The Potential of Metarhizium anisopliae Blastospores to Control Aedes aegypti Larvae in the Field

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Abstract

Entomopathogenic fungi are promising as an environmentally benign alternative to chemical pesticides for mosquito control. The current study investigated the virulence of Metarhizium anisopliae blastospores against Aedes aegypti under both laboratory and field conditions. Virulence bioassays of conidia and blastospores were conducted in the laboratory, while field simulation bioassays were conducted under two conditions: totally shaded (TS) or partially shaded (PS). In the first bioassay (zero h), the larvae were added to the cups shortly after the preparation of the blastospores, and in the subsequent assays, larvae were added to the cups 3, 6, 9, and 12 days later. The survival of the larvae exposed to blastospores in the laboratory was zero on day two, as was the case for the larvae exposed to conidia on the sixth day. Under TS conditions, zero survival was seen on the third day of the bioassay. Under PS conditions, low survival rates were recorded on day 7. For the persistence bioassay under PS conditions, low survival rates were also observed. Metarhizium anisopliae blastospores were more virulent to Ae. aegypti larvae than conidia in the laboratory. Blastospores remained virulent under field simulation conditions. However, virulence rapidly declined from the third day of field bioassays. Formulating blastospores in vegetable oil could protect these propagules when applied under adverse conditions. This is the first time that blastospores have been tested against mosquito larvae under simulated field conditions, and the current study could be the basis for the development of a new biological control agent.

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Fungal pathogen reduces potential for malaria transmission.

Andrew Read, Nina Jenkins, Tracey Turner … (2005)

Using a rodent malaria model, we found that exposure to surfaces treated with fungal entomopathogens following an infectious blood meal reduced the number of mosquitoes able to transmit malaria by a factor of about 80. Fungal infection, achieved through contact with both solid surfaces and netting for durations well within the typical post-feed resting periods, was sufficient to cause >90% mortality. Daily mortality rates escalated dramatically around the time of sporozoite maturation, and infected mosquitoes showed reduced propensity to blood feed. Residual sprays of fungal biopesticides might replace or supplement chemical insecticides for malaria control, particularly in areas of high insecticide resistance.

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Mosquito-Borne Human Viral Diseases: Why Aedes aegypti ?

Jeffrey R. Powell (2018)

Abstract. Although numerous viruses are transmitted by mosquitoes, four have caused the most human suffering over the centuries and continuing today. These are the viruses causing yellow fever, dengue, chikungunya, and Zika fevers. Africa is clearly the ancestral home of yellow fever, chikungunya, and Zika viruses and likely the dengue virus. Several species of mosquitoes, primarily in the genus Aedes, have been transmitting these viruses and their direct ancestors among African primates for millennia allowing for coadaptation among viruses, mosquitoes, and primates. One African primate (humans) and one African Aedes mosquito (Aedes aegypti) have escaped Africa and spread around the world. Thus it is not surprising that this native African mosquito is the most efficient vector of these native African viruses to this native African primate. This makes it likely that when the next disease-causing virus comes out of Africa, Ae. aegypti will be the major vector to humans.

