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  • Review
  • Open Access

Keeping track of mosquitoes: a review of tools to track, record and analyse mosquito flight

Parasites & Vectors201811:123

https://doi.org/10.1186/s13071-018-2735-6

  • Received: 3 October 2017
  • Accepted: 21 February 2018
  • Published:

Abstract

The health impact of mosquito-borne diseases causes a huge burden on human societies. Recent vector control campaigns have resulted in promising declines in incidence and prevalence of these diseases, notably malaria, but resistance to insecticides and drugs are on the rise, threatening to overturn these gains. Moreover, several vector-borne diseases have re-emerged, requiring prompt and effective response measures. To improve and properly implement vector control interventions, the behaviour of the vectors must be well understood with detailed examination of mosquito flight being an essential component. Current knowledge on mosquito behaviour across its life history is briefly presented, followed by an overview of recent developments in automated tracking techniques for detailed interpretation of mosquito behaviour. These techniques allow highly accurate recording and observation of mating, feeding and oviposition behaviour. Software programmes built with specific algorithms enable quantification of these behaviours. For example, the crucial role of heat on host landing and the multimodal integration of carbon dioxide (CO2) with other host cues, has been unravelled based on three-dimensional tracking of mosquito flight behaviour. Furthermore, the behavioural processes underlying house entry and subsequent host searching and finding can be better understood by analysis of detailed flight recordings. Further potential of these technologies to solve knowledge gaps is discussed. The use of tracking techniques can support or replace existing monitoring tools and provide insights on mosquito behaviour that can lead to innovative and more effective vector-control measures.

Keywords

  • Flight behaviour
  • Automated tracking
  • Mosquito
  • 3D analysis
  • Infectious diseases
  • Malaria
  • Behavioural ecology
  • Vector control

Background

Mosquito-borne diseases continue to impose a heavy burden on human societies and impede welfare and economic development [1]. Despite promising declines of malaria incidence within the last 15 years [2], other mosquito-borne diseases such as Zika, dengue, chikungunya and West-Nile virus are on the rise and have spread over various continents [3, 4]. In addition to climate change, international trade and human transport are considered to be the main drivers of introductions of new vectors, with or without their pathogens, into different geographical regions [59].

The recently reported global decline in malaria was achieved mainly by wide-spread use of long-lasting insecticide-treated nets (LLINs), indoor residual spraying (IRS) and proper drug treatment, but insecticide resistance and resistance to antimalarial drugs threaten to prevent further reductions or may even lead to disease resurgence [10, 11]. Innovative alternative methods are being developed, such as the use of entomopathogenic fungi [12], biolarvicides (Bacillus spp.) [13, 14], push-pull systems and mass trapping techniques using odour-baited traps [15, 16] in combination with house improvements [17]. It is expected that the combined use of these tools, along with LLINs, IRS and proper drug treatment, may provide a more sustainable strategy for vector-borne disease control [18]. Recently, the World Health Organization launched a strategic approach named Global Vector Control Response, aiming for locally adapted sustainable control measures to target multiple vectors [19, 20].

Successful implementation of new vector control tools along with existing tools following the integrated vector management approach requires detailed understanding of mosquito behaviour. For example, the contact rate of mosquitoes with insecticide-treated bed nets is an important parameter in understanding the efficacy of this technology [21, 22]; higher efficacy of mosquito traps requires knowledge on trap entry behaviour [2325], and push-pull strategies can be made more effective if mosquito foraging behaviour in the peri-domestic area is well understood [26].

Innovative tracking techniques have opened a new array of possibilities for examining insect behaviour in both the laboratory and (semi-) field [2729]. The focus of our review is on the tracking of mosquitoes in space in order to elucidate fundamental aspects of their behaviour in different phases of their life history. An overview of tools used for behavioural tracking of mosquitoes and their technical complexities is provided. We discuss how in-depth knowledge on mosquito behaviour can be exploited for the development and evaluation of vector control strategies.

Behaviour across the mosquito life history

The mosquito life history traits can be divided into plant feeding, mating, host feeding, oviposition, larval development and pupation. There is surprisingly little fundamental knowledge about the behavioural aspects of these phases, despite a wealth of knowledge on the factors affecting these behaviours [3036]. The behavioural aspects of these phases are briefly described below.

We pay attention to anthropophilic mosquito species which forage in and around human dwellings in search of a blood meal [37]. For Anopheles species, many of these behaviours occur in the evening or at night, when darkness makes direct observations more challenging. Video-recording makes it possible to visualize mosquito behaviour on a small scale, even during the scotophase, without having the experimental set-up affected by (extra) cues associated with the researcher. Besides host-seeking, describing other behaviours during the mosquito life-cycle, e.g. mating, (post-) feeding and oviposition, can also greatly benefit the understanding of mosquito-borne disease ecology and further assist in the development and evaluation of surveillance and intervention tools.

Plant feeding

Among the first activities of a mosquito after emergence from the pupa is the search for a sugar source. Carbohydrates provide energy for mosquitoes’ daily requirements and males rely fully on sugar-feeding [38]. The sugar source is mostly nectar, but can also consist of fruits, honeydew or extra-floral nectar. Mosquitoes can show strong preferences for certain plant sources (reviewed by [39]). Deprivation of sugar sources affects the flight capacity and can consequently affect mosquito dispersal, mating success and/or host-finding [4042].

Recent studies indicate that mosquitoes can learn to associate visual cues with the quality of sugar sources [30]. Such studies contribute to further development of effective use of (toxic) sugar baits to manipulate or control mosquito vectors [43, 44] or to monitor the presence of pathogens in local populations [45, 46]. Behavioural studies on sugar-feeding can contribute to the knowledge of preferred plant sources, time-budget spent based on efficiency and duration of feeding and possible competition for these sugar sources with other organisms visiting the plant [31, 38]. In addition, questions such as whether mosquitoes are important for pollination can be answered based on behavioural observations; this is an area of research showing growing evidence that mosquitoes are not just nectar thieves [38, 47, 48].

Mating

Mosquitoes, along with other Diptera, have the special ability to mate in flight [49]. Depending on the species, mating occurs in swarms formed by males (most anopheline species) but both sexes can also assemble near emergence sites or around vertebrate hosts (mostly culicine species) [50]. Acoustic signals play an important role in mate selection, but the role of pheromones is less clear and only explored for Aedes aegypti [51]. Mating behaviour receives increased attention as a result of the proposed releases of sterile insects (SIT) through irradiation or genetic engineering whereby mate finding and competition are crucial for rapid spread in wild populations [5256]. Furthermore, successful implementation of SIT requires a large production of sterile males for which insights in mating behaviour can be used to optimize rearing conditions and boost mosquito colonies [57, 58]. Attempts to study the behaviour of nocturnal males using a camera and infrared (IR) light date back to 1974, when it was shown that male responsiveness is closely related to their circadian rhythm [59]. Butail & Manoukis et al. [6062] were the first to record, analyse and discern interactions between wild mating swarms of male Anopheles gambiae. On a different scale, mating behaviour has been studied in confined areas using tethered mosquitoes [32, 63, 64] and follow-up studies in larger arenas with untethered Culex [65] and Anopheles mosquitoes [66]. These behavioural studies provided further evidence how acoustic signals play a role during mate finding and courtship rather than being an epiphenomenon [67]. Exploiting the flight tones to which males respond opens a new array of techniques to either enhance or disrupt mating success or to capture the mosquitoes for monitoring purposes or as a control tool [57, 6870].

Host-seeking

Behavioural research on mosquitoes is dominated by studies on host-seeking. This is not surprising, given the direct link with mosquito nuisance and the mosquito capabilities of vectoring human and animal pathogens. Finding a suitable blood host is critical for the uptake of protein needed for vitellogenesis. During host-seeking, mosquitoes make use of multiple host-derived cues [36, 7173] and flight strategies vary among different species [74]. Host preference and feeding habits are the main drivers for these different strategies [33].

