Download Effects of interspecific competition, predation, and their interaction

December, 2004
Journal of Vector Ecology
277
Effects of interspecific competition, predation, and their interaction on
survival and development time of immature Anopheles quadrimaculatus
Tiffany M. Knight1, Jonathan M. Chase1, Charles W. Goss2, and Jennifer J. Knight3
Department of Biology, Washington University, Box 1137, St. Louis, MO 63130, U.S.A.
Department of Biological Sciences, Florida International University, Miami, FL 33199, U.S.A.
3
Department of Psychology, Florida State University, Tallahassee, FL 32306, U.S.A.
1
2
Received 25 November 2003; Accepted 1 March 2004
ABSTRACT: We examined the effect of predation by the backswimmer (Notonecta undulata; Hemiptera:
Notonectidae), competition by zooplankton and snails, and both predation and competition on the survival and
development time of larval Anopheles quadrimaculatus mosquitoes in experimental mesocosms. We found that
both predation and interspecific competition greatly reduced the survivorship of larvae and the number of larvae
emerging into adulthood. Treatments with both predators and competitors had fewer larvae transitioning among
instars and into adulthood but not in an additive way. In addition, mosquito larvae in the presence of predators and
competitors took two days longer to emerge than where predators and competitions were absent. Our work provides
evidence that biotic interactions, such as predation and competition, can strongly regulate the number of mosquito
larvae by reducing the number of larvae that survive through instars and to emergence and by increasing the
generation time. Journal of Vector Ecology 29 (2): 277-284. 2004.
Keyword Index: Anopheles quadrimaculatus, interspecific competition, Notonecta undulata, predation, zooplankton,
survival, development time.
INTRODUCTION
Most models of temporal variation in population
densities of mosquitoes focus on abiotic conditions, such
as precipitation and temperature (Focks et al.1993,
Maguire et al. 1999, Shaman et al. 2002). However, biotic
interactions such as interspecific competition and
predation can often have a strong influence on both
container and wetland breeding mosquito populations.
A wide variety of predators consume larval mosquitoes,
including insects (Hemipterans—Streams 1992; Odonate
larvae—Sunahara et al 2002; Dipteran larvae—Sunahara
et al. 2002, Grill and Juliano 1996), copepods (Calliari
et al. 2003) and fish (Nelson and Keenan 1992). These
predators often reduce the survival of mosquito larvae
(Blaustein et al. 1995, Fincke et al. 1997) and in some
cases increase their development time (Grill and Juliano
1996). Further, female mosquitoes often avoid
ovipositing in pools that contain predators (Chesson
1984, Petranka and Fakhoury 1991, Blaustein et al. 1995,
Blaustein 1998, Angelon and Petranka 2002, Spencer et
al. 2002, Kiflawi et al. 2003a, b), and thus the presence
of predators may reduce the amount of aquatic habitat
available to mosquitoes.
While not as frequently studied as predation,
interspecific competition for limiting resources can also
be important and has been shown to have large effects
on mosquito larvae. Mosquitoes compete with tadpoles
(Blaustein and Margalit 1994, Mokany and Shine 2002),
other species of mosquitoes (Grill and Juliano 1996,
Juliano 1998) and cladocerans (Blaustein and Karban
1990). Competitors, like predators, have been shown to
decrease survival and increase the developmental time
of mosquito larvae (Blaustein and Margalit 1994, Juliano
1998, Mokany and Shine 2002). Finally, some evidence
exists that mosquitoes may preferentially oviposit in
habitats that have fewer competitors (Blaustein and
Kotler 1993, Kiflawi et al. 2003a,b, Mokany and Shine
2003). Thus, spatial variation in biotic interactions can
explain spatial variation in larval mosquito densities
(Chase and Knight 2003).