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Oviposition Site Selection by the Dengue Vector Aedes aegypti and Its Implications for Dengue Control

Jacklyn Wong, Steven Stoddard, Helvio Astete … (2011)

Introduction Dengue viruses are transmitted to humans primarily by the mosquito Aedes aegypti and represent an increasing public health concern in tropical and subtropical regions worldwide. Because no vaccine or antiviral therapy is commercially available, controlling the mosquito vector is the only current means to prevent dengue outbreaks [1]. Contemporary control campaigns, rather than attempting to eradicate Ae. aegypti, aim to suppress mosquito populations below a threshold density at which they no longer support viral amplification [2]. Controlling adult mosquitoes is made challenging by the behavior of domestic Ae. aegypti. Adult Ae. aegypti rest inside homes, typically on clothing, curtains, bedspreads, and furniture, items that cannot be sprayed with residual insecticides [3]. Aerosol space sprays consist of small airborne droplets of insecticide designed to kill adult mosquitoes on contact, but difficulty in reaching indoor adult resting sites can limit their efficacy [4]. Even when space sprays are effective in reducing adult populations, effects are transient due to the continuing emergence of new adults or immigration from untreated areas [3], [5]. Insecticide-treated materials (curtains, water container covers, and bednets) have shown promise in reducing Ae. aegypti populations [6], [7], but the impact of these reductions on dengue transmission has not been determined. Currently, the World Health Organization recommends directing routine Ae. aegypti control toward the immature stages [2]. Ae. aegypti females lay eggs singly just above the water line, often in man-made containers located in the home or yard (buckets, drums, tires, and vases, etc.) [8]–[10]. Eggs hatch when inundated, and larvae develop by filter feeding and browsing for microorganisms and organic detritus [11], [12]. Control approaches such as container removal (source reduction) and larvicide application aim to reduce the number of new emerging adults in the population [2]. Traditional models of Ae. aegypti assume that population dynamics are regulated predominantly by density-dependent competition for food during early larval stages and little affected by oviposition rates [13], [14]. Based on these models, some researchers have assumed that all containers suitable for larval development receive an excess of eggs, thereby leading all larvae to experience density-dependent competition [15]. This is the rationale behind targeted source reduction (a WHO-recommended control strategy) and the expectation that eliminating containers that, for example, produce 75% of adults will lead to a proportionate decrease in the overall adult population [15]–[17]. Much remains unclear, however, about the factors regulating Ae. aegypti adult production and how reducing, but not eliminating, containers will ultimately affect adult abundance. In some mosquito species, female choice in oviposition site is adaptive and can influence population distribution and dynamics [18], [19]. Females can enhance survival and development of their offspring by selecting egg-laying sites that reduce exposure to predators and competitors [19], [20], or increase access to food [21], [22]. In general, understanding insect egg-laying decisions may provide additional insight into the factors affecting population regulation and aid in predicting how populations will respond to control measures [23]. Oviposition preferences by Ae. aegypti have been studied in the laboratory [24]–[31], but to a lesser extent in the field [32]–[36]. Research has typically involved varying one or two oviposition site factors at a time and observing the number of eggs laid in response (reviewed in [24]). Such studies reveal the types of abiotic and biotic stimuli potentially affecting oviposition, but yield limited information on the relative importance of these stimuli in nature [24], [37]. The goals of our study were to test whether free-ranging Ae. aegypti females make active choices regarding where they oviposit and to identify factors influencing oviposition. Although selective oviposition has been demonstrated using small oviposition traps in the field [32]–[35] or water-storage containers in an enclosure [36], we examined for the first time females' oviposition choices among naturally-occurring containers in homes throughout a large, dengue-endemic city. We also investigated the consequences of oviposition site selection for offspring fitness by testing whether females choose sites to maximize the amount of food available for their progeny. Food