Studies on the host-seeking behaviour of mosquitoes focus on the role of specific cues and, ideally, how the integration with other cues drives their orientation. Because of the multi-modal integration, the exact role of a single cue is difficult to determine [72]. Over the years, extensive behavioural studies on the role of CO2, heat, visual cues and specific host odours relevant for host preference have been performed. Outcomes of such studies were often quantified based on responses to a trap and/or by personal observations [7579]. However, insects display a sequence of behaviours before ending up inside a trap and responses to specific cues that initiate attraction or landing may be missed [77]. With the development of digital tracking techniques, more detailed studies can now be performed, allowing links to be made between detailed in-flight behaviours and a combination of host cues [71, 72, 8082]. The spatial scale at which such studies are performed is strongly correlated with the host cues of interest. The role of heat or the effects of contact repellents, for example, are studied in confined spaces such as small insect cages or wind tunnels [80, 8285]. Responses to CO2 in host-seeking activation, source localization and host finding are, on the other hand, studied not only in cages and wind tunnels, but also in (semi-) field settings [72, 81, 86].

We distinguish between fundamental behavioural studies that focus on the host cues (mainly volatiles) that mosquitoes can encounter in the wild, and the cues that may affect these behaviours as a result of interventions. Understanding mosquito responses to host cues encountered under natural conditions is an important prerequisite for the correct interpretation of responses to synthetic attractants and/or repellents. Tracking mosquito behaviour to study the effect of (synthetic) attractants, repellents, insecticide-impregnated bed nets or variations in house improvements has shown its relevance in understanding and improving vector-host interventions [21, 22, 8790].

Behavioural resistance to toxicants is relevant for estimating the effectiveness of interventions with LLINs or IRS. Such behavioural effects cannot be measured with standard WHO susceptibility tests, whereas behavioural data obtained with mosquito-tracking tools can be included in predictive models for the effectiveness of control strategies [91, 92].

Oviposition

Responses to oviposition cues depend on the physiological state of the mosquito [73, 93, 94]. Female mosquitoes show a temporary absence in behavioural response to host cues when they are fully engorged with blood [95]. After maturation of the eggs, oviposition cues take over and responses to host cues are restored after the eggs have been deposited [95, 96]. At the oviposition site, Anopheles and Culex species have been observed to hover above the water and make repeated descents, whereas these behaviours are absent in Ae. aegypti [97].

Finding and choosing a suitable breeding site is crucial for gravid female mosquitoes. The water body should retain water long enough for full development of the larvae, contain sufficient nutrients and preferably be free of predators [98, 99]. Disrupting this process of oviposition site selection may be highly beneficial for the implementation of (larval) control methods, either by luring the females to a less-favourable breeding site, e.g. one that is treated with a larvicide, or by luring it into oviposition traps [100103]. Efficient lures could also benefit the development of monitoring tools [104]. Responses to chemosensory cues that are involved in oviposition-site selection are, however, highly species-specific [105]. In addition to the chemical components, visual cues also affect site selection [106, 107]. The success rate of ‘attract-and-kill’ methods can benefit from behavioural observations to unravel the range at which (chemical) cues can mediate the desired behaviour [108, 109]. Interestingly, oviposition behaviour may also be affected indirectly, for example following the exposure to spatial repellents at an earlier stage of the mosquito life-cycle [110].

Behavioural observations on actual egg laying are limited and most studies focus on the ‘end result’ for choice assays by counting egg distributions in response to certain oviposition cues [111113].

Larval behaviour

Mosquito larvae are bounded by the breeding site in which they hatched. Their development and survival depend on factors such as water temperature as well as on intra- and inter-species competition over food and space, cannibalism and predation [114116]. Behavioural responses of larvae can be triggered by direct physical contact or water movement, fluctuations in light intensity (phototaxis), temperature (thermokinetic) or by chemical stimuli (reviewed by Clements [50]). Recently, responses to chemical cues by larvae have been studied to further unravel the olfactory signalling pathway of mosquitoes [34]. 2D tracking of larvae in a Petri dish demonstrated opposite behaviours when exposed to DEET versus yeast. As a follow-up, the thermosensory pathways and larval responses of mosquitoes were studied using a similar setup [35].

Behavioural analyses of mosquito larvae are few, but the previously mentioned studies have improved our understanding of sensory response mechanisms in adult mosquitoes [34, 35] and can contribute to model systems for community ecology studies [115].

Parasite and pathogen mediated mosquito behaviour

Parasite and/or pathogen infections can affect the energy budget and (flight-) behaviour of mosquitoes and this has consequences for their vector competence [117119]. In fact, parasites and pathogens can manipulate mosquito behaviour directly because of the effects of the infection on the mosquito and indirectly through the cues emitted from the infected hosts [120124]. Working with infected mosquitoes comes with an extra experimental challenge, as the study organisms should not pose a risk to the local environment and their inhabitants. A number of controlled studies demonstrated the role of mosquito infections and how behaviours may be affected to enhance further transmission of the pathogen [125130]. Studies with Plasmodium infections have demonstrated altered feeding behaviours of mosquitoes [131]. Although there is growing evidence that parasite/pathogen-infected mosquitoes express a modified behavioural response, data on actual flight performance of infected mosquitoes are scarce [132134].

Infections with entomopathogenic fungi used as a biopesticide, whether or not in combination with Plasmodium infections, have also demonstrated to have an impact on flight behaviour and host location [135139].

On another level, Wolbachia infections, which have the potential to disrupt pathogen transmission cycles, have been shown to affect mating success, probing behaviour and increased locomotor activities in Ae. aegypti mosquitoes [140144].

Insect flight tracking: the state of the art

Image tracking

Mosquito flight recordings were initially processed manually, by describing or quantifying the recorded behaviours [59, 78, 145]. More accurate quantification was conducted by manually digitizing the recorded images: a laborious task, pointing the subject of interest (the mosquito) frame-by-frame. For 3D analysis, the process had to be done twice with a minimum of 2 × 25 frames per second (fps), requiring 50 mouse-clicks for one second of video recording [24]. Machine-vision technology has developed rapidly and with the increase of computer power, development of high-resolution cameras, the possibilities for automated tracking of mosquito behaviour became available [146]. In short, the technology starts with acquiring images from cameras which are processed using specific thresholds to identify the object (insect) of interest. The thresholds mainly rely on differences in contrast of the image. Calibration of the set-up is critical, especially if data points from two different cameras will be merged for 3D reconstruction of the flight paths. Generally, the direct linear transformation (DLT) method is used [147], in which sets of 2D coordinates obtained from the cameras are linked to the known 3D coordinates of markers on a calibration frame. Depending on the lens type used, lens distortion correction is needed. To date, software with codes for automated tracking can be found online as freeware [148150]. Other software packages that cover automated tracking, and sometimes 3D reconstruction, are mostly custom-made solutions and some have evolved to an add-on tool next to commercially available motion analysis systems (idTracker, Trackit 3D, Ethovision Track3D) [27, 151, 152]. Overall, the technical development has led to a variety of tracking systems to choose from, a reduction in costs and an increase in the portability of recording set-ups. Advantages and disadvantages of quantification techniques have recently been summarized in a non-exhaustive list by Poh et al. [153]. It is remarkable that the table lists a variety of techniques that are described as either ‘complex to set up’ or ‘cover a small area of observation’ indicating that there is still much room for improvement. Aspects that need to be taken into account to improve tracking systems are discussed below.

Lighting and contrast

Successful tracking relies on a clear contrast between the object of interest and the background. The insect is either illuminated against a dark background, or it is depicted against a brighter background. Light conditions of the experimental set-up should not interfere with the natural day-night rhythm of the studied mosquito. For An. gambiae, it has been suggested that filming with infrared light at λ > 900 nm does not alter the visual environment of the mosquito [154, 155]. Filming at night requires near-infrared lighting so that the reflection of the light on the mosquito wing is caught by IR-sensitive cameras and is visible against a dark background. A drawback here is that most background materials/environments also reflect the IR-light, causing unwanted bright areas that make tracking difficult or impossible. The use of black polycarbonate as a background solves this problem [80]. Black fibreglass netting and ‘blackboard paint’ can serve as alternatives, especially when the contrast of uneven surface areas has to be improved. Another approach is to backlight the cameras so that the mosquito appears as a silhouette in the recorded image [22, 155]. Careful evaluation of the experimental requirements is needed to select optimal lighting solutions. This can be extra-challenging under (semi-) field conditions.