When eggs are laid in a given habitat, mosquito
competitors and predators can reduce mosquito densities
by several mechanisms, including direct mortality and/
or reduced growth rates in the presence of predators (due
to reduced activity) and competitors (due to reduced food
availability). Finally, there can be indirect effects on
mosquito larvae through direct effects of mosquito
predators on mosquito competitors. First, predators can
indirectly benefit mosquitoes by consuming competitors
278
Journal of Vector Ecology
if the competitor effect is stronger than the predator
effect. Second, the effects of predators on mosquitoes
can be amplified in the presence of competitors through
“apparent competition” (sensu Holt 1977). In this study,
we established wetland communities in experimental
mesocosms with interspecific competitors, predators, or
both competitors and predators. We then introduced a
known number of 1st instar mosquito larvae (Anopheles
quadrimaculatus) to determine the mechanisms of
predator and competitor effects on the survival and
growth rates of larval mosquitoes.
MATERIALS AND METHODS
Anopheles quadrimaculatus larvae are some of the
most common mosquitoes found in wetlands (stagnant
waters such as ponds, swamps, and marshes) throughout
eastern North America. As larvae, they consume a wide
variety of food types; the majority of their diet, however,
consists of microbes and algae consumed from the
surface of the water column (O’Malley 1992). As such,
they are likely to compete for resources with several cooccurring species. Two primary groups of potential
mosquito competitors include: (1) cladoceran
zooplankton that filter algae and microbes from the water
column, which likely limits their abundance in the water
surface where An. quadrimaculatus larvae feed and (2)
surface skimming herbivores (e.g., snails and tadpoles)
that graze algae and detritus from surfaces, including
the water surface. Furthermore, An. quadrimaculatus
are vulnerable to predators and are common prey items
for a wide variety of predatory insects and fish (O’Malley
1992).
In early August 2002, we established 32
experimental mesocosms at the Tyson Research Center,
the field station of Washington University in St. Louis
near Eureka, MO, U.S.A. (Jefferson County).
Mesocosms were 80 L “keg buckets” 0.5 m in diameter
x 1 m tall. We first filled each bucket with 25 g of dried
tree leaves (a mixture of several oak [Quercus spp.] and
hickory [Carya spp.] species) collected from the ground
of a nearby forest, and then added approximately 60 L
of well water (filling each mesocosm 80% full). Each
mesocosm was covered with 1 mm Fiberglas® mesh
“window-screening” in order to prevent colonization or
emigration of any organisms in this study; the mesh was
firmly attached to the lip of the mesocosms using metal
binder clips.
We assigned each mesocosm to one of four
treatments each replicated eight times: (1) Control (no
competitors or predators added), (2) Competitor
(competitors present), (3) Predator (predators present),
and (4) Both (competitors and predators present). Three
December, 2004
d after mesocosms were filled with water (to allow leaves
to sink to the bottom, and the water to settle), Competitor
and Both treatments received 2.5 L of water including a
concentrated inoculation of zooplankton from nearby
wetlands and six snails (Physella gyrina 3-6 mm in
length) five d prior to the introduction of mosquito larvae.
Zooplankton were collected and concentrated by pulling
a 30 mm mesh zooplankton net (10 cm diameter)
repeatedly through eight ponds on the Tyson Research
Center. Snails were collected from a single pond using a
1 mm mesh dip net. Common zooplankton in these ponds
included several cladocerans (e.g., Daphnia,
Ceriodaphnia, Chydorus, Simocephalus) and copepods
(e.g., Diacyclops, Diaptomus), all of which consume
algal and microbial food resources and potentially
compete with mosquitoes. One copepod species present
in this innocula, Mesocyclops sp., is known to consume
very small An. quadrimaculatus larvae (Hurlbut 1938)
but also consumes algae and detritus. Thus, this species
might better be placed as both a predator when larvae
are small and a competitor when they are large.
Nevertheless, Mesocyclops is very rare in samples from
the area, and from an experiment done in similar
mesocosms (Johnson and Chase, unpublished data).
Thus, Mesocyclops was not likely to play a large role in
the results of this experiment.