availability is known to affect components of mosquito fitness such as offspring survival, development time, and adult size [38]. Lastly, we considered the implications of selective oviposition behavior for Ae. aegypti population regulation and the success of targeted larval control strategies. Materials and Methods Study location Our study was conducted in Iquitos (73.2°W, 3.7°S, 120 m above sea level), a city of approximately 380,000 people located in the Amazon Basin, Department of Loreto, Northeastern Peru [10], [39]–[41]. Rain falls during all months of the year and average temperature and relative humidity are fairly consistent [42]. During our study period from July 2007 to August 2009, mean monthly temperature ranged from 24.8°C (±1.1 SD) in June 2008 to 26.5°C (±1.1 SD) in December 2008. Average relative humidity ranged from 80.2% (±4.1 SD) in August 2007 to 86.2% (±4.4 SD) in April 2009. More detailed climate data for the years 2007 to 2009 are given in the Supporting Information (Table S1). In response to the unreliable municipal water supply, Iquitos residents store water in containers [40]. Household containers are filled in three primary ways: 1) from spigots in the home or neighborhood (manually filled), 2) intentionally placed outside to collect rain water (rain-filled), and 3) filled with rain water as a result of being untended outside (unmanaged). Method of filling is correlated with the frequency of water turnover and amount of organic detritus present in containers, with manually filled containers kept the cleanest and unmanaged containers collecting the most organic material. Containers in Iquitos generally lack predators of larval Ae. aegypti, such as copepods or fish (ACM and JW, unpublished data), but do occasionally contain immature Culex which may act as competitors [10]. Ae. aegypti are reproductively active all year in Iquitos. Of the roughly 290,000 containers examined by Morrison et al. [10], 7.3% contained Ae. aegypti larvae and/or pupae. Observational study Consent process The households included in this study were identified through three ongoing, longitudinal cohort studies on dengue transmission dynamics approved by the University of California, Davis (Protocol #2006.14381, 2006.14405, 2007.15244) and Naval Medical Research Center Detachment (Protocol #NMRCD 2007.001, NMRC 2005.0009, NMRCD2007.007) Institutional Review Boards (IRBs). As described in detail by Morrison et al. [43], Ae. aegypti abundance surveys were conducted in private homes by two-person teams that administered a brief questionnaire to residents, counted the number of water-holding containers present on the property, inspected containers for immature Ae. aegypti, and collected adult mosquitoes using backpack aspirators. Entomological surveys required a verbal informed consent process in which the survey procedures were explained to residents and if they consented, the survey team was allowed into the household. Both IRBs approved verbal consent without written documentation because the survey form would indicate consent of the residents. Our oviposition study was approved by the local ministry of health (Dirección Regional de Salud -Loreto). The Naval Medical Research Center IRB determined that our study (Project #: PJT-NMRCD.032) did not meet the definition of human subject research. Survey procedures We conducted a large-scale survey to examine female oviposition choices among naturally-occurring containers in Iquitos homes. For nine weeks during July to September 2007 (collection period 1), seven weeks during May to July 2008 (collection period 3), and six weeks during October to December 2008 (collection period 4), we closely observed the number of Ae. aegypti eggs laid in containers within a subset of surveyed houses. Collection period 2 is described later. Each week, 3 to 6 houses having at least one Ae. aegypti-positive container were selected to be included in this study. For each of those houses, we visited 2 to 3 additional houses on the same block (matched in time and space) that had containers but no larvae, such that 9 to 18 total houses were visited per week. All surveyed houses, along with their associated entomological data, were geocoded using a geographic information system previously developed for Iquitos [39]. In each selected house, 2 to 4 inspectors examined the entire property (indoors and outdoors) for water-filled containers and used strips of brown paper towel to line the inside of containers (limited by homeowner permission) at the water line to collect eggs. The following characteristics were recorded on the first day: container size (circumference, capacity, and water volume), location and sun exposure, lid presence, fill method, insecticide