Multiple object tracking

From the start of the development of automated tracking techniques, scientists searched for solutions to track multiple objects. Occultations are a challenge and in the case of mosquito flight studies, confined arenas are needed to prevent individuals from flying out of sight, with the risk of being confused with other individuals upon returning to the arena. The best tracking results are currently established with tailor-made (MATLAB) tracking codes [60, 72, 151]. Angarita-Jaimes et al. [156] extended the volume of their imaging system by connecting two cameras. This system is able to track multiple mosquitoes within 2.0 × 1.2 × 2.0 m, where it should be noted that the presented parameters on mosquito velocity are based on 2D data. Another way to enlarge the area of interest is by using pan-tilt cameras [27, 157]. By adding extra cameras, provided that they are properly synchronized, occultations can be reduced and additional accuracy and details of flight tracks can be obtained.

Track the beat

Another method of ‘keeping track’ of mosquitoes is by exploiting their wing beats. When the sound that mosquito wings produce is used to record the activity or passage of mosquitoes, we refer to this as acoustic tracking. Literature on responses of mosquitoes to sound is scarce, but dates back to 1949 [158]. Acoustic tracking has the potential to be used as a relatively cheap method to monitor behavioural activity of mosquitoes. Sound sensors are generally cheaper than image sensors and there is no additional lighting equipment required. Also, the amount of incoming data is smaller compared to image tracking. Differences in wingbeat frequencies can be used to classify individual mosquitoes to species complex level; however, overlap in frequencies does occur [159161]. Field tests report a major challenge in filtering ambient noise [162], although a recent study using mobile phones as acoustic sensors managed to filter this noise and mosquito identification was succesful [163]. More promising seems the development of opto-acoustic tracking, making use of break-beam technology. When mosquitoes pass a set of infrared emitters and receivers, the interrupted beam is used as an electronic signal by the receiver [164, 165]. Species identification using this technique is challenging, but possible by analysing wingbeat patterns and proof of principle was already reported in 1986 [166, 167]. Automated identification is now reported based on wing movement or wing shape- and pattern analysis and accuracy levels are on the rise [168172].

Mosquito behaviour at a glance

Using tracking technology as discussed above, several studies have described the behaviour of mosquitoes during different life stages. We are not aware of studies on oviposition behaviour or plant feeding that have incorporated tracking techniques, but such studies could give additional information on the approach strategies of mosquitoes and measure responses to the provided cues in a (semi-) natural environment [31, 99]. Butail et al. [60] created a 3D reconstruction of a mating event of wild An. gambiae mosquitoes, a critical step to characterise the trajectories of both male and female mosquitoes before successful mating occurs. Tracking the behaviour of larvae revealed that thermosensory responses are comparable to those of adult mosquitoes, which may have indirect implications for adult mosquito vectorial capacity, triggered at the larval stage [35]. The same technique was used to demonstrate that larvae show an altered behaviour after exposure to the mosquito repellent DEET [34]. In studies on host-seeking female mosquitoes, Spitzen et al. [80] and McMeniman et al. [72] described how responses to specific host cues are integrated and evoke completely different flight patterns, compared to treatments where cues such as heat or CO2 are lacking, or cannot be sensed by mutant mosquitoes. Several studies incorporated mosquito-behavioural analysis in more applied settings directed at interventions with mosquitoes as target. These studies focus on the approach of mosquitoes to host cues in different settings. The obtained information is used to evaluate the effects of the intervention technique [22, 24], or to provide ideas on how and where to implement interventions [90, 136]. The parameters selected to interpret the behaviours of interest show many similarities across studies. Spatial-temporal distributions reveal the relative attractiveness, and thus importance, of the (host-) cues studied. The change in velocity of the insect is closely related to the distance from and intensity of the stimulus, and is a measure of orthokinesis. In addition, the velocity parameter is used as an indicator of the insect nearing mechanical barriers, e.g. when mosquitoes approach a house, bed net or host. Change of headings, the intensity of convoluted pathways, sometimes expressed as the number of turns, are closely related parameters and linked to whether mosquitoes are, or have been, tracking odour cues. At close range, locomotor activity or mobility thresholds can be used to determine whether a mosquito is still in flying modus or has landed and is sitting on or near the target. This parameter is also valuable in 2D tracking systems where accurate estimates of flight velocities are lacking. Setting the mobility threshold should be carefully evaluated, as automated tracking can also produce ‘movements’ while the insect is actually sitting still and the deviation is caused by slight shifts in illuminated pixels [21]. The latter can cause interpretation errors, especially when filming under low resolution.

Data evaluation and considerations before taking off

The tracking data that are generated need careful evaluation before any meaningful interpretations of the observed behaviours can be made. Occasional tracking errors, for example caused by reflections of IR on interfering objects, should be filtered out to avoid inaccurate data on flight parameters. Such filters can be based on previously-reported maximum flight velocities. Missing data, for example caused by lack of resolution or lighting conditions, can be fixed using interpolation. This avoids unnecessary cuts in tracks that would otherwise be appointed to multiple individuals or flight events. Data interpolation seems especially acceptable for set-ups where more than one camera is used, when missing data is only replaced in cases where the other camera ‘confirms’ the actual continuation of the track [24, 80].

Analysing the average value of measured flight parameters over an entire track may not explain the observed behaviour correctly. For example, the flight speed of host-seeking mosquitoes can drastically change when they lose track of an odour or when they come close to their target [24, 80, 84]. This becomes apparent when the data are divided in sections or bins with certain increments from the target stimuli. With conventional recording speeds (25 fps), however, the number of data points within these smaller sections is limited; this has consequences for the power of the statistical analysis. Recording with higher frame rates solves this problem. For multiple object tracking, high frame rates help to minimize the likelihood of incorrect assignments of individual mosquitoes, as the typical mosquito displacement between frames becomes smaller than the spacing between two individuals [156]. High-speed recording, however, comes with other limitations since it requires extra illumination, computer processing speed and data storage.

Filming with high-resolution cameras at high frame rates generates large data files. When recording gigabytes per minute, data storage becomes the limiting factor for the duration of the behavioural experiment. Real-time tracking would be the solution here, where ‘only’ the x,y, (z) positions of the insect are stored for further analysis and not the video itself. However, the tracking code used should be verifiable using sample videos [173, 174].

Future perspectives

Integration of spatial observations with fundamental studies on flight dynamics

Recent studies on the flight kinematics and aerodynamics of mosquitoes, together with the spatial data reviewed above, throw new light on the way mosquitoes move through their environment [29, 175, 176]. Compared to other similarly-sized insects, mosquitoes fly with exceptionally high wing beat frequencies and low amplitudes [175], and after taking a blood meal they can escape from their host without being noticed [176]. Although studying the aerodynamics of mosquito flight often requires an experimental set-up that is highly confined in space, it reveals the physical possibilities and limitations of mosquito flight. Integrating the technical expertise and obtained knowledge on biomechanics with the expanding information on flight behaviours observed in the (semi-) field can accelerate the innovation of vector control tools, for example by fine-tuning fan-powered traps or manipulation of mating behaviour.

How to exploit mosquito behaviour for surveillance and intervention

With the rapid development and increased availability of tracking hardware and (open-source) software, scientists should consider what system(s) are required to answer their research question(s). This seems obvious, but in the field of mosquito research we distinguish between a focus on fundamental questions on flight behaviour and a more applied approach, where flight data are used for innovation purposes, surveillance, or measuring the effectiveness of intervention strategies. For the applied questions, 2D data are often sufficient and this can drastically reduce the budget required, decrease the amount of incoming raw data and thereby minimize the time required for answering the initial question. For example studies analysing number of landings, or amount of time spent on (insecticide-) treated surfaces, or whether mosquitoes pass certain intervention barriers can benefit from 2D tracking solutions [21, 87, 177]. However, when detailed data on the approach of mosquitoes to certain targets, or responses to particular cues is requested, 3D data are a prerequisite because of their typical convoluted flight patterns, both horizontally and vertically [72, 80].

