How to Kill Mosquito Larvae in Standing Water With Household Products, Hunker

How to Kill Mosquito Larvae in Standing Water With Household Products

Invisible to the naked eye, mosquito larvae live in standing water and eventually grow into pesky, blood-sucking adults. The larvae require only minimal amounts of water, and even something as small as a puddle of water on a discarded plastic bag can house them. Whenever possible, it is better to prevent mosquito larvae than to try to deal with the problem after the fact. There are, however, several household items you can use to safely kill any larvae that may be lurking in standing water outside your home.

Preventing Larvae

The best way to deal with mosquito larvae is to avoid them. To do so, remove full buckets, barrels, toys, plastic bags and other clutter from your property. Anything that can hold water can host mosquito larvae, so examine your surroundings closely for potential breeding sites. Consider cleaning your gutters, as well, if they overflow when it rains. Clogged gutters may hold standing water in a location safely above predators where larvae can thrive. If you have a backyard pond, keep the water well-aerated and stock it with goldfish varieties that eat mosquito larvae. It is also helpful to grow plants that naturally repel mosquitoes throughout your landscape. Some suggestions include lime basil, catnip and thyme. Change the water in birdbaths regularly and stay on top of pool maintenance.

Killing Larvae

If you find standing water on your property that may already be hosting mosquito larvae, you can use household items to kill them safely. Pay attention to the environment around you, and use control methods that are safe for children, pets and other wildlife. Some methods work better on some mosquito species than others, so feel free to experiment with multiple methods to see which works best for you.

A thin coating of oil on top of water suffocates mosquito larvae, killing them quickly. Use natural oils, such as olive oil or vegetable oil, for this control method. You also can use cinnamon oil. You only need 1 teaspoon of oil per gallon of water. Do not use this method on ponds and other bodies of water that contain fish.

Apple Cider Vinegar

Adding apple cider vinegar to standing water effectively kills mosquito larvae but requires about 18 hours to get the job done. Completely non-toxic, vinegar kills mosquito larvae at a ratio of 15 percent vinegar to 85 percent water. Make sure you use enough vinegar; lower concentrations don’t kill the larvae. Vinegar is environmentally safe, so use more than necessary when in doubt. Vinegar also can be used as a mosquito repellent.

Hardware stores typically stock insecticidal soap to help gardeners control pests. Any soap, however, will work. To get rid of mosquito larvae, simply put some dish soap or shampoo into standing water. A single milliliter of soap in a gallon of water will kill most mosquito larvae in a day.

Bleach

Although not environmentally friendly, household bleach is an effective mosquito larvae killer. Use bleach only as a last resort and only if you are sure a runoff can’t enter a water source or harm wildlife. Most often used to clean larvae from rain gutters, a tablespoon of bleach per gallon of standing water will kill larvae. For ongoing protection, consider wiping your gutters with a 1-to-1 solution of bleach and water.

Bacteria

Though not a household item, mosquito dunks are an excellent way to safely kill mosquito larvae. These doughnut-shaped rings contain bacteria (Bacillus thuringiensis israelensis) that are toxic only to mosquito larvae. Each dunk treats a water surface up to 100 square feet and lasts about a month. Although deadly to mosquito larvae, these bacteria are harmless to pets and wildlife. Simply drop the dunk in the water you wish to treat, and you’re finished. The same bacteria are available in pellet form to treat smaller areas of standing water.

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Michelle Miley

Home is where the heart is, and Michelle frequently pens articles about ways to keep yours looking great and feeling cozy. Whether you want help organizing your closet, picking a paint color or finishing drywall, Michelle has you covered. If she’s not puttering in the house, you’ll find her in the garden playing in the dirt. Her garden articles provide tips and insight that anyone can use to turn a brown thumb green. You’ll find her work on Modern Mom, The Nest and eHow as well as sprinkled throughout your other online home decor and improvement favorites.

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How Mosquitoes Work

Mosquito Development

All mosquitoes lay eggs in water, which can include large bodies of water, standing water (like swimming pools) or areas of collected standing water (like tree holes or gutters). Females lay their eggs on the surface of the water, except for Aedes mosquitoes, which lay their eggs above water in protected areas that eventually flood. The eggs can be laid singly or as a group that forms a floating raft of mosquito eggs (see Mosquito Life Cycle for a picture of an egg raft). Most eggs can survive the winter and hatch in the spring.