treatment, conspecific larvae (abundance and estimated mean density), presence of conspecific pupae, and presence of immature Culex (Table 1). Insecticide treatment (temephos or pyriproxyfen) was scored depending on whether an insecticide sachet was present in containers; we did not determine how long sachets had been in containers or whether insecticidal activity was still active. The abundance of larval Ae. aegypti and the presence of larval Culex were noted by visual inspection without removing larvae. Larval Ae. aegypti estimates per container were categorized as: none, 1 to10, 11 to 50, 51 to 100, or >100 larvae. Estimated mean density of Ae. aegypti larvae was calculated by dividing the midpoint of the larval abundance category (or 200 in the case of >100 larvae) by water volume. Any pupae occurring in containers were collected daily and brought to the field laboratory to be counted and the emerging adults identified as either Ae. aegypti or Culex spp. If third instar Ae. aegypti larvae (determined by size and morphology) were present on the first day, up to 25 third instars were removed per container for starvation bioassays (described below) to assess food availability in containers [38], [44]. Otherwise, mosquito larvae were left undisturbed. 10.1371/journal.pntd.0001015.t001 Table 1 Container characteristics recorded during oviposition survey in Iquitos, Peru, and regression parameters for oviposition models. Variable Levels No. containers Median (range) Circumference Continuous 126 cm (10; 540) (Circumference)2 Continuous 15,791 cm2 (100; 291,600) Location and sun exposure Indoor (enclosed by roof and at least 3 walls) 134 Outdoor shade (exposed to sunlight 100 larvae, retained during survey 45 >100 larvae, removed on day 1 17 Ae.aegypti pupae Absent 454 Present 137 Immature Culex Absent 560 Present 31 Collection period 1 - July to September 2007 (9 weeks) 222 2 - January to May 2008 (14 weeks) 202 3 - May to July 2008 (7 weeks) 93 4 - October to December 2008 (6 weeks) 74 Paper strips were checked daily for three consecutive days to collect a representative sample of eggs laid within each house. Collections were conducted between 09:00 to 12:00 h to minimize disturbance of ovipositing females [45]. If eggs were present, new paper lining was exchanged. Papers with eggs were brought to the field laboratory to count eggs under a dissecting microscope at 20× magnification. Subsamples of collected eggs were hatched once a week to confirm their identity as Ae. aegypti. To prevent production of adult mosquitoes in sampled houses, containers with larvae were overturned or treated with pyriproxyfen at the conclusion of the 3-day survey. During 14 weeks from January to May 2008 (collection period 2), we surveyed containers following the above procedures, with the exception that all larvae and pupae were removed using a net and/or turkey baster on the first day. Therefore, no immature mosquitoes were present in containers when females oviposited on the following three days, but the water was “conditioned” by the previous presence of immatures. All larvae were taken to the field laboratory, where they were enumerated to genus and instar. Up to 25 third instar Ae. aegypti per container were used for starvation bioassays as described below. Data analysis Regression analyses were conducted using R version 2.8.1 [46]. To check for spatial autocorrelation among containers surveyed in the same week as a potential confounder, we estimated Moran's I for egg counts using a Euclidean distance matrix with the APE package within R [47]. Because no spatial structure was evident, subsequent analyses did not take spatial coordinates into account. We attempted to include the density of adult female Ae. aegypti as a predictor variable in our models, but collections were too sparse (mean = 0.14±0.52 SD females per house) for meaningful analyses. Instead, using a separate chi square test, we examined whether the presence of Ae. aegypti larvae was independent from capture of adult females during the abundance survey. To identify variables that best predicted whether or not female Ae. aegypti laid eggs in a container, a logistic regression model was fitted to our data (1 = container received eggs at least once during three days of observation, 0 = container received no eggs). Categories of Ae. aegypti larval abundance were further divided depending on whether larvae were retained during the survey or removed from containers on the first day. Collection period was included to control for time. The three measures of container size were collinear (circumference-capacity, Spearman's ρ = 0.86; circumference-water volume, Spearman's ρ = 0.65, capacity-water volume, Spearman's ρ = 0.85). Because