The addition of zooplankton to these treatments also
inadvertently added a variety of microbes, including
phytoplankton, bacteria, and protists. To control for these
additions in the Predator and Control treatments, we
introduced 2.5 L of water from the same sources but
filtered it through a 30 mm net to remove the
zooplankton. We chose six snails/mesocosm because this
density reflected the mean of natural snail densities from
several ponds in the area (N=6 ponds, snail density=5.6
+2.3/m2). The short life cycle of many of the zooplankton
species of one to several days per generation (Pennak
1989) makes it likely that the five-d establishment period
allowed them to grow to densities where they could have
a strong competitive effect on mosquito larvae upon their
introduction; their populations likely continued to grow
during the early phase of the experiment. Finally, in the
Predator and Both treatments, we introduced three adult
Notonecta undulata, which reflected the average number
of potential mosquito predators observed in several
nearby ponds (N=6 ponds, predator density=3.2+1.1/m2).
These predators were introduced several h before we
introduced mosquito larvae.
To collect An. quadrimaculatus for use in the
experiment, we filled several 60 L buckets with dried
leaves and water and set them out near a natural wetland.
Eggs of An. quadrimaculatus appeared in less than 24
h, and we collected 1st instar larvae that hatched in those
December, 2004
Journal of Vector Ecology
buckets on the day we set up the experiment. On August
16, 2002, we released 75 1st instar An. quadrimaculatus
larvae into each mesocosm.
There are several other Anopheles species that are
known to breed in Missouri that are very difficult to
distinguish (An. barberi, An. crucians, An. punctipennis,
and An. walkeri) (Darsie and Ward 1981), although An.
quadrimaculatus makes up more than 95% of adult
Anopheles collected at this site (J. M. Chase, unpublished
data). While it is impossible for us to have identified
every individual used in this experiment, we are confident
that nearly all of the individuals we used in this
experiment were An. quadrimaculatus for several
reasons. First, our habitats were not similar to those used
by some species; An. barberi is a treehole species
(O’Malley 1992) and is not likely to breed in our
mesocosms designed to mimic wetlands in the open,
while An. crucians prefers acidic waters (O’Malley 1992)
and the water at our field site (and in our mesocosms)
was basic (pH>8). Second, An. walkeri tends to prefer
shaded larval habitats, whereas our mesocosms were in
the open sun. Third, An. punctipennis tends to be more
common in the spring, whereas our work was done in
late summer when An. quadrimaculatus is more common
(O’Malley 1992). Finally, we randomly collected more
than 50 adults as they emerged from our control treatment
and identified every one as An. quadrimaculatus.
Nevertheless, the possibility remains that a very small
proportion of the individuals used in our experiment were
of other species, which would add variance to our results.
An. quadrimaculus is actually a complex of cryptic
subspecies, more than one of which may co-occur at our
site (Reinert et al. 1997). We made no attempts to
distinguish among these.
To census mosquitoes, an observer put his/her head
very close to the water surface and counted every
individual in the mesocosm. This counting was relatively
straightforward because these mosquito larvae rest on
the surface, which was clear of most debris (except for a
few floating leaves). We feel confident that we were able
to observe the majority of mosquito larvae because only
on very rare occasions did we find more mosquitoes in
one census than in the previous census; in such cases,
we assumed that the extra individual was present in the
previous census. We furthermore estimated the size
category each individual was in as a rough estimate of
their instar (which we calibrated using individuals
observed under a dissecting microscope). Although we
could not tell for certain the instar of every individual,
instar size classes were distinct enough that we felt we
were able to categorize the majority of individuals
appropriately. In addition to larval instars (I-IV), we
categorized individuals as pupa and adults. Adults were
279
counted by (1) lifting the screens covering the mesocosms
and counting them as they exited, and (2) counting their
discarded pupal exuviae. The experiment was terminated
on September 3, when all mosquito larvae had either
emerged or died.
Because we did not mark individuals, it was
impossible to know the exact fate of every individual from
each time step to the next. However, we were interested
in determining the number of individuals that transitioned
among stages and the approximate time they spent in each
stage. To do this, we counted the number of individuals
in each stage each day and calculated back to estimate
the fate of individuals from each stage (e.g., died, grew
to next instar, remained in current instar). We had to make
assumptions that all individuals developed at an average
rate. As such, we were certainly not able to track every
individual and its stage completely, but our assumptions
will not alter the primary results of interest, which were
the number of mosquitoes surviving to emergence and
the time it took them to do so.