Larva

The mosquito eggs hatch into larvae or «wigglers,» which live at the surface of the water and breathe through an air tube or siphon. The larvae filter organic material through their mouth parts and grow to about 0.5 to 0.75 inches (1 to 2 cm) long; as they grow, they shed their skin (molt) several times. Mosquito larvae can swim and dive down from the surface when disturbed (see Mosquito Life Cycle for a Quicktime movie of free-swimming Asian tiger mosquito larvae). The larvae live anywhere from days to several weeks depending on the water temperature and mosquito species.

After the fourth molt, mosquito larvae change into pupae, ­or «tumblers,» which live in the water anywhere from one to four days depending on the water temperature and species. The pupae float at the surface and breathe through two small tubes (trumpets). Although they do not eat, pupae are quite active (see Mosquito Life Cycle for a Quicktime movie of free-swimming Asian tiger mosquito pupae). At the end of the pupal stage, the pupae encase themselves and transform into adult mosquitoes.

Adult

Inside the pupal case, the pupa transforms into an adult. The adult uses air pressure to break the pupal case open, crawls to a protected area and rests while its external skeleton hardens, spreading its wings out to dry. Once this is complete, it can fly away and live on the land.

One of the first things that adult mosquitoes do is seek a mate, mate and then feed. Male mosquitoes have short mouth parts and feed on plant nectar. In contrast, female mosquitoes have a long proboscis that they use to bite animals and humans and feed on their blood (the blood provides proteins that the females need to lay eggs). After they feed, females lay their eggs (they need a blood meal each time they lay eggs). Females continue this cycle and live anywhere from many days to weeks (longer over the winter); males usually live only a few days after mating. The life cycles of mosquitoes vary with the species and environmental conditions.

You can distinguish the larvae of various mosquito species. Anopheles larvae lie parallel to the surface of the water, while larvae of Aedes and Culex extend down into the water (the air tubes of Culex are longer than those of Aedes).

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Biology

Lifecycle

The natural history of malaria involves cyclical infection of humans and female Anopheles mosquitoes. In humans, the parasites grow and multiply first in the liver cells and then in the red cells of the blood. In the blood, successive broods of parasites grow inside the red cells and destroy them, releasing daughter parasites (“merozoites”) that continue the cycle by invading other red cells.

The blood stage parasites are those that cause the symptoms of malaria. When certain forms of blood stage parasites (gametocytes, which occur in male and female forms) are ingested during blood feeding by a female Anopheles mosquito, they mate in the gut of the mosquito and begin a cycle of growth and multiplication in the mosquito. After 10-18 days, a form of the parasite called a sporozoite migrates to the mosquito’s salivary glands. When the Anopheles mosquito takes a blood meal on another human, anticoagulant saliva is injected together with the sporozoites, which migrate to the liver, thereby beginning a new cycle.

Thus the infected mosquito carries the disease from one human to another (acting as a “vector”), while infected humans transmit the parasite to the mosquito, In contrast to the human host, the mosquito vector does not suffer from the presence of the parasites.

The malaria parasite life cycle involves two hosts. During a blood meal, a malaria-infected female Anopheles mosquito inoculates sporozoites into the human host . Sporozoites infect liver cells and mature into schizonts , which rupture and release merozoites . (Of note, in P. vivax and P. ovale a dormant stage [hypnozoites] can persist in the liver (if untreated) and cause relapses by invading the bloodstream weeks, or even years later.) After this initial replication in the liver (exo-erythrocytic schizogony ), the parasites undergo asexual multiplication in the erythrocytes (erythrocytic schizogony ). Merozoites infect red blood cells . The ring stage trophozoites mature into schizonts, which rupture releasing merozoites . Some parasites differentiate into sexual erythrocytic stages (gametocytes) . Blood stage parasites are responsible for the clinical manifestations of the disease. The gametocytes, male (microgametocytes) and female (macrogametocytes), are ingested by an Anopheles mosquito during a blood meal . The parasites’ multiplication in the mosquito is known as the sporogonic cycle . While in the mosquito’s stomach, the microgametes penetrate the macrogametes generating zygotes . The zygotes in turn become motile and elongated (ookinetes) which invade the midgut wall of the mosquito where they develop into oocysts . The oocysts grow, rupture, and release sporozoites, which make their way to the mosquito’s salivary glands. Inoculation of the sporozoites into a new human host perpetuates the malaria life cycle.