the amount of space available for oviposition is determined by container circumference, we included circumference rather than capacity or water volume in our model. Larval abundance and estimated mean larval density also were collinear (Spearman's ρ = 0.92). Larval abundance was used because it provided a better model fit to the data. Starting with a saturated model including all variables listed in Table 1, we employed a log-likelihood test to eliminate, stepwise, the non-significant variable with the greatest χ2 p-value (2× log-likelihood of current model–2× log-likelihood of previous model ∼χ2, df = 1, p>0.10). If the final model included a variable with more than two levels, Tukey's multiple comparisons were applied using the MULTCOMP package [48] to identify differences in level effects. Only containers receiving eggs were included in the analysis to identify variables influencing the number of eggs laid in containers. Negative binomial regression was performed using the MASS package [49]. Our response variable was the mean number of eggs laid per container per day, rounded to the nearest integer. To more closely examine the association between egg abundance and container size, we included both container circumference and (circumference)2 as predictor variables in the model. As with the logistic regression model, containers were classified according to larval abundance and whether or not larvae were removed on the first day, and to collection period to control for time. Model selection was based on the log-likelihood test. To confirm that model assumptions were met, deviance residuals were plotted against: (1) fitted values, (2) each explanatory variable included in the model, (3) each explanatory variable eliminated from the model, (4) survey date, and (5) spatial coordinates [50]. Starvation bioassays We measured larval resistance to starvation (RS, number of days larvae survive without food) as an indirect measure of per capita food availability in containers [38]. In general, mosquito larvae that consume more food are able to store more energy reserves and resist starvation longer [13], [44]. During the above-described survey of Iquitos containers, 5 to 25 third instar larvae were removed from containers in the field and transferred to individual plastic cups (5 cm diameter×6 cm height) filled to 2/3 capacity with bottled drinking water. Third instars were used for bioassays because fourth instar Ae. aegypti frequently pupate when starved [44]. Cups were placed indoors in our field laboratory, where larvae were exposed to natural light and temperature. Water was changed every two days to prevent accumulation of waste and microbial growth [51]. Time to death (in days) was recorded for each larva. Because starvation times were not distributed symmetrically for larvae from each container, the median larval RS was used as the measure of central tendency for the data for each container. Spearman rank correlation was used to identify any association between larval RS and egg density (mean eggs laid per day/circumference). Data were stratified according to whether or not all larvae had been removed from containers on the first survey day. To account for potential effects of larval abundance and container size, data also were stratified by larval abundance (≤50 larvae vs. >50 larvae) and container capacity (≤20 L vs. >20 L). Experimental study For 12 weeks during June to August 2009, we carried out an experimental study manipulating both the presence of conspecific larvae and accumulation of organic material in containers and recorded oviposition by wild females. This experiment was replicated in three central Iquitos residences, the courtyard of our field laboratory and in the yards of two other houses selected based on the consistent presence of Ae. aegypti and homeowner willingness to participate. At each residence, three identical 6-liter blue plastic buckets (20 cm diameter×23 cm height) were placed close to one another (0.5 m apart) to minimize differences in container position. Hourly at each house, ambient temperature and relative humidity were recorded using a Hobo® ProV2 data logger (U23-001; Onset Computer Corporation, Pocasset, MA) and water temperature was recorded in one container per house using a Hobo® Pendant logger (UA-002-64). We created three container treatments: A (unmanaged, with larvae), B (unmanaged, no larvae), and C (manually filled, no larvae). Unmanaged containers (A and B) were filled with four liters of tap water and allowed to accumulate organic debris for 12 weeks, whereas manually filled containers (C) were cleaned and refilled with new tap water every other day. Fifty first instar Ae. aegypti larvae were introduced into treatment A containers