Our understanding of behavioural repertoires of mosquito vectors during their life-cycle, whether infected or not, can be exploited for the development and effective implementation of novel monitoring and control tools (e.g. [16, 133, 178]). Behavioural data can be added to data obtained from conventional monitoring tools such as traps, resting catches and human landing collections, in order to validate models on malaria transmission [179, 180]. Tracking techniques can be combined with trapping techniques or even replace traps as a surveillance tool to assess mosquito abundance or activity. For the monitoring of species abundance, or measuring the activity of mosquitoes around human dwellings, there is no need to analyse the full behavioural repertoire. The break-beam technology that exploits specific wing beat characteristics can be advantageous here, especially when combined with a timer to register circadian rhythms [164, 165, 171, 181]. Whereas the use of microphones seems inapplicable for larger arenas because of background noise, the use of opto-acoustics to measure activity and classify mosquitoes that enter houses (e.g. via eaves or eave tubes) or traps may provide an easier-to-install and cheaper method than filming. Even if species recognition is imperfect, the technique can still provide data on flight activity on a temporal scale. Given their size, it is unlikely that (harmonic) radar systems will become available to follow long-range movements of mosquitoes [182184]. Aerial sampling with balloons at 40–250 m above ground level revealed that a significant number of mosquitoes exploit wind streams to migrate at high altitudes (Tovi Lehmann, personal communication). Such studies can help to explain seasonal (re-) appearance of mosquito species and predict the need for continuous vector control measures. Mark-recapture studies form another alternative to measure (migration) movements, with the challenge being to capture the wild individual in the first place so that it can be marked [185]. The latter can be solved by using the stable isotope method, with which larvae can be marked and re-captured as adults [186]. Mark-recapture studies will not reveal details on movements between point A and B, but can give valuable information on mosquito dispersal.

Conclusions

The implementation of vector intervention tools, as part of the Global Vector Control Response strategy launched by the WHO [20], comes along with questions on the effectiveness and possible behavioural adaptations of mosquitoes to such tools. For example, the shift towards outdoor transmission by behaviourally resistant mosquitoes requires adjustments in intervention techniques [180]. Recent developments in insect tracking technology are promising for further implementation in the field and are expected to provide the necessary feedback and estimations of mosquitoes that pass through holes, eaves, windows and doors [89, 90, 187, 188]. Interventions may have an effect on airflow coming from houses and quantifying this requires particle tracking of airflows, as reviewed by Fu et al. [189]. More directly, manipulating the airflow using fog dispensers has shown to affect the flight capabilities of mosquitoes and could function as an intervention tool in itself [190]. To date, automated tracking techniques have contributed to our understanding of how the multimodal integration of (host-) cues plays a role in source finding of mosquitoes [72, 80]. The tools have provided information on attraction or repellent modes by studying mosquito behaviour around treated surfaces such as bed nets [21, 22, 191, 192] or around odour-baited traps [24]. Recently, the obtained knowledge led to promising implementations in the field where adding a heat source to an odour-baited trap resulted in a 6.5 fold increase of trap catches of An. gambiae compared to traps without heat [193].

Abbreviations

2D: 

two-dimensional

3D: 

three-dimensional

DEET: 

n,n-diethyl-meta-toluamide

DLT: 

direct linear transformation

fps: 

frames per second

IR: 

infrared

IRS: 

indoor residual spraying

LLINs: 

long-lasting insecticide-treated nets

SIT: 

sterile insect technique

WHO: 

World Health Organization

Declarations

Acknowledgments

We thank Florian Muijres, Martin Lankheet, Kees Spoor and colleagues from the Experimental Zoology Group, Wageningen University and Research for discussing and developing automated-tracking solutions. The valuable suggestions offered by Nakul Chitnis of the Swiss Tropical and Public Health Institute and by Tovi Lehmann of the NIAID, NIH, USA are much appreciated. We thank Marcel Dicke for his comments on previous versions of the manuscript. Two anonymous reviewers are acknowledged for their suggestions to improve the manuscript.

Funding

Not applicable.

Availability of data and materials

Not applicable.

Authors’ contributions

JS and WT discussed the contents of the review. JS wrote the first draft. WT revised the manuscript. Both authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors collaborated in the development of Track3D, however this did not affect the content of the current manuscript.

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Authors’ Affiliations

(1)
Laboratory of Entomology, Wageningen University and Research, PO Box 16, 6700 AA Wageningen, The Netherlands

