larvae (Walker and Merritt 1991, Kaufman et al. 2001, 2002). Recent measurements
using qPCR in our laboratory indicate fungal DNA is greatly reduced when larvae are present
(M. G. Kaufman, unpublished data). This may be related to methodological differences, in that
previous studies have defined surface microorganisms as those released via sonication for 12
minutes (e.g. Kaufman et al. 2001). In this study, rubbing surfaces followed by sonication likely
released a larger and different subset of leaf-associated microorganisms.
In general, feeding effects on microbial groups did not appear to differ between A. j.
japonicus and A. triseriatus, and there is no evidence in our study to suggest differential
utilization of these food resources by either species. However, we did not quantify all microbial
groups and qPCR measurements of abundance must be qualified. More detailed analyses of
heterotrophic protists, for example, might have revealed that the two mosquito species
harvested this food resource differently.
Our measurements of leaf decay, quantified as the relative amount of leaf mass lost,
revealed higher decomposition rates across all larval treatments in our shaded microcosms
compared to sunlit microcosms during both experiments. This is likely due to algal cells utilizing
nutrients (e.g. N) that are normally available for leaf decay organisms. Nitrogen has benen
86

shown to be important for leaf-associated fungi in similar larval studies (see Kaufman et al.
2006a) and algal sequestering of N could have inhibited fungal decay processes. Additionally,
leaf weights from sunlit microcosms may have been inflated by the presence of algae-rich
biofilms, since we did not remove these prior to drying and weighing the leaf material.
In terms of mosquito production, we observed the strongest effect of light treatment on
the overall proportion of emerged adults, which was consistently greater in sunlit relative to
shaded microcosms for all species combinations during both our laboratory and field
experiments. In general, sunlight also facilitated faster emergence of females – for A. j.
japonicus, female emergence times were shorter in sunlight relative to shade, and for A.
triseriatus and C. pipiens, we did not observe any females at all emerging from our shaded
treatments. Few females emerged from our experiments in general, probably reflecting the
low resource conditions. Most of the females that did emerge were A. j. japonicus, showing the
more rapid intrinsic development of this species.
Effects of interspecific competition with C. pipiens and A. triseriatus on A. j. japonicus
were minor and comparable to those of intraspecific competition. Aedes j. japonicus and A.
triseriatus performed similarly, however, A. j. japonicus females emerged quicker and in greater
numbers than A. triseriatus females, which may provide A. j. japonicus with a competitive
advantage. These results correspond with those of previous studies (Alto 2011, Hardstone and
Andreadis 2012) indicating that A. j. japonicus by and large is not a superior competitor over A.
triseriatus, even when under more naturalistic conditions as in our field study. This is puzzling
given that A. j. japonicus seems to be responsible for local displacement of A. triseriatus in
some areas (Andreadis and Wolfe 2010), and that asymmetry in resource competition is often
87

recognized to be a cause of species displacement (Juliano and Lounibos 2005). Additionally, we
did not observe any effects of light treatment and the resulting microbial dynamics on
competitive outcomes between A. j. japonicus and A. triseriatus.
The most striking effect of interspecific competition that we observed was the complete
lack of survival of C. pipiens when grown in microcosms with A. j. japonicus. This result could
have been a function of the relatively low amount of food resources in our lab study. Culex
pipiens is known to occur primarily in eutrophic habitats (Vinogradova 2000), thus, resource
conditions in our microcosms would not have been ideal for this species. However, we did
observe some individuals surviving and emerging from our intraspecific C. pipiens treatments;
only in competition with A. j. japonicus did we see such high mortality, indicating that A. j.
japonicus is a superior competitor over C. pipiens, at least in resource-poor habitats. This is
contrary to the results obtained by Hardstone and Andreadis (2012), who observed low levels
of interspecific competition between the two species; however, their study occurred at higher
resource conditions than ours. Other studies have indicated that these two species forage
similarly, using both filtering and browsing techniques (Yee et al. 2004, O’Donnell and
Armbruster 2007). Thus, C. pipiens and A. j. japonicus likely do compete for resources directly.
The higher reduction of limnetic chlorophyll a by C. pipiens, however, is suggestive of greater
time or efficiency in filtering activity. Further examination of the relationship between
competitive interactions and resource levels for these two species is necessary.
Overall survival rates of A. triseriatus and C. pipiens in our laboratory experiment were
positively affected by sunlight, however, A. j. japonicus survivorship was not affected by light
treatment in either our laboratory or field experiments. It is interesting to note that compared
88