every two weeks starting on the first day. Oviposition was monitored by lining the inside of buckets with strips of brown paper towel to collect eggs. Every second day, papers were exchanged and the number of eggs counted as described above. On egg collection days, we also temporarily removed larvae from treatment A containers to determine their developmental stage and count them. Larvae were then returned to the container from which they originated. To estimate the accumulation of organic detritus and bacterial growth in unmanaged containers, a thoroughly mixed water sample was measured for cloudiness using a turbidity tube [52] and dissolved oxygen content using an Ecological Test Kit (Rickly Hydrological Company, Columbus, OH). Water samples were returned to containers after testing. In all containers, tap water was added every few days to replace water lost to evaporation. Any pupae were removed to prevent emergence of adult Ae. aegypti. Data analysis Due to repetitive sampling, effects of treatment (A, B, or C), house, and week on the number of eggs laid per week ( transformed) were analyzed by repeated measures analysis of variance (RM ANOVA). RM ANOVA was also used to examine effects of treatment, week, and house on water turbidity ( transformed) and dissolved oxygen content (χ3 transformed). RM ANOVAs were carried out using PROC MIXED in SAS version 9.2 [53] and transformations were performed to meet ANOVA assumptions. Results Observational study We monitored oviposition in 591 containers in 448 households across Iquitos. Ae. aegypti eggs were deposited in 51.8% of surveyed containers (306 of 591). Egg counts per container per day were strongly skewed, with the majority of containers receiving 0 to 50 eggs (median = 2, mean = 41), and a few containers receiving hundreds of eggs (Figure 1). All mosquitoes reared from collected eggs were Ae. aegypti, which we found to be the only Aedes species present in domestic containers throughout Iquitos. The presence of Ae. aegypti larvae in households was independent from whether or not adult females were caught during entomological surveys (χ2 = 1.897, df = 1, p = 0.169). Culex mosquitoes were occasionally present in the same containers (5.2% of all containers surveyed, 11.3% of Ae. aegypti-positive containers), but were easily distinguished by morphology. We did not find any containers colonized only by Culex. 10.1371/journal.pntd.0001015.g001 Figure 1 Frequency distribution of Ae. aegypti eggs. Number of eggs collected per day in naturally-occurring containers throughout Iquitos, Peru (n = 591 containers). After controlling for collection period, three variables were significant predictors of whether females laid eggs in containers: Ae. aegypti larvae, exposure to sunlight (≥20% of day), and absence of a container lid (Table 2). The probability of oviposition increased when sites held conspecific larvae (β = 1.658; 95% CI = [1.286, 2.030]; p z Intercept −0.379 0.247 −1.533 0.125 Larvae (1–10, retained) 1.285a 0.362 3.546 100, retained) 2.332a 0.470 4.964 100, removed) 1.581a 0.557 2.840 0.005 Location (inside) −0.601b 0.249 −2.416 0.016 Location (outside, shade) −0.538b 0.211 −2.550 0.011 Lid (present) −0.706 0.369 −1.914 0.056 Collection period 2 −0.788c 0.275 −2.863 0.004 Collection period 3 −0.209 0.280 −0.749 0.454 Collection period 4 −1.027c 0.309 −3.327 0.10). Larvae refers to Ae. aegypti. Parameter estimates followed by the same letter are not statistically different from one another as indicated by Tukey's multiple comparisons. Significant p-values are indicated in bold. Among containers receiving eggs, the number of eggs laid was affected by larval abundance, whether larvae were removed prior to oviposition, pupae, fill method, circumference, and (circumference)2 (Table 3). Females laid more eggs when over 50 conspecific larvae were present in containers (β = 0.759; 95% CI = [0.483, 1.035]; p z Intercept 2.964 0.311 9.543 100, retained) 0.784a 0.227 3.459 100, removed) 0.838a 0.363 2.308 0.021 Pupae (present) 0.448 0.156 2.864 0.004 Fill method (manual) 0.073 0.160 0.458 0.647 Fill method (unmanaged) 0.387 0.150 2.578 0.010 Collection period 2 −0.470b 0.219 −2.144 0.032 Collection period 3 −0.531b 0.169 −3.138 0.002 Collection period 4 −1.293 0.225 −5.754 0.10). Larvae and pupae refer to Ae. aegypti. Parameter estimates followed by the same letter are not statistically different from one another as indicated by Tukey's multiple comparisons. Significant p-values are indicated in bold. Starvation bioassays Third instar larvae were collected for starvation bioassays from 113 containers. For the majority of containers, median larval RS was between 5 to 15 days (range 0 to 28 days) (Figure 3). There were no significant correlations between median RS and the mean density of eggs laid per day (all other larvae retained, n = 59 containers, Spearman's ρ = 0.15; all other larvae removed, n = 54 containers, Spearman's ρ = 0.0008). No correlations were evident when the data were also stratified by larval abundance or container capacity (data not shown). 