References

  1. Murray CJL, Rosenfeld LC, Lim SS, Andrews KG, Foreman KJ, Haring D, et al. Global malaria mortality between 1980 and 2010: a systematic analysis. Lancet. 2012;379(9814):413–31.PubMedView ArticleGoogle Scholar
  2. WHO-RBM. Action and investment to defeat malaria 2016–2030. For a malaria-free world. Geneva: WHO; 2015. p. 97.Google Scholar
  3. Benelli G, Mehlhorn H. Declining malaria, rising of dengue and Zika virus: insights for mosquito vector control. Parasitol Res. 2016;115(5):1747–54.PubMedView ArticleGoogle Scholar
  4. Saiz JC, Vázquez-Calvo Á, Blázquez AB, Merino-Ramos T, Escribano-Romero E, Martín-Acebes MA. Zika virus: The latest newcomer. Front Microbiol. 2016;7:496.PubMedPubMed CentralGoogle Scholar
  5. Caminade C, Kovats S, Rocklov J, Tompkins AM, Morse AP, Colón-González FJ, et al. Impact of climate change on global malaria distribution. Proc Natl Acad Sci USA. 2014;111(9):3286–91.Google Scholar
  6. Rezza G. Dengue and chikungunya: long-distance spread and outbreaks in naïve areas. Pathog Glob Health. 2014;108(8):349–55.PubMedPubMed CentralView ArticleGoogle Scholar
  7. Medlock JM, Leach SA. Effect of climate change on vector-borne disease risk in the UK. Lancet Infect Dis. 2015;15(6):721–30.PubMedView ArticleGoogle Scholar
  8. Kilpatrick AM. Globalization, land use, and the invasion of West Nile virus. Science. 2011;334(6054):323–7.PubMedPubMed CentralView ArticleGoogle Scholar
  9. Imperato PJ. The convergence of a virus, mosquitoes, and human travel in globalizing the Zika epidemic. J Community Health. 2016;41(3):674–9.PubMedView ArticleGoogle Scholar
  10. Benelli G, Jeffries CL, Walker T. Biological control of mosquito vectors: Past, present, and future. Insects. 2016;7(4):52.PubMed CentralView ArticleGoogle Scholar
  11. WHO. World malaria report 2017. Geneva: WHO; 2017.Google Scholar
  12. Scholte EJ, Ng'habi K, Kihonda J, Takken W, Paaijmans K, Abdulla S, et al. An entomopathogenic fungus for control of adult African malaria mosquitoes. Science. 2005;308(5728):1641–2.PubMedView ArticleGoogle Scholar
  13. Boyce R, Lenhart A, Kroeger A, Velayudhan R, Roberts B, Horstick O. Bacillus thuringiensis israelensis (Bti) for the control of dengue vectors: systematic literature review. Tropical Med Int Health. 2013;18(5):564–77.Google Scholar
  14. Dambach P, Louis VR, Kaiser A, Ouedraogo S, Sié A, Sauerborn R, et al. Efficacy of Bacillus thuringiensis var. israelensis against malaria mosquitoes in northwestern Burkina Faso. Parasit Vectors. 2014;7:371.PubMedPubMed CentralView ArticleGoogle Scholar
  15. Menger DJ, Omusula P, Holdinga M, Homan T, Carreira AS, Vandendaele P, et al. Field evaluation of a push-pull system to reduce malaria transmission. PLoS One. 2015;10(4):e0123415.PubMedPubMed CentralView ArticleGoogle Scholar
  16. Homan T, Hiscox A, Mweresa CK, Masiga D, Mukabana WR, Oria P, et al. The effect of mass mosquito trapping on malaria transmission and disease burden (SolarMal): a stepped-wedge cluster-randomised trial. Lancet. 2016;388(10050):1193–201.PubMedView ArticleGoogle Scholar
  17. Tusting LS, Willey B, Lines J. Building malaria out: improving health in the home. Malar J. 2016;15(1):1–3.View ArticleGoogle Scholar
  18. WHO. Handbook for integrated vector management. Geneva: WHO; 2012.Google Scholar
  19. Alonso P, Engels D, Reeder J. Renewed push to strengthen vector control globally. Lancet. 2017;389(10086):2270–1.PubMedView ArticleGoogle Scholar
  20. WHO. Global vector control response 2017–2030. Geneva: WHO; 2017.Google Scholar
  21. Spitzen J, Ponzio C, Koenraadt CJM, Pates Jamet HV, Takken W. Absence of close-range excitorepellent effects in malaria mosquitoes exposed to deltamethrin-treated bed nets. Am J Trop Med Hyg. 2014;90(6):1124–32.PubMedPubMed CentralView ArticleGoogle Scholar
  22. Parker JEA, Angarita-Jaimes N, Abe M, Towers CE, Towers D, McCall PJ. Infrared video tracking of Anopheles gambiae at insecticide-treated bed nets reveals rapid decisive impact after brief localised net contact. Sci Rep. 2015;5:13392.PubMedPubMed CentralView ArticleGoogle Scholar
  23. Matowo NS, Koekemoer LL, Moore SJ, Mmbando AS, Mapua SA, Coetzee M, et al. Combining synthetic human odours and low-cost electrocuting grids to attract and kill outdoor-biting mosquitoes: field and semi-field evaluation of an improved mosquito landing box. PLoS One. 2016;11(1):e0145653.PubMedPubMed CentralView ArticleGoogle Scholar
  24. Cooperband MF, Cardé RT. Orientation of Culex mosquitoes to carbon dioxide-baited traps: flight manoeuvres and trapping efficiency. Med Vet Entomol. 2006;20(1):11–26.PubMedView ArticleGoogle Scholar
  25. Hiscox A, Otieno B, Kibet A, Mweresa CK, Omusula P, Geier M. Development and optimization of the Suna trap as a tool for mosquito monitoring and control. Malar J. 2014;13:257.PubMedPubMed CentralView ArticleGoogle Scholar
  26. Killeen GF, Chitnis N, Moore SJ, Okumu FO. Target product profile choices for intra-domiciliary malaria vector control pesticide products: repel or kill? Malar J. 2011;10:207.PubMedPubMed CentralView ArticleGoogle Scholar
  27. Fry SN, Bichsel M, Müller P, Robert D. Tracking flying insects using pan-tilt cameras. J Neurosci Methods. 2000;10:59–67.View ArticleGoogle Scholar
  28. Kröner C, Towers CE, Angarita-Jaimes N, Parker JEA, McCall P, Towers DP. 3D tracking of mosquitoes: A field compatible technique to understand malaria vector behaviour. In: Proceedings Imaging and Applied Optics 2016, Heidelberg, Germany. 2016.Google Scholar
  29. Dickerson AK, Shankles PG, Madhavan NM, Hu DL. Mosquitoes survive raindrop collisions by virtue of their low mass. Proc Natl Acad Sci USA. 2012;109(25):9822–7.Google Scholar
  30. Bernáth B, Anstett V, Guerin PM. Anopheles gambiae females readily learn to associate complex visual cues with the quality of sugar sources. J Insect Physiol. 2016;95:8–16.PubMedView ArticleGoogle Scholar
  31. Otienoburu PE, Nikbakhtzadeh MR, Foster WA. Orientation of Anopheles gambiae (Diptera: Culicidae) to plant-host volatiles in a novel diffusion-cage olfactometer. J Med Entomol. 2016;53(1):237–40.PubMedView ArticleGoogle Scholar
  32. Cator LJ, Ng'Habi KR, Hoy RR, Harrington LC. Sizing up a mate: Variation in production and response to acoustic signals in Anopheles gambiae. Behav Ecol. 2010;21(5):1033–9.View ArticleGoogle Scholar
  33. Takken W, Verhulst NO. Host preferences of blood-feeding mosquitoes. Annu Rev Entomol. 2013;58:433–53.PubMedView ArticleGoogle Scholar
  34. Liu C, Pitts RJ, Bohbot JD, Jones PL, Wang G, Zwiebel LJ. Distinct olfactory signaling mechanisms in the malaria vector mosquito Anopheles gambiae. PLoS Biol. 2010;8(8):e1000467.Google Scholar
  35. Liu C, Zwiebel LJ. Molecular characterization of larval peripheral thermosensory responses of the malaria vector mosquito Anopheles gambiae. PLoS One. 2013;8(8):e72595.PubMedPubMed CentralView ArticleGoogle Scholar
  36. Cardé RT, Gibson G. Host finding by female mosquitoes: mechanisms of orientation to host odours and other cues. In: Takken W, Knols BGJ, editors. Olfaction in vector-host interactions. Wageningen: Wageningen Academic Publishers; 2010.Google Scholar
  37. Lyimo IN, Ferguson HM. Ecological and evolutionary determinants of host species choice in mosquito vectors. Trends Parasitol. 2009;25(4):189–96.PubMedView ArticleGoogle Scholar
  38. Foster WF. Mosquito sugar feeding and reproductive energetics. Annu Rev Entomol. 1995;40:443–7.PubMedView ArticleGoogle Scholar
  39. Yuval B. The other habit: sugar feeding by mosquitoes. Bull Soc Vector Ecol. 1992;17(2):150–6.Google Scholar
  40. Nayar JK, van Handel W. The fuel for sustained mosquito flight. J Insect Physiol. 1971;17:471–81.View ArticleGoogle Scholar