to A. triseriatus and C. pipiens, A. j. japonicus survived equally well across all treatments in both
experiments, indicating that this species may be able to better tolerate low resource levels than
its competitors. This hypothesis is consistent with field observations of this species occurring in
a range of habitats spanning a wide variety of resource levels (Joy and Sullivan 2005, Bevins
2007), but contradictory to results obtained by Alto (2011), who observed poorer performance
of A. j. japonicus when compared to A. triseriatus at low resource levels. They suggest that
competitive outcomes between the two species vary under different environmental conditions
(also see Juliano 2009). To this effect, our study has shown that sunlight, and more directly
algal biomass, probably does not play a role in competition between these two species. We
postulate instead that versatility in terms of tolerance of different habitat conditions may be
partially responsible for the invasion success of A. j. japonicus. In general, it has been observed
that wide ranges of tolerance for habitat factors may facilitate invasiveness (Williamson and
Fitter 1996, Sanders et al. 2010), though this is not true of all invasive species (Kolar and Lodge
2001). By occupying sunlit or low-resource habitats that other species avoid (e.g. Joy and
Sullivan 2005), A. j. japonicus may also be filling an empty niche (Williamson 1996, Juliano and
Lounibos 2005).
From the above experiments, we conclude that sunlight and resulting algal growth may
provide a benefit to developing mosquito larvae by increasing emergence rates, but probably
does not affect outcomes of competition between A. j. japonicus and native species. It is not
apparent at this time whether the positive effects of algae on mosquito production are the
result of the availability of algae in particular, or consequent stimulation of heterotrophic
microbes, or whether they are simply due to the presence of greater amounts of food in
89

general. Further experimentation is necessary to partition this relationship. Access to algal
resources did not affect the mosquito species differentially, with the exception of C. pipiens.
Similarly, differential utilization of microbial resources was only observed in the increased
consumption of limnetic algae by C. pipiens. These results suggest that while algae are
probably not integral to the utilization of larval habitats by A. j. japonicus, the ability to exploit
sunlit habitats and harvest algal food resources still contributes to the invasion success of this
species as it interacts with native species that may be more reliant on autotrophic production in
container habitats.

90

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91

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96

CONCLUSIONS

97

The goals of my thesis were to determine the relative importance of algal growth to the
colonization of larval habitats and to the larval development and adult mosquito production of
the invasive mosquito species Aedes japonicus japonicus. We conducted three major
experiments to test the following hypotheses:
1. Aedes j. japonicus females will deposit more eggs in containers with high densities of
algae than in containers with low densities of algae.
2. Aedes j. japonicus larval development is enhanced in sunlit or partially-sunlit containers
because of the availability of algae as an additional food source within these habitats.
3. Access to algal resources will enhance performance of A. j. japonicus relative to
competing native species.
Our first experiment indicated that there was no overall relationship between algal
biomass and female oviposition; however, we detected a positive relationship between these
two variables at one of our three experimental sites (Toumey Woodlot). Algal densities at the
Toumey field site were significantly higher than those at our other two field sites, and we
conclude that algal density by itself does not play a role in oviposition preference of A. j.
japonicus females, but may stimulate increased oviposition activity when it is great enough to
alter overall organic matter content cues. As we noted copious oviposition activity by A. j.
japonicus in all of our containers, results from this experiment reinforce prior observations of
the catholic nature of this species in terms of the habitats it colonizes.
Results from additional lab and field microcosm experiments, in which A. j. japonicus
was reared with conspecifics or with A. triseriatus or Culex pipiens, indicated that algae do
98