10.1371/journal.pntd.0001015.g003 Figure 3 Median resistance to starvation vs. average number of eggs laid. Each circle represents an individual container (n = 113 containers). Median resistance to starvation is the median number of days that larvae from a container survive without food. Number of eggs laid in that container was averaged over the three day survey period and divided by container circumference (mm). Experimental study Ambient temperature and relative humidity were measured for the first four weeks and were consistent among the three study locations (field laboratory, house 1, and house 2) (Table 4). Water temperature (Table 4) was recorded for eight weeks and found to be similar for the field laboratory and house 1. Due to logger malfunction, water temperature was not recorded at house 2. Because Iquitos climate was relatively consistent during June to August 2009 (Table S1), we expect the data recorded at each location to be indicative of the entire study period. 10.1371/journal.pntd.0001015.t004 Table 4 Air temperature, relative humidity, and water temperature at three experimental study locations. Air temperature °C (± SD) Water temperature °C (± SD) Location Min Mean Max Mean RH % (± SD) Min Mean Max Field house 24.4±0.7 26.3±0.8 29.2±1.3 82.7±2.5 23.8±0.7 25.4±0.7 27.6±1.2 House 1 24.2±0.8 26.7±1.0 30.9±2.5 82.3±3.0 23.7±0.7 25.3±0.8 27.3±1.2 House 2 23.5±0.7 26.2±0.9 30.6±1.6 84.8±2.6 * * * *Water temperature data are missing from House 2 due to logger malfunction. Conspecific larvae were present in treatment A containers and absent from treatment B and C containers throughout the experiment. The number of Ae. aegypti eggs laid in each container per week was influenced by container treatment (ANOVA F = 77.70; df = 2, 4; p 50% of the day (ACM, unpublished data), in contrast to Iquitos, where abundant tree coverage limits sun exposure to only 10–40% of the day for most outdoor containers. We cannot directly compare our data to that of Barrera et al. [76] because metrics were not provided for container categories of “full sun,” “partial sun,” or “shaded.” We were not able to measure water temperature in each surveyed container. Maximum daily water temperatures from our experimental containers were typically 27–28°C, suggesting that water temperatures are lower in Iquitos compared to Puerto Rico. Attraction to large oviposition sites has been demonstrated in Ae. aegypti [36] as well as other mosquito species [67], possibly because large sites collect more food or are resistant to desiccation. We found that the number of Ae. aegypti eggs laid increased with container circumference up to a threshold around 270 cm, after which oviposition leveled off, indicating that perhaps the relative advantage of large container size diminishes as containers become bigger. Due to the low occurrence in Iquitos of containers exceeding 270 cm in circumference (n = 26 of 591 containers, 4% of surveyed containers), we could not assess the relationship further between increasing container size and oviposition. A major limitation of our study design was the inability to examine effects of container material and/or texture on oviposition. Container texture affects Ae. aegypti oviposition, with females preferring to lay their eggs on rough surfaces [34], [37]. Because we lined containers with strips of paper towel to transport eggs back to the field laboratory, we artificially made container surfaces homogeneous. In a previous Iquitos field study, we showed that females laid more eggs in cement containers compared to plastic or metal containers when all were unlined and similar in size [45]. Additional experimental studies should be conducted to investigate the importance of container material to oviposition site choice when conspecific presence and abundance, fill method, sun exposure, and container size are varied. Ae. aegypti oviposition site choice appears to be flexible, potentially reflecting a mix of site selection strategies across the population. A small portion of females may act as “founders” (e.g., [77]), choosing non-colonized sites based on environmental indicators of quality, whereas the majority of females respond predominantly to conspecific cues. Alternatively, each female may partition her egg batch so that most eggs are laid in colonized containers, when colonized containers are available, and a smaller fraction elsewhere. It should