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Human Factors And Malaria

Biologic characteristics and behavioral traits can influence an individual’s risk of developing malaria and, on a larger scale, the intensity of transmission in a population.

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Computational and experimental insights into the chemosensory navigation of Aedes aegypti mosquito larvae

Department of Biology, University of Washington, Box 351800, Seattle, WA 98195, USA

Tjinder S. Grewal

Department of Biochemistry, University of Washington, Box 357350, Seattle, WA 98195, USA

Jeffrey A. Riffell

Department of Biology, University of Washington, Box 351800, Seattle, WA 98195, USA

Department of Biology, University of Washington, Box 351800, Seattle, WA 98195, USA

Tjinder S. Grewal

Department of Biochemistry, University of Washington, Box 357350, Seattle, WA 98195, USA

Jeffrey A. Riffell

Department of Biology, University of Washington, Box 351800, Seattle, WA 98195, USA

Abstract

Mosquitoes are prolific disease vectors that affect public health around the world. Although many studies have investigated search strategies used by host-seeking adult mosquitoes, little is known about larval search behaviour. Larval behaviour affects adult body size and fecundity, and thus the capacity of individual mosquitoes to find hosts and transmit disease. Understanding vector survival at all life stages is crucial for improving disease control. In this study, we use experimental and computational methods to investigate the chemical ecology and search behaviour of Aedes aegypti mosquito larvae. We first show that larvae do not respond to several olfactory cues used by adult Ae. aegypti to assess larval habitat quality, but perceive microbial RNA as a potent foraging attractant. Second, we demonstrate that Ae. aegypti larvae use chemokinesis, an unusual search strategy, to navigate chemical gradients. Finally, we use computational modelling to demonstrate that larvae respond to starvation pressure by optimizing exploration behaviour—possibly critical for exploiting limited larval habitat types. Our results identify key characteristics of foraging behaviour in an important disease vector mosquito. In addition to implications for better understanding and control of disease vectors, this work establishes mosquito larvae as a tractable model for chemosensory behaviour and navigation.

1. Introduction

The mosquito Aedes aegypti is a global vector of diseases such as Dengue, Zika and Chikungunya [1]. This synanthropic mosquito is evolutionarily adapted to human dwellings, with some populations breeding exclusively indoors [2,3]. The urban microhabitat features unique climatic regimes, photoperiod and resource availability. In response to these selective pressures, successful synanthropic animals including cockroaches [4], rats [5] and crows [6] exhibit many behaviours absent in non-urbanized sibling species. Understanding these behaviours is of major importance to public health. Throughout human history, synanthropic disease vectors have caused devastating pandemics like the Black Death, which killed an estimated 30–40% of the western European population [7,8]. Like rats or cockroaches, adult Ae. aegypti mosquitoes exhibit many behavioural adaptations to human microhabitats [2,9]. However, comparatively little is known about larval adaptations. The larval environment directly affects adult body size [10,11], fecundity [10] and biting persistence [12], and understanding vector survival at all life stages is crucial for improving disease control [13]. Despite growing interest [14–16], it remains an open question how environmental stimuli affect larval behaviour to regulate these responses and processes.

In addition to the above public health implications, the behaviour of synanthropic mosquito larvae is fascinating from a theoretical search strategy perspective. Aedes aegypti larvae are aquatic detritivores that live in constrained environments such as vases and tin cans [11]. In such limited environments, do larvae exhibit a chemotactic search strategy (in which animals change their direction of motion in response to a chemical stimuli), or do they use a chemokinetic response (in which animals change a non-directional component of motion, such as speed or turn frequency, in response to a chemical stimuli) [17]? Mechanistic understanding of larval foraging behaviour may provide insight into chemosensory systems controlling the behaviour as well as the evolutionary adaptations for these systems in synanthropic environments.