enhance mosquito production by increasing the proportion of adults which emerge successfully
as well as shortening emergence times of females, lending support to our second hypothesis.
However, this was true for all three species and we also did not observe an overall effect of
algal density on competitive outcomes between A. j. japonicus and the other two species. Thus
our third hypothesis that algae affect larval competition was unsupported. There was no
apparent competitive asymmetry between A. j. japonicus and A. triseriatus when grown
together, except that A. j. japonicus developed faster and successfully produced a greater
number of adult females than A. triseriatus. The faster relative developmental rate of A. j.
japonicus appears to be intrinsic for the species, is apparently unrelated to food resources, and
is currently under investigation in our lab. In contrast, we observed striking competitive
asymmetry between A. j. japonicus and C. pipiens, in that no C. pipiens survived when placed in
microcosms with A. j. japonicus. The mechanisms driving this asymmetry are unclear, and may
be due to resource competition, unidentified parasitism, or intraguild predation.
Microbial communities were affected similarly by all three mosquito species, with the
exception that C. pipiens reduced densities of limnetic algae to a greater degree than the other
two species. Overall, larvae were responsible for decreasing all microbial groups measured and
for altering microbial community structure, indicating that larvae were actively feeding on
microorganisms in both the water column and on leaf surfaces. However, our measurements
did not detect any unique utilization of microbial groups by A. j. japonicus and therefore this
route to competitive advantage seems unlikely. Because sunlit treatments in our experiments
also increased bacterial production and bacterial DNA concentrations, a consequence of algal
activity may be to increase other microbial food resources in addition to providing algal
99

biomass for larval assimilation. It is unclear if algae or associated bacterial biomass are
primarily responsible for enhanced mosquito growth in sunlit treatments, but algae were
almost certainly contributing to adult mosquito production via their essential nutrient (e.g.
lipids) content.
Field surveys of algal density in containers harboring A. j. japonicus larvae indicated that
algae are often present but seldom reach densities which would likely affect oviposition
decisions. We postulate instead that the larval habitat promiscuity exhibited by this species, its
apparent high intrinsic development rate relative to native competitors, and its ability to
successfully produce adults under a range of larval resource conditions all contribute to its
success as an invader. Future studies should further address the role of this species in the
apparent displacement of native species, as well as re-evaluate the potential for the
competitive exclusion of C. pipiens in habitats containing both species.

100

APPENDIX I
RECORD OF DEPOSITION OF VOUCHER SPECIMENS

101

102

APPENDIX II
SURVEY OF ALGAL GROWTH IN CONTAINER HABITATS IN THE VICINITY OF
MICHIGAN STATE UNIVERSITY

103

The invasive mosquito species Aedes (Finlaya) japonicus japonicus (Theobald) is a
container-breeder and has been noted to occur in habitats with visible algae present (Andreadis
et al. 2001, Scott et al. 2001). We postulated that living in container habitats with algal
populations would benefit A. j. japonicus by providing its larvae with an additional food source.
We conducted a survey of container habitats in order to determine the relative amount and
types of algae that occur with A. j. japonicus in containers on Michigan State University’s
campus and in the surrounding area. We chose our containers based on prior knowledge of
mosquito colonization, though not all containers sampled had larvae in them on sampling
dates. The purpose of the following survey was not to determine what kinds of containers A. j.
japonicus occurred in, but rather to document the presence of algal communities therein. We
conducted the survey during two periods over the summer of 2011 – May 9 and 10, and July 12
and 14. See Table A2.1 for precise geographic coordinates and Figure A2.1 for a map of the
locations of our containers.

Figure A2.1. Locations of survey sites are marked with stars.

104

Table A2.1. Geographical coordinates of survey sites.
Latitude

Longitude

Hudson Woodland

42°41'53.91"N

84°28'32.79"W

Toumey Woodlot

42°42'11.37"N

84°27'54.49"W

MSU River Lab

42°43'45.45"N

84°29'50.67"W

Horse Trough

42°43'55.08"N

84°28'55.52"W

42°43'13.63"N

84°28'37.58"W

Evergreen Cemetery

42°42'30.60"N

84°30'42.26"W

Potter Park Zoo
Williamston
"Concrete Concepts"
Store

42°43'7.03"N

84°31'35.36"W

42°41'41.72"N

84°19'24.93"W

42°42'4.07"N

84°17'46.52"W

East Lansing

Toxicology Pond
Lansing

Summitt Cemetery

Sampling Protocol. Upon arriving at each container, we recorded the time of day,
ambient temperature, weather, and the relative amount of sunlight received. We then
removed 50 mL of water (or as much water as the container held if less than 50 mL) and stored
in a 50-mL centrifuge tube (Corning) on ice under dark conditions. We then used forceps to
remove approximately 3 decaying leaves from the container and took approximately two 1-inch
cores from each leaf using a cork borer. The number of leaf cores taken was always 6, unless
there was not enough leaf material in the container. Leaf cores were placed in plastic ziplock
bag to which a small amount of deionized water was added. The leaf cores were gently