be noted that these scenarios are not mutually exclusive; for any female, the decision to reject or accept a particular site may change with time. For example, results from studies on herbivorous insects demonstrated that ovipositing females typically become more accepting of low-ranking sites as search time progresses (reviewed in [78]). Recent theoretical work on animal decision rules suggests that when individuals are limited by time, number of options, and accuracy with which they can assess site quality, decisions should be based on the best-of-n rule [79]. If female Ae. aegypti use this rule, they are likely to assess a fixed number of sites (n) and choose the perceived best among them, rather than searching longer for a site that meets specific criteria. Such a rule could explain the oviposition patterns we observed in Iquitos; colonized containers tend to be utilized when found, but other site characteristics (size, sunlight, and organic detritus) are used to judge site quality if the n sites do not include a colonized container. This remains to be confirmed in the field. Decision rules used by Ae. aegypti to select oviposition sites merit further investigation. Female choice of oviposition site may have greater impact on Ae. aegypti population dynamics than previously thought. We propose that, due to strong conspecific attraction, oviposition site selection could lead to dense aggregations of larvae and actually contribute to density-dependent regulation. This phenomenon may explain why larvae in the field frequently develop under food-limiting conditions [9], [38], [80]. It is likely that while some colonized sites become crowded, other suitable larval development sites remain empty. A companion study in Iquitos indicated comparable survival and development rates when larvae were reared in water collected from colonized vs. non-colonized containers in the field, suggesting no difference in food content (STS, unpublished data). These results imply that availability of larval food is not the primary determinant of oviposition choices and agree well with our larval starvation data presented herein. A similar study in Trinidad, West Indies, revealed no difference in nutrient levels between water-storage drums colonized or not by Ae. aegypti [81]. Our results have direct implications for strategies to control Ae. aegypti. Targeting containers that produce the most Ae. aegypti adults for removal or larvicide treatment will reduce mosquito populations in the short term. Sustained population suppression, however, will be difficult to achieve by these means. Elimination of highly productive containers (or the immature Ae. aegypti within) will likely shift new eggs to alternative suitable containers. If immature conspecifics are no longer available as a strong oviposition cue, females that would have concentrated their eggs in those highly productive sites may instead oviposit among suitable, previously unoccupied containers based on food availability and/or sun exposure. Strategies that kill mosquitoes late in their development (i.e., insect growth regulators (IGRs) that kill pupae [82], [83] rather than larvae) will enhance vector control by creating “egg sinks,” treated containers that exploit conspecific attraction of ovipositing females, but reduce emergence of adult mosquitoes via density-dependent larval competition and late acting insecticide. For an egg sink strategy, it would be best to employ IGRs that have no repellent effects on ovipositing females, such as pyriproxyfen [84] or methoprene [85]. Pyriproxyfen is of particular interest because adult females are able to transfer the IGR to other oviposition sites [84], [86]. Thus, pyriproxyfen-treated containers could potentially serve as both egg sinks and sources for insecticide dissemination. The success of this approach would depend on oviposition patterns of individual females. Alternatively, rather than relying on conspecific larvae, control tools could be designed to capitalize on the attractant or stimulant properties of semiochemicals influencing Ae. aegypti oviposition responses in the field. Bacteria-derived oviposition attractants could be used to lure females to lethal ovitraps or stimulants could be used to increase their exposure to insecticide-impregnated substrates [30]. The fact that wild Ae. aegypti are quite selective when choosing oviposition sites may be the basis for development of new strategies and products for control of dengue virus vectors. Supporting Information Table S1 Mean air temperature, relative humidity, and rainfall in Iquitos by month during 2007 through 2009. (DOC) Click here for additional data file. Checklist S1 STROBE checklist. (DOC) Click here for additional data file.