  41. Klowden MJ. Effects of sugar deprivation on the host-seeking behaviour of gravid Aedes aegypti mosquitoes. J Insect Physiol. 1986;32:479–83.View ArticleGoogle Scholar
  42. Maïga H, Niang A, Sawadogo SP, Dabiré RK, Lees RS, Gilles JRL, et al. Role of nutritional reserves and body size in Anopheles gambiae males mating success. Acta Trop. 2014;132(1):S102–7.PubMedView ArticleGoogle Scholar
  43. Qualls WA, Müller GC, Traore SF, Traore MM, Arheart KL, Doumbia S, et al. Indoor use of attractive toxic sugar bait (ATSB) to effectively control malaria vectors in Mali, West Africa. Malar J. 2015;14(1):301.PubMedPubMed CentralView ArticleGoogle Scholar
  44. Müller GC, Beier JC, Traore SF, Toure MB, Traore MM, Bah S, et al. Successful field trial of attractive toxic sugar bait (ATSB) plant-spraying methods against malaria vectors in the Anopheles gambiae complex in Mali, West Africa. Malar J. 2010;9(1):210.PubMedPubMed CentralView ArticleGoogle Scholar
  45. Billingsley PF, Hodivala KJ, Winger LA, Sinden RE. Detection of mature malaria infections in live mosquitoes. Trans R Soc Trop Med Hyg. 1991;85:450–3.PubMedView ArticleGoogle Scholar
  46. Hall-Mendelin S, Ritchie SA, Johansen CA, Zborowski P, Cortis G, Dandridge S, et al. Exploiting mosquito sugar feeding to detect mosquito-borne pathogens. Proc Natl Acad Sci USA. 2010;107(25):11255–9.Google Scholar
  47. Inouye DW. Mosquitoes: More likely nectar thieves than pollinators. Nature. 2010;467(7311):27.PubMedView ArticleGoogle Scholar
  48. Peach DAH, Gries G. Nectar thieves or invited pollinators? A case study of tansy flowers and common house mosquitoes. Arthropod-Plant Interact. 2016;10(6):1–10.Google Scholar
  49. Downes JA. The swarming and mating flight of Diptera. Annu Rev Entomol. 1969;14:271–98.View ArticleGoogle Scholar
  50. Clements AN. The biology of mosquitoes, vol. II. Wallingford: CABI Publishers; 1999.Google Scholar
  51. Vanickova L, Canale A, Benelli G. Sexual chemoecology of mosquitoes (Diptera, Culicidae): current knowledge and implications for vector control programs. Parasitol Int. 2017;66(2):190–5.Google Scholar
  52. Achinko D, Thailayil J, Paton D, Mireji PO, Talesa V, Masiga D, et al. Swarming and mating activity of Anopheles gambiae mosquitoes in semi-field enclosures. Med Vet Entomol. 2016;30(1):14–20.PubMedView ArticleGoogle Scholar
  53. Patil PB, Niranjan Reddy BP, Gorman K, Seshu Reddy KV, Barwale SR, Zehr UB, et al. Mating competitiveness and life-table comparisons between transgenic and Indian wild-type Aedes aegypti L. Pest Manag Sci. 2015;71(7):957–65.PubMedView ArticleGoogle Scholar
  54. Diabate A, Tripet F. Targeting male mosquito mating behaviour for malaria control. Parasit Vectors. 2015;8:347.PubMedPubMed CentralView ArticleGoogle Scholar
  55. Carvalho DO, McKemey AR, Garziera L, Lacroix R, Donnelly CA, Alphey L, et al. Suppression of a field population of Aedes aegypti in Brazil by sustained release of transgenic male mosquitoes. PLoS Negl Trop Dis. 2015;9(7):e0003864.PubMedPubMed CentralView ArticleGoogle Scholar
  56. Harris AF, Nimmo D, McKemey AR, Kelly N, Scaife S, Donnelly CA, et al. Field performance of engineered male mosquitoes. Nature Biotech. 2011;29:1034–7.View ArticleGoogle Scholar
  57. Benelli G. Research in mosquito control: current challenges for a brighter future. Parasitol Res. 2015;114(8):2801–5.PubMedView ArticleGoogle Scholar
  58. Benelli G. The best time to have sex: mating behaviour and effect of daylight time on male sexual competitiveness in the Asian tiger mosquito, Aedes albopictus (Diptera: Culicidae). Parasitol Res. 2015;114(3):887–94.PubMedView ArticleGoogle Scholar
  59. Charlwood DJ. Infrared T.V. for watching mosquito behaviour in the ‘dark’. Trans R Soc Trop Med Hyg. 1974;68:264.PubMedGoogle Scholar
  60. Butail S, Manoukis N, Diallo M, Ribeiro JM, Lehmann T, Paley DA. Reconstructing the flight kinematics of swarming and mating in wild mosquitoes. J R Soc Interface. 2012;9(75):2624–38.PubMedPubMed CentralView ArticleGoogle Scholar
  61. Butail S, Manoukis NC, Diallo M, Ribeiro JMC, Paley DA. The dance of male Anopheles gambiae in wild mating swarms. J Med Entomol. 2013;50(3):552–9.PubMedPubMed CentralView ArticleGoogle Scholar
  62. Manoukis NC, Diabate A, Abdoulaye A, Diallo M, Dao A, Yaro AS, et al. Structure and dynamics of male swarms of Anopheles gambiae. J Med Entomol. 2009;46(2):227–35.PubMedPubMed CentralView ArticleGoogle Scholar
  63. Potamitis I, Rigakis I. Measuring the fundamental frequency and the harmonic properties of the wingbeat of a large number of mosquitoes in flight using 2D optoacoustic sensors. Appl Acoust. 2016;109:54–60.View ArticleGoogle Scholar
  64. Warren B, Gibson G, Russell IJ. Sex recognition through midflight mating duets in Culex mosquitoes is mediated by acoustic distortion. Curr Biol. 2009;19(6):485–91.PubMedView ArticleGoogle Scholar
  65. Simões PMV, Ingham RA, Gibson G, Russell IJ. A role for acoustic distortion in novel rapid frequency modulation behaviour in free-flying male mosquitoes. J Exp Biol. 2016;219(13):2039–47.PubMedView ArticleGoogle Scholar
  66. Simões PMV, Gibson G, Russell IJ. Pre-copula acoustic behaviour of males in the malarial mosquitoes Anopheles coluzzii and Anopheles gambiae s.s. does not contribute to reproductive isolation. J Exp Biol. 2017;220(3):379–85.Google Scholar
  67. Gibson G, Warren B, Russell IJ. Humming in tune: Sex and species recognition by mosquitoes on the wing. J Assoc Res Otolaryngol. 2010;11(4):527–40.PubMedPubMed CentralView ArticleGoogle Scholar
  68. Balestrino F, Iyaloo DP, Elahee KB, Bheecarry A, Campedelli F, Carrieri M, et al. A sound trap for Aedes albopictus (Skuse) male surveillance: response analysis to acoustic and visual stimuli. Acta Trop. 2016;164:448–54.Google Scholar
  69. Johnson BJ, Ritchie SA. The Siren's song: exploitation of female flight tones to passively capture male Aedes aegypti (Diptera: Culicidae). J Med Entomol. 2016;53(1):245–8.Google Scholar
  70. Stone CM, Tuten HC, Dobson SL. Determinants of male Aedes aegypti and Aedes polynesiensis (Diptera: Culicidae) response to sound: Efficacy and considerations for use of sound traps in the field. J Med Entomol. 2013;50(4):723–30.Google Scholar
  71. Cardé RT. Multi-Cue Integration: How female mosquitoes locate a human host. Curr Biol. 2015;25(18):R793–5.PubMedView ArticleGoogle Scholar
  72. McMeniman CJ, Corfas RA, Matthews BJ, Ritchie SA, Vosshall LB. Multimodal integration of carbon dioxide and other sensory cues drives mosquito attraction to humans. Cell. 2014;156(5):1060–71.Google Scholar
  73. Takken W, Knols BGJ. Odor-mediated behavior of Afrotropical malaria mosquitoes. Annu Rev Entomol. 1999;44:131–57.PubMedView ArticleGoogle Scholar
  74. Cardé RT, Willis MA. Navigational strategies used by insects to find distant, wind-borne sources of odor. J Chem Ecol. 2008;34(7):854–66.PubMedView ArticleGoogle Scholar
  75. Gillies MT. Methods for assessing the density and survival of blood-sucking Diptera. Annu Rev Entomol. 1974;19:345–62.Google Scholar
  76. Gillies MT, Wilkes TJ. Trapping host-seeking mosquitoes with electrocuting grids. Trans R Soc Trop Med Hyg. 1981;75(4):600–1.Google Scholar
  77. Gibson G, Torr SJ. Visual and olfactory responses of haematophagous Diptera to host stimuli. Med Vet Entomol. 1999;13(1):2–23.PubMedView ArticleGoogle Scholar
  78. Healy TP, Copland MJW. Activation of Anopheles gambiae mosquitoes by carbon dioxide and human breath. Med Vet Entomol. 1995;9(3):331–6.PubMedView ArticleGoogle Scholar
  79. Klun JA, Kramer M, Debboun M. Four simple stimuli that induce host-seeking and blood-feeding behaviors in two mosquito species, with a clue to DEET's mode of action. J Vector Ecol. 2013;38(1):143–53.PubMedView ArticleGoogle Scholar