105

massaged to remove biofilms. Leaf cores were then discarded and the water containing the
biofilm residues was rinsed into a plastic scintillation vial and placed on ice under dark
conditions before being returned to the laboratory. We also collected mosquito larvae from
each container (if present). Using a turkey baster, we removed aliquots of water and placed
them in a white cup, then used a small plastic pipette to collect mosquito larvae from the cup.
We collected the first 10 larvae we saw, or as many larvae as we could find if there were less
than 10. Larvae were placed in scintillation vials and stored on ice until they could be returned
to the laboratory.
Sample Processing. Water samples were spun at 3000 rpm for 5 minutes at 20ºC on an
Eppendorf centrifuge (Model 5810R, Eppendorf, Hamburg, Germany). The resulting
supernatant was poured into a new 50 mL centrifuge tube and spun again under the same
parameters. We then discarded the remaining supernatant. The pellets from each spin were
vortexed briefly and combined. We placed the sample in a clean 20 mL glass scintillation vial,
and brought the water volume up to 20 mL with deionized water. Five mL was promptly
removed and filtered through a glass fiber filter. The filter was immediately wrapped in foil and
frozen for later quantification of chlorophyll a. We preserved the remaining 15 mL of sample in
3% glutaraldehyde (un-buffered). Leaf biofilm samples were first brought to a volume of 20 mL
with deionized water. We removed 5 mL and filtered for chlorophyll a quantification. The
remaining 15 mL were preserved with 3% glutaraldehyde. All preserved samples were stored at
4ºC under dark conditions until processing.

106

All mosquitoes collected in the 3
2

nd

rd

th

or 4 instar were killed with 70% ethanol. First and

instars were reared at room temperature to the 3

rd

instar on an ad libitum diet of Tetramin

Tropical Flakes (Tetra®, Blacksburg, VA, USA) and yeast. Any pupae collected were allowed to
eclose before being killed by freezing. We counted and identified all individuals to species
under a dissecting microscope using Darsie and Ward (2005) (see Table A2.2).
We examined algal samples from a randomly-selected subset of leaf and water samples
from each date in a Palmer cell under a light microscope (Leica Microsystems GmbH, Wetzlar,
Germany). Each sample was thoroughly scanned (20 fields per sample) at 40X magnification.
The most commonly-observed taxa were photographed and can be found in Figure A2.2.
Chlorophyll a samples were kept frozen until they could be quantified fluorometrically. We
extracted the filters for 24 hours in 90% ethanol at 4ºC under dark conditions. We then
quantified each sample on a Turner fluorometer (Model TD-700, Turner Designs, Sunnyvale, CA,
USA). To correct for pheophytin, we acidified each sample with 0.1N HCl, then quantified again
(protocol adapted from Arar and Collins 1997).

107

Table A2.2. Mean (± 1 SE) limnetic and benthic chlorophyll a for each habitat type sampled. Containers without an error term had a
sample size of 1. Table also includes the mean percent of individuals from each species collected from each container type.
Asterisks (*) indicate that there were no larvae collected from the container(s).
Horse Trough

Aluminum
Boats

Tires

Tarps

Treeholes

Cement
Birdbaths

Cemetary
Vases

Plastic Basin
at Tox. Pond

May
(n=1)
Algae
Mean
Limnetic
Chlorophyll a
(µg/L)
Mean Benthic
Chlorophyll a
2
(µg/cm )
Mosquitoes
Percent
A. j. japonicus
Percent
A. triseriatus
Percent
C. restuans
Percent
C. pipiens

July
(n=1)

May
(n=2)

July
(n=2)

May
(n=5)

July
(n=4)

May
(n=2)

July
(n=0)

May
(n=6)

July
(n=6)

May
(n=5)

July
(n=0)

May
(n=1)

July
(n=2)

May
(n=1)

July
(n=1)