In this work, we investigate larval Ae. aegypti behavior from a chemical ecological and search theory perspective. First, we explore chemosensory cues involved in larval foraging. Although many olfactory cues are used by adult females to select oviposition sites [18], it is unclear if larvae and adults use the same chemicals to assess larval habitat quality. Second, we consider larval search behaviour in spatially restricted environments using empirical data and computational modelling. Our work identifies the lack of chemotaxis in foraging Ae. aegypti larvae—an example of how environmental restrictions may drive the evolution of animal behaviour. We further identify microbial RNA as a potent and unusual larval foraging attractant. Together, our results identify Ae. aegypti larvae as a tractable model in biological search theory, and highlight the importance of investigating synanthropic disease vectors at all life-history stages.

2. Results

(a) Effects of sex, physiological state and circadian timing on larval physiology

Behavioural experiments in insects have demonstrated the importance of circadian timing, starvation and age [19]. However, little is known about the effects of these variables on Ae. aegypti larvae. To better understand the effects of nutritional state and sex on our study organism, we used machine vision to track individual 4th instar Ae. aegypti larvae in a custom arena before each experiment (figure 1a). For both fed and starved animals, female larvae were larger than males (fed larvae: n = 135 female, 153 male, p

Figure 1. Quantifying the chemosensory environment in naturalistic larval habitat sizes. (a) Diagram of experimental conditions, adapted from [20], including a Basler Scout Machine Vision GigE camera (orange), infrared lighting (yellow) and a behaviour arena (blue). (b) Chemosensory diffusion map of the behaviour arena at the end of the 15 min experiment. (c) Example of an individual larval trajectory during the 15 min acclimation phase (left). Trajectory of same individual during the 15 min experiment phase, responding to food added to the left side of the arena (right). (d) Trajectory of all starved animals presented with food (top) or quinine (bottom). Although trajectories are shown aggregated into one image, all animals were tested individually. Scatter points show the position of each animal at the end of the experiment and colour overlays show the chemosensory diffusion map at the end of the 15 min experiment. (Online version in colour.)

(b) Quantifying the chemosensory environment in naturalistic larval habitat sizes

Previous research has shown that other species of mosquito larvae detect many different chemosensory stimuli [23]. In Ae. aegypti, it is unclear what chemical cues, if any, larvae use to navigate their environment. Nevertheless, chemosensory cues may be essential in avoiding predation or foraging efficiently. Using our arena and machine vision methods, we investigated larval preference for eight putatively attractive and aversive sets of stimuli. First, we experimentally verified the chemical diffusion in the arena and found that larval movement significantly increased the diffusion of stimuli within the arena (p −1 h −1 ) from growing populations of microbes in freshwater habitats [32], and could provide valuable foraging information to omnivores such as Ae. aegypti. By contrast, other isolated macronutrients such as salts, sugars and amino acids elicit little to no attraction in other larval mosquito species [33].

(c) Physiological feeding state affects larval attraction towards ecologically relevant odours

For each of these eight sets of stimuli, in addition to water, we compared the stimulus preference of larvae before and after stimulus addition (figures 1c, 2a; electronic supplementary material, figures S3–S5). Preference was defined as the median concentration chosen by the larvae throughout the 15 min experiment, normalized to behaviour during the previous 15 min acclimation phase. Starved larvae were attracted to food (n = 32, p

Figure 2. Physiological feeding state affects larval attraction towards ecologically relevant odours. (a(i)) Example trajectory of a starved larva during the acclimation (top) and the experiment phase (below), responding to food introduced to the top left. (a(ii)) Distribution of larvae during the acclimation phase (grey) and experiment phase (green), median concentration. The shaded box visualizes the mean ΔP across all individuals. Note that owing to the unequal distribution of high and low concentration areas in the behaviour arena, animals naturally appear to distribute near lower concentrations when no stimulus is present. (b(i)) Example trajectory of a fed larva during the acclimation (top) and experiment phase (below), responding to food introduced to the top left. (b(ii)) Distribution of fed larval preference during the acclimation (grey) and experiment phase (purple). In ((a)ii) and ((b)ii), asterisks denote the significance level of paired-sample Welch’s t-tests comparing acclimation P and experiment P (n.s., not significant). n values reported next to each stimulus describe the number of animals in the treatment. (Online version in colour.)

