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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. 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