0.72

25.83

1.48±
0.95

1.10±
0.47

8.22±
5.87

0.87±
0.39

10.45
±9.8

n/a

0.22±
0.12

3.19±
1.38

n/a

0.54

2.61±
0.72

0.06

2.02

0.06

0.09

0.05±
0.005

0.19±
0.03

0.31±
0.19

0.02±
0.008

0.03±
0.02

n/a

0.01±
0.005

0.07±
0.019
0.001
±0.00
02

0.04

n/a

0.004

n/a

0.01

0.36

100

100

100

*

3

86.5

100

n/a

17

9

95

n/a

100

29

*

*

0

0

0

*

42

10

0

n/a

83

91

5

n/a

0

71

*

*

0

0

0

*

55

2.5

0

n/a

0

0

0

n/a

0

0

*

*

0

0

0

*

0

1

0

n/a

0

0

0

n/a

0

0

*

*

108

Figure A2.2. Algal specimens typical of our sampled containers viewed at 40X magnification.
(A) Filamentous green alga, (B) Chlamydomonadaceae, (C) unidentified coccoid Chlorophyte,
and (D) cyanobacterial filament. Calibration marks measure 10 µm. (For interpretation of the
references to color in this and all other figures, the reader is referred to the electronic version
of this thesis).

Results and Conclusions. We observed highly variable amounts of chlorophyll a across
our sampled containers, however, almost every container had at least a small amount of
chlorophyll a present, both on leaf surfaces and in the water column. Chlorophyll a quantities
can be found in Table 2. Based on the subset of samples examined, the majority of algae

109

occurring in container habitats of A. j. japonicus are small coccoid Chlorophytes. We also
observed the presence of filamentous Cyanobacteria, filamentous Chlorophytes, and
Chlamydomonadaceae (Figure A2.2). We recovered 4 mosquito species from our sampled
containers: A. j. japonicus, Aedes triseriatus, Culex restuans, and Culex pipiens (see Table A2.2).
The species which occurred most commonly with A. j. japonicus in our containers was A.
triseriatus.
General results from our survey indicate that algae are generally present in container
habitats supporting A. j. japonicus larvae. The majority of our chlorophyll a measurements
were quite low relative to those observed in experimental field containers from previous
experiments (A.R.L. unpublished data), thus, algal growth in container habitats probably does
not comprise a major food source for larvae of A. j. japonicus. However, we did notice algal
blooms in some containers sampled (e.g. the Horse Trough monument during July). As
mosquito larvae in general are indiscriminate feeders (see Merritt et al. 1992), it is likely that
during blooms when algal cells are abundant mosquito larvae consume algae as a large
proportion of their diet.

110

LITERATURE CITED

111

Literature Cited
Andreadis, T. G., J. F. Anderson, L. E. Munstermann, R. J. Wolfe and D. A. Florin. 2001.
Discovery, distribution, and abundance of the newly introduced mosquito Ochlerotatus
japonicus (Diptera : Culicidae) in Connecticut, USA. J. Med. Entomol. 38: 774-779.
Arar, E. J. and G. B. Collins. 1997. In vitro determination of chlorophyll a and pheophytin a in
marine and freshwater algae by fluorescence. EPA Method 445.0, Version 1.2. National
Exposure Research Laboratory Office of Research and Development, Cincinnati, OH.
Darsie, R. F. and R. A. Ward. 2005. Identification and geographical distribution of the
mosquitos of North America, north of Mexico. Gainsville, University Press of Florida.
Merritt, R. W., R. H. Dadd and E. D. Walker. 1992. Feeding behavior, natural food, and
nutritional relationships of larval mosquitoes. Annu. Rev. Entomol. 37: 349-376.
Scott, J. J., F. L. Carle and W. J. Crans. 2001. Ochlerotatus japonicus collected from natural
rockpools in New Jersey. J. Am. Mosq. Cont. Assoc. 17: 91-92.

112

APPENDIX III
PHOTOGRAPHS OF ALGAE FROM PCA FROM CHAPTER 3

113

Figure A3.1. Unidentified cyanobacterium from leaf-associated algal communities, field
microcosm experiment, see chapter 3. Calibration mark measures 10 µm. (For interpretation
of the references to color in this and all other figures, the reader is referred to the electronic
version of this thesis).

Figure A3.2. Unidentified chlorophyte colony from leaf-associated algal communities, field
microcosm experiment, see chapter 3. (For interpretation of the references to color in this and
all other figures, the reader is referred to the electronic version of this thesis).

114

Figure A3.3. Unidentified oval chlorophyte from water column algal communities, field
microcosm experiment, see chapter 3. (For interpretation of the references to color in this and
all other figures, the reader is referred to the electronic version of this thesis).

115