  80. Spitzen J, Spoor CW, Grieco F, ter Braak C, Beeuwkes J, van Brugge SP, et al. A 3D analysis of flight behavior of Anopheles gambiae sensu stricto malaria mosquitoes in response to human odor and heat. PLoS One. 2013;8(5):e62995.PubMedPubMed CentralView ArticleGoogle Scholar
  81. Lacey ES, Ray A, Cardé RT. Close encounters: contributions of carbon dioxide and human skin odour to finding and landing on a host in Aedes aegypti. Physiol Entomol. 2014;39(1):60–81.PubMedPubMed CentralView ArticleGoogle Scholar
  82. Breugel F, Riffell J, Fairhall A, Dickinson MH. Mosquitoes use vision to associate odor plumes with thermal targets. Curr Biol. 2015;25(16):2123–9.PubMedPubMed CentralView ArticleGoogle Scholar
  83. Healy TP, Copland MJ, Cork A, Przyborowska A, Halket JM. Landing responses of Anopheles gambiae elicited by oxocarboxylic acids. Med Vet Entomol. 2002;16(2):126–32.PubMedView ArticleGoogle Scholar
  84. Lacey ES, Cardé RT. Activation, orientation and landing of female Culex quinquefasciatus in response to carbon dioxide and odour from human feet: 3-D flight analysis in a wind tunnel. Med Vet Entomol. 2011;25(1):94–103.PubMedView ArticleGoogle Scholar
  85. Menger DJ, Van Loon JJA, Takken W. Assessing the efficacy of candidate mosquito repellents against the background of an attractive source that mimics a human host. Med Vet Entomol. 2014;28(4):407–13.PubMedView ArticleGoogle Scholar
  86. Lorenz LM, Keane A, Moore JD, Munk CJ, Seeholzer L, Mseka A, et al. Taxis assays measure directional movement of mosquitoes to olfactory cues. Parasit Vectors. 2013;6:131.PubMedPubMed CentralView ArticleGoogle Scholar
  87. Cooperband MF, Allan SA. Effects of different pyrethroids on landing behavior of female Aedes aegypti, Anopheles quadrimaculatus, and Culex quinquefasciatus mosquitoes (Diptera: Culicidae). J Med Entomol. 2009;46(2):292–306.PubMedView ArticleGoogle Scholar
  88. Miller JE, Gibson G. Behavioral response of host-seeking mosquitos (Diptera, Culicidae) to insecticide-impregnated bed netting - a new approach to insecticide bioassays. J Med Entomol. 1994;31(1):114–22.PubMedView ArticleGoogle Scholar
  89. Spitzen J, Koelewijn T, Mukabana WR, Takken W. Effect of insecticide-treated bed nets on house-entry by malaria mosquitoes: the flight response recorded in a semi-field study in Kenya. Acta Trop. 2017;172:180–5.Google Scholar
  90. Spitzen J, Koelewijn T, Mukabana WR, Takken W. Visualization of house-entry behaviour of malaria mosquitoes. Malar J. 2016;15(1):1–10.View ArticleGoogle Scholar
  91. Briët OJ, Penny MA, Hardy D, Awolola TS, Van Bortel W, Corbel V, et al. Effects of pyrethroid resistance on the cost effectiveness of a mass distribution of long-lasting insecticidal nets: a modelling study. Malar J. 2013;12(1):77.PubMedPubMed CentralView ArticleGoogle Scholar
  92. Stone C, Chitnis N, Gross K. Environmental influences on mosquito foraging and integrated vector management can delay the evolution of behavioral resistance. Evol Appl. 2016;9(3):502–17.PubMedPubMed CentralView ArticleGoogle Scholar
  93. Qiu Y-T, Gort G, Torricelli R, Takken W, van Loon JJA. Effects of blood-feeding on olfactory sensitivity of the malaria mosquito Anopheles gambiae: Application of mixed linear models to account for repeated measurements. J Insect Physiol. 2013;59(11):1111–8.PubMedView ArticleGoogle Scholar
  94. Afify A, Horlacher B, Roller J, Galizia CG. Different repellents for Aedes aegypti against blood-feeding and oviposition. PLoS One. 2014;9(7):e103765.PubMedPubMed CentralView ArticleGoogle Scholar
  95. Takken W, Van Loon JJA, Adam W. Inhibition of host-seeking response and olfactory responsiveness in Anopheles gambiae following blood feeding. J Insect Physiol. 2001;47(3):303–10.PubMedView ArticleGoogle Scholar
  96. Davis EE. Regulation of sensitivity in the peripheral chemoreceptor systems for host-seeking behaviour by a haemolymph-borne factor in Aedes aegypti. J Insect Physiol. 1984;30:179–83.Google Scholar
  97. Kennedy JS. On water-finding and oviposition by captive mosquitoes. Bull Entomol Res. 1942;32:279–301.View ArticleGoogle Scholar
  98. Wondwosen B, Birgersson G, Seyoum E, Tekie H, Torto B, Fillinger U, et al. Rice volatiles lure gravid malaria mosquitoes, Anopheles arabiensis. Sci Rep. 2016;6:37930.PubMedPubMed CentralView ArticleGoogle Scholar
  99. Suh E, Choe DH, Saveer AM, Zwiebel LJ. Suboptimal larval habitats modulate oviposition of the malaria vector mosquito Anopheles coluzzii. PLoS One. 2016;11(2):e0149800.PubMedPubMed CentralView ArticleGoogle Scholar
  100. Mboera LE, Takken W, Mdira KY, Pickett JA. Sampling gravid Culex quinquefasciatus (Diptera: Culicidae) in Tanzania with traps baited with synthetic oviposition pheromone and grass infusions. J Med Entomol. 2000;37(1):172–6.PubMedView ArticleGoogle Scholar
  101. Kartzinel MA, Alto BW, Deblasio MW, Burkett-Cadena ND. Testing of visual and chemical attractants in correlation with the development and field evaluation of an autodissemination station for the suppression of Aedes aegypti and Aedes albopictus in Florida. J Am Mosq Control Assoc. 2016;32(3):194–202.PubMedView ArticleGoogle Scholar
  102. Quiroz-Martinez H, Garza-Rodriguez MI, Trujillo-Gonzalez MI, Zepeda-Cavazos IG, Siller-Aguillon I, Martinez-Perales JF, et al. Selection of oviposition sites by female Aedes aegypti exposed to two larvicides. J Am Mosq Control Assoc. 2012;28(1):47–9.PubMedView ArticleGoogle Scholar
  103. Fillinger U, Knols BGJ, Becker N. Efficacy and efficiency of new Bacillus thuringiensis var. israelensis and Bacillus sphaericus formulations against Afrotropical anophelines in western Kenya. Tropical Med Int Health. 2003;8(1):37–47.Google Scholar
  104. Eneh LK, Okal MN, Borg-Karlson AK, Fillinger U, Lindh JM. Gravid Anopheles gambiae sensu stricto avoid ovipositing in Bermuda grass hay infusion and it's volatiles in two choice egg-count bioassays. Malar J. 2016;15(1):276.PubMedPubMed CentralView ArticleGoogle Scholar
  105. Afify A, Galizia CG. Chemosensory cues for mosquito oviposition site selection. J Med Entomol. 2015;52(2):120–30.PubMedView ArticleGoogle Scholar
  106. Li J, Deng T, Li H, Chen L, Mo J. Effects of water color and chemical compounds on the oviposition behavior of gravid Culex pipiens pallens females under laboratory conditions. J Agric Urban Entomol. 2009;26(1):23–30.Google Scholar
  107. Beehler JW, Millar JG, Mulla MS. Synergism between chemical attractants and visual cues influencing oviposition of the mosquito, Culex quinquefasciatus (Diptera: Culicidae). J Chem Ecol. 1993;19(4):635–44.PubMedView ArticleGoogle Scholar
  108. Michaelakis A, Mihou AP, Koliopoulos G, Couladouros EA. Attract-and-kill strategy. Laboratory studies on hatched larvae of Culex pipiens. Pest Manag Sci. 2007;63(10):954–9.PubMedView ArticleGoogle Scholar
  109. Schorkopf DLP, Spanoudis CG, Mboera LEG, Mafra-Neto A, Ignell R, Dekker T. Combining attractants and larvicides in biodegradable matrices for sustainable mosquito vector control. PLoS Negl Trop Dis. 2016;10(10):e0005043.PubMedPubMed CentralView ArticleGoogle Scholar
  110. Choi DB, Grieco JP, Apperson CS, Schal C, Ponnusamy L, Wesson DM, et al. Effect of spatial repellent exposure on dengue vector attraction to oviposition sites. PLoS Negl Trop Dis. 2016;10(7):e0004850.PubMedPubMed CentralView ArticleGoogle Scholar
  111. Okal MN, Lindh JM, Torr SJ, Masinde E, Orindi B, Lindsay SW, et al. Analysing the oviposition behaviour of malaria mosquitoes: design considerations for improving two-choice egg count experiments. Malar J. 2015;14:250.Google Scholar