We used one-way ANOVA to determine the effect of
treatment on the number of individuals surviving in each
life stage (instar I-IV, pupa, adult), and the probability of
surviving from one life stage to the next. When there was
a significant effect of treatment, we used Tukey’s HSD
to examine pairwise differences among treatments. We
used two-way ANOVA to determine the main effects of
predators and competitors and their interactive effects
on the number of emerged adults. To examine growth
rates for each mesocosm, we calculated a mean
development time as the number of days it took for larvae
to go from 1st instar to emerged adult. One-way ANOVA
was used to determine the effect of treatment on
development time and Tukey’s HSD to examine pairwise
differences among treatments. Mesocosms with fewer
than three individuals emerging were excluded from this
analysis. Because none of the mesocosms in the Both
treatment had >3 individuals emerge, this treatment was
excluded from this analysis.
RESULTS
We found a strong effect of treatment on the number
of larvae in each instar (Table 1, Figure 1): larvae (all
instars), pupa, and emerged adults were most numerous
in the control treatment. Similarly, the probability of
survival from one stage to the next was generally higher
for individuals in the control treatment, except for the
probability of surviving from 4th instar to pupa, which
was equivalent across treatments (Table 1). Two-way
ANOVA results demonstrated that the presence of
competitors (F 1,28=45.469, p<0.001) and predators
(F1,28=95.101, p<0.001) reduced the number of emerged
280
Journal of Vector Ecology
adults, and that there was an interaction between these
two factors (F1,28=35.891, p<0.001).
Larvae in the predator and competitor treatments
took longer to develop into adults than larvae in the
control treatment (ANOVA, F2,17=17.994, p<0.001)
(Figure 2). Larvae in the predator treatments took four
d longer than larvae in the control treatment (Tukey’s
HSD, p<0.05), and larvae in the competitor treatment
took seven d longer than larvae in the control treatment
(Tukey’s HSD, P<0.05). The difference between the
predator and competitor treatments was marginally
insignificant (Tukey’s HSD, p=0.08). We note that there
was a considerable amount of variation in the time to
emergence among individuals within treatments. Some
of this was likely due to the fact that male and female
mosquitoes have different development times (Lounibos
et al. 1996, Holzapfel and Bradshaw 2002);
unfortunately, we did not record sexes of emerging
individuals and thus can not discern this effect.
DISCUSSION
When both predators and competitors were present,
the number of An. quadrimaculatus and their survival
probabilities were sometimes lower than when either
December, 2004
predators or competitors were present in isolation. For
example, the number of adults that emerged was lower
in the Both treatment than in the Competitor treatment,
and survival from 3rd to 4th instar was lower in the Both
treatment than in either the Predator or Competitor
treatments (Table 1). However, the effect was not
additive, because some individual mosquito larvae still
survived in the Both treatments, whereas if we considered
the separate effects of predators and competitors, we
would expect the combined effect to drive the population
locally extinct. In addition, we found a significant
predator by competitor interaction of the number of
adults that emerged using two-way ANOVA. This result
could have been due to the fact that mosquito competitor
and predator effects may be density dependant. There
may have been a minimum level to which predators could
reduce mosquito densities if those remaining could find
sufficient refuge. Furthermore, there could be some
exclusive resources only available to a few larval
mosquitoes that were not available to their competitors.
Alternatively, there could have been some indirect
interaction between predators and competitors that
caused their effects to be non-additive. For example,
members of the genus Notonecta, the predators used in
our study, are known to consume zooplankton and can
Table 1. ANOVA results for number of larvae and survival for each treatment (N=none [control], C=competitor, P=predator, B=both).