The physiological feeding state of an adult mosquito has a strong impact on subsequent behavioural preferences [34], but it remains unknown how feeding status influences responses to chemosensory stimuli in larvae. We thus fed larvae ad libitum fish food before testing their responses to each of the eight stimuli and a water control (figure 2b). Fed larvae showed no significant attraction to food (n = 57, p = 1), food extract (n = 19, p = 1) and RNA (n = 20, p = 1), supporting the prediction that microbial RNA functions as an attractant in the context of foraging. Nonetheless, fed larvae still exhibited aversive responses to quinine (n = 24, p = 0.003), demonstrating that the lack of response to foraging cues is not because of a global reduction in chemosensory behaviour. Similar to starved larvae, fed animals showed no preference for the water control (n = 39, p = 1), indole (100 µM: n = 36, p = 0.98; 10 mM: n = 17, p = 1), glucose (n = 17, p = 1) or the amino acid mixture (n = 23, p = 1). Fed larvae exhibited significant aversion to o-cresol (n = 36, p = 0.026).

(d) A chemokinesis navigation strategy is most consistent with larval aggregation towards cues investigated in this study

Next, we investigated the behavioural mechanism by which Ae. aegypti larvae locate sources of odour, because such information could provide insight into the chemosensory pathways that mediate the behaviours. We hypothesized that larval aggregation near attractive cues such as food is mediated by chemotaxis—a common form of directed motion observed in many animals and microbes [35–37]. In chemo-klino-taxis (hereafter chemotaxis), animals exhibit directed motion with respect to a chemical gradient. Alternatively, larvae may exhibit chemo-ortho-kinesis (hereafter chemokinesis)—a process in which animals respond to local conditions by regulating speed rather than direction—or chemo-klino-kinesis (hereafter klinokinesis)—in which animals respond to local conditions by regulating turning frequency. Finally, larvae may be unable to detect chemosensory stimuli, and thus exhibit purely random behaviour (hereafter anosmic). To differentiate between these strategies, we quantified six observable metrics used to characterize navigation behaviour (table 1). By breaking down larval trajectories into several different components (figure 3a,b) and identifying which variables correlate with stimulus preference (figure 3c,d), we can infer which search strategy best explains larval behaviour.

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Figure 3. Larval exploration behaviour is best explained by a chemokinesis search model. (a) Diagram of behavioural quantifications. Larvae were observed during a 15 min acclimation period in clean water, followed by a 15 min experiment in the presence of the stimulus. The arena was divided into an area of high (≥ 50%) and low concentration (

Table 1. Comparing larval exploration behaviour to canonical animal search strategy models. (Four different chemosensory search strategies are listed (central columns) along with the expected observable behaviour metrics for each strategy (left column). By comparing the experimental observations (right column) with the expected results, we determined that Ae. aegypti larval chemosensory navigation is best explained by a chemokinesis search strategy model. Cells in bold type indicate expected experimental results (anosmic, chemotaxis, klinotaxis, and chemokinesis) or statistically significant observed results (far right column).)

potential chemosensory search strategies anosmic chemotaxis klinokinesis chemokinesis experiment observations stimulus preference ΔP no yes yes yes yes(p 1 , we further simulated habitats 50, 100 and 150 cm in diameter for comparison. We found that larvae still discovered the food source in several hours (fed simulations: 1.7, 4.3 and 8.3 h; starved simulations: 1.2, 4.2 and 7.5 h for 50, 100 and 150 cm arenas, figure 4e). Finally, the slope for starved animals in smaller habitats was about twice that of fed animals (starved: 45.3 s cm −1 ; fed: 22.9 s cm −1 ), suggesting that the benefit of behavioural modification in starved animals is more pronounced in smaller arena sizes (slope of difference between fed—starved simulations: −22.4 s cm −1 ).

Figure 4. Starved Ae. aegypti optimize exploration behaviour to increase the probability of finding food. (a) Starved larvae spend more time exploring the arena than fed larvae. (b) Starved larvae spend less time within one body length of the arena wall. (a,b) Violin plot. Dots represent each individual, and black bar is the mean across all individuals (n > 168 per treatment); asterisks denote p

3. Discussion

In this study, we quantify essential characteristics of Ae. aegypti larval behaviour that are crucial for the development of future studies. Furthermore, we identify previously unknown behaviours that highlight the unique evolutionary history and developmental biology of these disease vector mosquitoes. First, we show that larvae perceive microbial RNA as a foraging attractant, but do not respond to several olfactory cues that attract adult Ae. aegypti for oviposition. Second, we demonstrate that Ae. aegypti larvae use chemokinesis, rather than chemotaxis, to navigate with respect to chemical sources. Finally, we use experimental observations and computational analyses to demonstrate that larvae respond to starvation pressure by changing their behaviour to increase the probability of finding food sources in realistic habitat sizes.