Response variable
Sum of Squares
df
Mean Square
F-ratio
P
Tukey pairwise
comparisons
Number of larvae
2nd Instar
2428.750
3
809.583
6.833
<0.001
N>P,B
3rd Instar
5146.594
3
1715.53
16.97
<0.001
N>C,P,B
4th Instar
10371.34
3
3457.12
55.08
<0.001
N>C>P,B
Pupae
7520.594
3
2506.865
55.286
<0.001
N>C>P,B
Emerged
7640.594
3
2546.865
58.820
<0.001
N>C,P,B; C>B
1st to 2nd Instar
0.432
3
0.144
6.833
<0.001
N>P,B
2nd
0.709
3
0.236
5.541
0.004
N>P,B
3.315
3
1.105
67.275
<0.001
N>C>P>B
0.049
2
0.024
1.223
0.318
0.185
2
0.093
7.543
0.005
Probability of survival from
3rd
th
to
3rd
Instar
to
4th
Instar1
2
4 Instar to Pupa
3
Pupa to Emergence
N>C
1. Two predator replicates not included in this analysis because <4 larvae survived to 3rd instar
2. “Both” treatment could not be included because all but 1 replicate had <4 larvae survive to 4th instar. Three predator replicates not included in this analysis
because <4 larvae survived to 4th instar.
3. “Both” treatment could not be included because all of the replicates had <4 larvae survive to pupa. Four predator replicates not included in this analysis
because <4 larvae survived to pupa.
December, 2004
Journal of Vector Ecology
281
80
Number of mosquitoes
70
60
50
40
30
20
10
0
I
II
III
IV
P
Control
A
I
II
III
IV
P
A
Competitor
I
II
III
IV
Predator
P
A
I
II
III
IV
P
A
Both
Figure 1. The average (+ 1 standard error) number of mosquitoes (Anopheles quadrimaculatus) alive in each life
stage in each treatment. The life stages are abbreviated I,II,III,IV to denote larvae in instars 1-4, and P and A to
denote pupae and adult (individuals that emerged from these aquatic mesocosms). The treatments included (1)
Control, where no mosquito competitors and predators were present, (2) Competitor, where zooplankton and snails
(Physella gyrina) were present, (3) Predator, where the backswimmer Notonecta undulata was present, and (4)
Both, where both competitors and predators were present.
22
Development time (days)
20
18
16
14
12
10
8
Control Competitors
Predators
Figure 2. The average (+ 1 standard error) number of days it took for mosquito (Anopheles quadrimaculatus)
larvae to go from 1st instar to adult in each treatment. The treatments included (1) Control, where no mosquito
competitors and predators were present, (2) Competitor, where zooplankton and snails (Physa gyrina) were present,
(3) Predator, where the backswimmer Notonecta undulata was present.
282
Journal of Vector Ecology
sometimes have significant effects on zooplankton
abundance and species composition (e.g., Murdoch et
al. 1984); this in turn could reduce the overall effect of
competitors on mosquito larvae when predators are
present. Alternatively, the presence of competitors, which
are also food for the predators, could have caused
predators to have a lower per capita effect on mosquito
larvae due to predator switching (e.g., Chesson 1989).
Unfortunately, we did not count the density of
zooplankton throughout this experiment and thus cannot
discern the mechanisms behind the observed effect.
In addition to their effects on An. quadrimaculatus
density, both predators and competitors greatly increased
the time it took for larvae to grow to adulthood. This, in
turn, will cause a longer generation time, which will slow
the rate at which mosquito populations can increase.
Mechanisms that may have caused the increase in
development time in the presence of predation include
mosquito larvae acquiring fewer resources because they
spent more time motionless, hiding from predators (e.g.,
Juliano and Gravel 2002), or because they may have
spent more energy escaping from predators. The delayed
development time in the competitive environments could
be because competitors reduced food availability and
caused the larval mosquitoes to either intake less food
or spend more energy foraging for food. Both of these
mechanisms could result in larvae spending a longer time
in the larval stage in order to acquire enough resources
to transition among instars and emerge into the adult
stage. Thus, even though the effects of predators and
competitors on An. quadrimaculatus density were
similar, the fact that larvae took nearly three d longer to
develop in the competitor than in the predator treatment
suggests that the effects of competitors on mosquito
population dynamics may be greater in the long term
than the effects of predators.