Although adult Ae. aegypti feeding is regulated by ATP perception [39], we are unaware of other work demonstrating perception of nucleotides or nucleic acids such as RNA in Ae. aegypti larvae. In our state-dependent preference experiments, we investigate the ecological basis of larval RNA attraction, and propose that RNA may function as one of the foraging indicators in the larval environment. However, 44 different nutrients are required for Ae. aegypti larval survival [27], and the attractiveness of other potential phagostimulants including vitamins and carbohydrates have not been tested with the sensitivity of our experimental methods. In addition, the concentration and relative composition of phagostimulants may have complex effects on larval preference, and these combinatorial effects were not examined in this study. In a natural environment Ae. aegypti larvae probably rely on a combination of stimuli to locate food sources. Nevertheless, an earlier study demonstrated that olfactory receptor deficient (orco −/−) Ae. aegypti larvae showed no defects in attraction to food [20]. Taken together, our results support the hypothesis that sensory information gained from gustatory or ionotropic receptors may be more integral to larval chemosensation than olfactory receptors. Furthermore, larval attraction to RNA suggests that the importance of nucleotide phagostimulation is preserved throughout a mosquito’s life cycle, from larval foraging to adult blood engorgement and oviposition.

Our study also raises a number of comparative questions that could be addressed in future research. For instance, is chemokinesis in mosquito larvae associated with human association and man-made containers? Future studies could compare chemotactic ability in other spatially constrained mosquitoes, such as Toxorhynchites (which inhabit tree holes) or Aedes albopictus (another container-breeding mosquito) [40], to species that oviposit in larger bodies of water such as Aedes togoi (marine rock pools) or opportunistic species such as Culex nigripalpus that oviposit in a wide range of habitat sizes [40,41]. Additionally, computational modelling of fluid dynamics and larval movement may help determine whether chemotaxis is physiologically and physico-chemically challenging in small, man-made environments. Owing to the diffusive environment in the small containers, where shallow gradients dominate and turbulence is lacking, the change in time or space of the chemical signal may be too small for the larvae to detect. This is particularly relevant considering our results showing that larval movement significantly modifies the stimulus gradient [42].

Synanthropic mosquitoes are increasingly important to global health as urbanization progresses: currently, over half of all humans live in urban environments, and this proportion is only expected to increase [43]. Adaptations that facilitate human cohabitation, like specialized larval foraging strategies, are vital to our understanding of mosquito behaviour and success as a disease vector [9].

4. Material and methods

Details on the insects, selection of preparation of odorants and statistical analyses, can be found in the electronic supplementary material.

(a) Behaviour arena and experiment

We previously developed a paradigm to investigate chemosensory preference in larval Ae. aegypti [20]. In this study, we expanded our protocol by mapping the chemosensory environment in our arena using fluorescein dye. Importantly, because larval swimming activity increases chemical movement within the arena, we mapped the dye distribution from experiments containing an actively swimming larva. Fluorescein dye (100 µl) was added to a white arena of the same material and dimensions, each containing one Ae. aegypti larva. Dye colour was converted to concentration values using a standardization dataset of 13 reference concentrations (electronic supplementary material, figure S2C). Dye diffusion through time was quantified by the mean of all values in each 1 mm 2 area, linearly interpolated throughout time (n = 10; electronic supplementary material, figure S2B).

During behaviour experiments, we recorded animals for 15 min before each experiment to analyse baseline activity and confirm that the arena was fair in the absence of chemosensory cues. Subsequently, 100 µl of a chemical stimulus was gently pipetted into the left side of the arena to minimize mechanosensory disturbances, and larval activity was recorded for another 15 min (figure 1c).

(b) Video analyses

Video data were obtained and processed as previously described [20] using M ultitracker software by Floris van Breugel [44] and P ython v.3.6.2. Additionally, approximate larval length was measured for each animal in I mage J F iji [45], as the pixel length from head to tail, in a selected video frame that showed the larva in a horizontal position. Lengths were converted to mm using the known inner container width as the conversion ratio. Experimenters were blind to larval sex when measuring lengths. Throughout our analyses, the arena was divided into areas of high concentration (≥ 50% initial stimulus) and low concentration ( −1 was required to qualify as moving up or down the concentration map.