Our experiments were all conducted at one density
of mosquito larvae, and those densities declined due to
the treatments over the course of the experiment. Thus,
we cannot discern the effects of intraspecific density from
the effects of competitors and predators. For example,
predators, by reducing intraspecific density, could have
allowed those mosquito larvae remaining to acquire more
resources per capita than mosquito larvae in communities
with higher intraspecific density. Alternatively, if
predators have a Type II (saturating) functional response,
the per capita mortality rate would be lower when
mosquito larvae are at a higher density. In addition, the
initial density used was relatively low compared to the
natural densities at which mosquito larvae can occur. At
higher densities, the effects of predation and competition
may be less than observed here, if the larvae experience
density dependent population control (intraspecific
December, 2004
competition). In order to continue to disentangle the
mechanisms by which predation, interspecific
competition, and intraspecific competition interact to
determine larval mosquito abundance and emergence,
future experiments will need to explicitly manipulate
intraspecific density. Finally, intraspecific competition,
as well as interspecific competition and predation, could
all influence the size of emerging adults (e.g. Mercer
1999). Because we did not measure this variable, we
cannot discern the role that it plays.
In nature, the presence of competitors and predators
should vary spatially and temporally. In a previous paper,
Chase and Knight (2003) showed that one important
reason for the spatial and temporal variation in mosquito
abundances (primarily An. quadrimaculatus) may have
been the interaction between wetland drying and the
abundance of mosquito competitors and predators. Many
mosquito larvae predators, such as fish and large insects,
can only live in wetlands that retain permanent standing
water for a significant proportion of the time (or are
connected to habitats that do have permanent water).
Thus, predators are often absent or extremely rare in
small ephemeral wetlands. Other predators are present
in these more ephemeral wetlands (Schneider and Frost
1996, Brendonck et al. 2002), but they are often less
efficient predators (J.M. Chase, unpublished data).
However, mosquito larvae competitors, such as
zooplankton, can live, and often attain high densities, in
these ephemeral wetlands (Chase and Knight 2003). This
is because many species of zooplankton have adapted to
deal with wetland drying. Specifically, they can lay
resting eggs that remain dormant while the wetland is
dry and then hatch when the wetland is filled with water
again. Other mosquito larvae competitors, such as snails,
can often survive wetland drying by burying themselves
in the soil and waiting for water to refill the wetland,
and tadpoles emerge from drying wetlands to the adult
(frog) stage and lay their eggs into the wetland when it
refills.
There has been a long-standing interest in explaining
continuing patterns of mosquito abundance and
mosquito-borne disease outbreaks. Most of this research
has focused on abiotic correlates, such as precipitation
and temperature (Lounibos et al. 1985). However, abiotic
factors, such as yearly precipitation, are typically poor
predictors of yearly trends in mosquito population size
(Aniedu 1992, Gleiser et al. 2000), as well as the
incidence of diseases that mosquitoes vector (Hay et al.
2000). In this paper, we have shown that biotic factors,
specifically the presence of mosquito competitors and
predators and their interaction, can strongly limit the
number of An. quadrimaculatus that emerge from a
wetland (see also Chase and Knight 2003) and increase
December, 2004
Journal of Vector Ecology
the time it takes for their development. Future studies
should continue to explore the patterns and mechanisms
underlying the interaction between biotic and abiotic
factors in limiting larval mosquito abundance.
Acknowledgments
We thank E. Marnocha and J. Kneitel for discussions
and field help, and D. Larson and the Tyson staff for
logistical support, and L. Blaustein and one anonymous
reviewer for useful suggestions. This research was
supported by Washington University and an NSF grant
(DEB 0108118) to J.M.C.
REFERENCES CITED
Angelon, K.A and J.W. Petranka. 2002. Chemicals of
predatory mosquitofish (Gambusia affinis) influence
selection of oviposition site by Culex mosquitoes.
J. Chem. Ecol. 28: 797-806.
Aniedu, I. 1992. A comparative study of the distribution
and seasonal abundance of malaria vectors in 3
ecologically distinct habitats in Baringo District,
Kenya. J. Appl. Entomol. 114: 268-274.
Blaustein, L. 1998. Influence of the predatory
backswimmer, Notonecta maculata, on invertebrate
community structure. Ecol. Entomol. 23: 246-252.
Blaustein, L. and R. Karban.1990. Indirect effects of the
mosquitofish Gambusia affinis on the mosquito
Culex tarsalis. Limnol. Oceanogr. 35: 767-771.
Blaustein, L. and B.P. Kotler 1993. Oviposition habitat
selection by the mosquito Culiseta longiareolata:
effects of conspecifics, food and green frog tadpoles.
Ecol. Entomol. 18: 104-108.
Blaustein, L. and J. Margalit 1994. Mosquito larvae
(Culiseta longiareolata) prey upon and compete
with toad tadpoles (Bufo viridis). J. Anim. Ecol. 63:
841-850.
Blaustein, L., B.P. Kotler, and D. Ward. 1995. Direct
and indirect effects of a predatory backswimmer
(Notonecta maculata) on community structure of
desert temporary pools. Ecol. Entomol. 20: 311-318.
Brendonck, L., E. Michels, L. DeMeester, and B.
Ribboch. 2002. Temporary pools are not ‘enemyfree’. Hydrobiologia 486: 147-159.
Calliari D, K. Sanz, M. Martinez, G. Cervetto, M. Gomez,
and C. Basso. 2003. Comparison of the predation
rate of freshwater cyclopoid copepod species on
larvae of the mosquito Culex pipiens. Med. Vet.
Entomol. 17: 339-342.
Chase, J.M. and T.M. Knight. 2003. Drought induced
mosquito outbreaks in wetlands. Ecol. Lett. 6: 10171024.
283
Chesson, J. 1984. Effect of Notonecta (Hemiptera:
Notonectidae) on mosquitoes (Diptera: Culicidae):
Predation or selective oviposition? Environ.
Entomol. 13: 531-538.
Chesson, J. 1989. The effect of alterative prey on the
functional response of Notonecta hoffmani. Ecology
70: 1227-1235.
Darsie, R.F. and R.A. Ward. 1981. Identification and
geographical distribution of the mosquitoes of North
America, north of Mexico. Mosq. Syst. (Suppl) 1:
1-313.
Fincke, O.M., S.P. Yanoviak, and R.D. Hanschu. 1997.
Predation by odonates depresses mosquito
abundance in water-filled tree holes in Panama.
Oecologia 112: 244-253.
Focks, D.A., D.G. Haile, E. Daniels, and G.A. Mount.
1993. Dynamics life table model for the Aedes
aegypti (Diptera, Culcidae) – Analysis of the
literature and model development. J. Med. Entomol.
30: 1003-1017.
Gleiser, R.M., D.E. Gorla, and G. Schelotto. 2000.
Population dynamics of Aedes albifasciatus
(Diptera: Culicidae) South of Mar Chiquita Lake,
Central Argentina. J. Med. Entomol. 37: 21-26.
Grill, C.P. and S.A. Juliano. 1996. Predicting species
interactions based on behaviour: Predation and
competition in container-dwelling mosquitoes. J.
Anim. Ecol. 5: 63-76.
Hay, S.I. M. F. Myers, D.S. Burke, D.W. Vaughn, T. Endy,
N. Ananda, G.D. Shanks, R. W. Snow, and D.J.
Rogers. 2000. Etiology of interepidemic periods of
mosquito-borne disease. Proc. Natl. Acad. Sci.
U.S.A. 97: 9335-9339.
Holt, R.D. 1977. Predation, apparent competition, and
the structure of prey communities. Theor. Popul.
Biol. 12: 197-229.
Holzapfel, C.M. and W.E. Bradshaw. 2002. Protandry:
The relationship between emergence time and male
fitness in the pitcher-plant mosquito. Ecology 83:
607-611.
Hurlbut, H.S. 1938. Copepod observed preying on
Anopheles quadrimaculatus Say. J. Parasitol. 24:
281.
Juliano, S.A. 1998. Species introduction and replacement
among mosquitoes: Interspecific resource
competition or apparent competition? Ecology 79:
255-268.
Juliano S.A. and M. E. Gravel. 2002. Predation and the
evolution of prey behavior: an experiment with treehole mosquitoes. Behav. Ecol. 13: 301-311.
Kiflawi, M., L. Blaustein, and M. Mangel. 2003a.
Predation-dependant oviposition habitat selection
by the mosquito Culiseta longiareolata: a test of
284
Journal of Vector Ecology
competing hypotheses. Ecol. Lett. 6: 35-40.