(c) Computational modelling

We developed a chemokinetic computational model to investigate larval foraging success in different environments. This model resampled the observed trajectories of Ae. aegypti larvae to investigate the consequences of a chemokinetic search strategy using realistic larval behavioural metrics. In the experimental foraging task, simulated animals explored a circular arena until they encountered a food source at the centre of the arena. These arenas included a range of 19 different arena sizes representing many of the ecologically realistic habitats reported in the literature (electronic supplementary material, table S1). The food target was scaled to arena size (comprising 3% of total area) under the assumption that habitats of larger diameter would also contain higher absolute amounts of food. Each simulated larvae began at a random point within the arena, and then explored the environment at each time step by sampling a paired speed-angle data point from experimental data (figure 4c(i)). We elected to pair these data points in our model because we observed that the two variables were correlated at higher speeds (figure 4c(i)). The time step was re-sampled if the selected data point would cause the trajectory of the simulated larvae to leave the boundary of the experimental arena. Data from animals tested with glucose and amino acids were not included. These experiments were conducted during the manuscript review process, and it was not possible to rerun simulations in the allotted time. Nevertheless, our simulations were sampled from over 700 000 data points from 416 individual larvae. To approximate chemokinetic behaviour, simulated larvae in areas of high food concentration (> 50%) moved slower, and larvae in areas of low food concentration (≤ 50%) moved faster. These differences were implemented by splitting the paired speed-angle data into two bins of equal size, with one bin containing the slowest half of all data points and the other containing the faster half. The probability of sampling from each half was determined as a function of the instantaneous food concentration (figure 4c(ii)), with the addition of an exponentially smoothed decision boundary to reduce thresholding artefacts. The empirical data pairs used in these models represented all data taken from larvae observed in clean water before the addition of experimental stimuli, with fed simulations sampling data from fed animals and starved simulations sampling data from starved animals only (n = 248 fed, n = 168 starved). To define the boundary of 50% food concentration for chemokinetic behavioural decisions, we defined the simulated chemical conditions using an exponential regression model of distance and concentration based on our empirical chemical map (electronic supplementary material, figure S2E). When the simulated larvae entered the food patch at the centre of the arena, the simulation was stopped and the time taken to discover the food was recorded (in seconds). We conducted 1000 simulations for each arena size and nutritional state (fed versus starved).

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Data accessibility

Code associated with this manuscript can be found at: https://github.com/eleanorlutz/aedes-aegypti-2019. Data available from the Dryad Digital Repository: https://dx.doi.org/10.5061/dryad.s1rn8pk3n [46].

Authors’ contributions

Conceptualization: E.K.L. and J.A.R.; methodology: E.K.L. and J.A.R.; software: E.K.L.; investigation: E.K.L. and T.S.G.; resources: E.K.L. and J.A.R.; data curation: E.K.L.; writing—original draft: E.K.L.; writing—review and editing: E.K.L., J.A.R. and T.S.G.; visualization: E.K.L.; supervision: J.A.R.; project administration: J.A.R.; funding acquisition: E.K.L. and J.A.R.

Competing interests

The authors declare no competing interests.

Funding

This work was supported in part by grants from the National Institute of Health grant no. 1RO1DCO13693 and 1R21AI137947 to J.A.R.; National Science Foundation grant nos IOS-1354159 to J.A.R. and DGE-1256082 to E.K.L.; Air Force Office of Sponsored Research under grant no. FA9550-16-1-0167 to J.A.R.; and the Robin Mariko Harris Award to E.K.L. We thank Floris van Breugel for assistance with video data analysis, the University of Washington Biostatistics Consulting Group for statistical advice, and Binh Nguyen and Kara Kiyokawa for maintaining the Riffell laboratory mosquito colony.

Acknowledgements

We also thank Thomas Daniel, Bingni Brunton, Kameron Harris and the Kincaid 320 Python Club for insightful discussions on programming and data management. Finally, we thank two anonymous reviewers for their contribution of time, expertise and thoughtful advice that significantly improved this manuscript.

Footnotes

1 Large breeding sites are probably more likely to contain multiple small patches of food distributed throughout the environment, rather than our simulated model of one single patch.

Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.4722287.

Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

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