Kiflawi, M., L. Blaustein, and M. Mangel. 2003b.
Oviposition habitat selection by the mosquito
Culiseta longiareolata in response to risk of
predation and conspecific larval density. Ecol.
Entomol. 28: 168-173.
Lounibos, L.P., J.R. Rey, and J.H. Frank, eds. 1985.
Ecology of mosquitoes: Proceedings of a workshop.
Florida Medical Entomology Laboratory, Vero
Beach, FL. 579 pp.
Lounibos, L.P., R.L. Escher, D. Duzak, and E.A. Martin.
1996. Body size, sexual receptivity and larval
cannibalism in relation to protandry among
Toxorhynchites mosquitoes. Oikos 77: 309-316.
Maguire, M., C. Skelly, P. Weinstein, and J. Moloney.
1999. Simulation modelling of Aedes aegypti
prevalence, an environmental hazard surveillance
tool for the control of dengue epidemics. Int. J.
Envir. Hlth. Res. 9: 253-259.
Mercer, D.R. 1999. Effects of larval density on the size
of Aedes polynesiensis adults (Diptera : Culicidae).
J. Med. Entomol. 36: 702-708.
Mokany, A. and R. Shine. 2002. Competition between
tadpoles and mosquitoes: the effects of larval density
and tadpole size. Aust. J. Zool. 50: 549-563.
Mokany, A. and R. Shine. 2003. Oviposition site
selection by mosquitoes is affected by cues from
conspecific larvae and anuran tadpoles. Aust. Ecol.
28: 33-37.
Murdoch, W.W., M.A. Scott, and P. Ebsworth. 1984.
Effects of the general predator, Notonecta
(Hemiptera) upon a freshwater community. J. Anim.
Ecol. 53: 791-808.
Nelson, S.M. and L.C. Keenan. 1992. Use of an
indigenous fish species, Fundulus zebrinus, in a
mosquito abatement program: A field comparison
with the mosquitofish, Gambusia affinis. J. Am.
December, 2004
Mosq. Contr. Assoc. 8: 301-304.
O’Malley, C.M. 1992. The biology of Anopheles
quadrimaculatus (Say). Proceedings of the SeventyNinth Annual Meeting of the New Jersey Mosquito
Control Association, Inc., pp 136-144.
Pennak, R.W. 1989. Fresh-water invertebrates of the
United States, protozoa to mollusca. John Wiley and
Sons, Inc., NY.
Petranka, J.W. and K. Fakhoury. 1991. Evidence of
chemically-mediated avoidance response of
ovipositing insects to blue-gills and green frog
tadpoles. Copeia 1991: 234-239.
Reinert, J.R., P.E. Kaiser, and J.A. Seawright. 1997.
Analyses of the Anopheles quadrimaculatus
complex of sibling species (Diptera: Culicidae)
using morphological, cytological, molecular,
genetic, biochemical, and ecological techniques in
an intergrated approach. J. Am. Mosq. Contr. Assoc.
13 (Suppl.): 1-102.
Schneider, D. W. and T. M. Frost. 1996. Habitat duration
and community structure in temporary ponds. J. N.
Am. Benth. Soc. 15: 64-86.
Shaman, J., M. Stieglitz, C. Stark, S. LeBlancq, and M.
Cane. 2002. Using a dynamic hydrology model to
predict mosquito abundances in flood and swamp
water. Emerg. Infect. Dis. 8: 6-13.
Spencer, M., L. Blaustein, and J.E. Cohen. 2002.
Oviposition habitat selection by mosquitoes
(Culiseta longiareolata) and consequences for
population size. Ecology 83: 669-679.
Streams, F.A. 1992. Intrageneric predation by Notonecta
(Hemiptera, Notonectidae) in the laboratory and in
nature. Ann. Entomol. Soc. Am. 85: 265-273.
Sunahara, T., K. Ishizaka, and M. Mogi. 2002. Habitat
size: a factor determining the opportunity for
encounters between mosquito larvae and aquatic
predators. J. Vector Ecol. 27: 8-20.