  112. Warburg A, Faiman R, Shtern A, Silberbush A, Markman S, Cohen JE, et al. Oviposition habitat selection by Anopheles gambiae in response to chemical cues by Notonecta maculata. J Vector Ecol. 2011;36(2):421–5.PubMedView ArticleGoogle Scholar
  113. Dugassa S, Lindh JM, Torr SJ, Lindsay SW, Fillinger U. Evaluation of the influence of electric nets on the behaviour of oviposition site seeking Anopheles gambiae s.s. Parasit Vectors. 2014;7:272.PubMedPubMed CentralView ArticleGoogle Scholar
  114. Grigaltchik VS, Webb C, Seebacher F. Temperature modulates the effects of predation and competition on mosquito larvae. Ecol Entomol. 2016;41(6):668–75.View ArticleGoogle Scholar
  115. Blaustein L, Chase JM. Interactions between mosquito larvae and species that share the same trophic level. Annu Rev Entomol. 2007;52:489–507.PubMedView ArticleGoogle Scholar
  116. Koenraadt CJM, Majambere S, Hemerik L, Takken W. The effects of food and space on the occurrence of cannibalism and predation among larvae of Anopheles gambiae s.l. Entomol Exp Appl. 2004;112(2):125–34.View ArticleGoogle Scholar
  117. Nyasembe VO, Teal PEA, Sawa P, Tumlinson JH, Borgemeister C, Torto B. Plasmodium falciparum infection increases Anopheles gambiae attraction to nectar sources and sugar uptake. Curr Biol. 2014;24(2):217–21.PubMedPubMed CentralView ArticleGoogle Scholar
  118. Scott TW, Takken W. Feeding strategies of anthropophilic mosquitoes result in increased risk of pathogen transmission. Trends Parasitol. 2012;28(3):114–21.PubMedView ArticleGoogle Scholar
  119. Takken W, Smallegange RC, Vigneau AJ, Johnston V, Brown M, Mordue-Luntz AJ, et al. Larval nutrition differentially affects adult fitness and Plasmodium development in the malaria vectors Anopheles gambiae and Anopheles stephensi. Parasit Vectors. 2013;6:345.PubMedPubMed CentralView ArticleGoogle Scholar
  120. Lacroix R, Mukabana WR, Gouagna LC, Koella JC. Malaria infection increases attractiveness of humans to mosquitoes. PLoS Biol. 2005;3(9):e298.PubMedPubMed CentralView ArticleGoogle Scholar
  121. De Moraes CM, Stanczyk NM, Betz HS, Pulido H, Sim DG, Read AF, et al. Malaria-induced changes in host odors enhance mosquito attraction. Proc Natl Acad Sci USA. 2014;111(30):11079–84.Google Scholar
  122. Busula AO, Bousema T, Mweresa CK, Masiga D, Logan JG, Sauerwein RW, et al. Gametocytemia and attractiveness of Plasmodium falciparum-infected Kenyan children to Anopheles gambiae mosquitoes. J Infect Dis. 2017;216(3):291–5.Google Scholar
  123. Lefèvre T, Thomas F. Behind the scene, something else is pulling the strings: emphasizing parasitic manipulation in vector-borne diseases. Inf Gen Evol. 2008;8(4):504–19.Google Scholar
  124. Hurd H. Manipulation of medically important insect vectors by their parasites. Annu Rev Entomol. 2003;48(1):141–61.PubMedView ArticleGoogle Scholar
  125. Gleave K, Cook D, Taylor MJ, Reimer LJ. Filarial infection influences mosquito behaviour and fecundity. Sci Rep. 2016;6:36319.PubMedPubMed CentralView ArticleGoogle Scholar
  126. Keating JA, Bhattacharya D, Rund SSC, Hoover S, Dasgupta R, Lee SJ, et al. Mosquito protein kinase g phosphorylates flavivirus ns5 and alters flight behavior in Aedes aegypti and Anopheles gambiae. Vector-Borne Zoonotic Dis. 2013;13(8):590–600.PubMedPubMed CentralView ArticleGoogle Scholar
  127. Lee JH, Rowley WA, Platt KB. Longevity and spontaneous flight activity of Culex tarsalis (Diptera: Culicidae) infected with western equine encephalomyelitis virus. J Med Entomol. 2000;37(1):187–93.PubMedView ArticleGoogle Scholar
  128. Berry WJ, Rowley WA, Christensen BM. Spontaneous flight activity of Aedes trivittatus infected with Dirofilaria immitis. J Parasitol. 1988;74(6):970–4.PubMedView ArticleGoogle Scholar
  129. Newman CM, Anderson TK, Goldberg TL. Decreased flight activity in Culex pipiens (Diptera: Culicidae) naturally infected with Culex flavivirus. J Med Entomol. 2016;53(1):233–6.PubMedView ArticleGoogle Scholar
  130. Vogels CBF, Fros JJF, Pijlman GP, van Loon JJA, Gort G, Koenraadt CJM. Virus interferes with host-seeking behaviour of mosquito. J Exp Biol. 2017;220(19):3598–603.PubMedView ArticleGoogle Scholar
  131. Smallegange RC, van Gemert GJ, van de Vegte-Bolmer M, Gezan S, Takken W, Sauerwein RW, et al. Malaria infected mosquitoes express enhanced attraction to human odor. PLoS One. 2013;8(5):e63602.PubMedPubMed CentralView ArticleGoogle Scholar
  132. Cator LJ, George J, Blanford S, Murdock CC, Baker TC, Read AF, et al. ‘Manipulation’ without the parasite: Altered feeding behaviour of mosquitoes is not dependent on infection with malaria parasites. Proc R Soc Lond B Biol Sci. 2013;280(1763)Google Scholar
  133. Cator LJ, Lynch PA, Read AF, Thomas MB. Do malaria parasites manipulate mosquitoes? Trends Parasitol. 2012;28(11):467–70.View ArticleGoogle Scholar
  134. Koella JC, Sorensen FL, Anderson RA. The malaria parasite, Plasmodium falciparum, increases the frequency of multiple feeding of its mosquito vector, Anopheles gambiae. Proc R Soc Lond B Biol Sci. 1998;265:763–8.View ArticleGoogle Scholar
  135. Scholte EJ, Knols BGJ, Takken W. Infection of the malaria mosquito Anopheles gambiae with the entomopathogenic fungus Metarhizium anisopliae reduces blood feeding and fecundity. J Invertebr Pathol. 2006;91(1):43–9.PubMedView ArticleGoogle Scholar
  136. Mnyone LL, Lyimo IN, Lwetoijera DW, Mpingwa MW, Nchimbi N, Hancock PA, et al. Exploiting the behaviour of wild malaria vectors to achieve high infection with fungal biocontrol agents. Malar J. 2012;11:87.PubMedPubMed CentralView ArticleGoogle Scholar
  137. George J, Blanford S, Domingue MJ, Thomas MB, Read AF, Baker TC. Reduction in host-finding behaviour in fungus-infected mosquitoes is correlated with reduction in olfactory receptor neuron responsiveness. Malar J. 2011;10:219.PubMedPubMed CentralView ArticleGoogle Scholar
  138. Blanford S, Chan BHK, Jenkins N, Sim D, Turner RJ, Read AF, et al. Fungal pathogen reduces potential for malaria transmission. Science. 2005;308(5728):1638–41.PubMedView ArticleGoogle Scholar
  139. Heinig RL, Thomas MB. Interactions between a fungal entomopathogen and malaria parasites within a mosquito vector. Malar J. 2015;14:22.PubMedPubMed CentralView ArticleGoogle Scholar
  140. Turley AP, Moreira LA, O'Neill SL, McGraw EA. Wolbachia infection reduces blood-feeding success in the dengue fever mosquito, Aedes aegypti. PLoS Negl Trop Dis. 2009;3(9):e516.PubMedPubMed CentralView ArticleGoogle Scholar
  141. Oliva CF, Damiens D, Benedict MQ. Male reproductive biology of Aedes mosquitoes. Acta Trop. 2014;132(1):12–9.View ArticleGoogle Scholar
  142. Moreira LA, Saig E, Turley AP, Ribeiro JMC, O'Neill SL, McGraw EA. Human probing behavior of Aedes aegypti when infected with a life-shortening strain of Wolbachia. PLoS Negl Trop Dis. 2009;3(12):e568.PubMedPubMed CentralView ArticleGoogle Scholar
  143. Evans O, Caragata EP, McMeniman CJ, Woolfit M, Green DC, Williams CR, et al. Increased locomotor activity and metabolism of Aedes aegypti infected with a lifeshortening strain of Wolbachia pipientis. J Exp Biol. 2009;212(10):1436–41.PubMedPubMed CentralView ArticleGoogle Scholar
  144. Chambers EW, Hapairai L, Peel BA, Bossin H, Dobson SL. Male mating competitiveness of a Wolbachia-introgressed Aedes polynesiensis strain under semi-field conditions. PLoS Negl Trop Dis. 2011;5(8):e1271.PubMedPubMed CentralView ArticleGoogle Scholar
  145. Takken W, Knols BGJ. Flight behaviour of Anopheles gambiae Giles (Diptera: Culicidae) in response to host stimuli: a windtunnel study. Proc Exp Appl Entomol. 1990;1:121–8.Google Scholar
  146. Davies ER. Computer and machine vision. 4th ed. London: Academic Press; 2012.Google Scholar
  147. Woltring HJ. Planar control in multi-camera calibration for 3-D gait studies. J Biomech. 1980;13(1):39–48.PubMedView ArticleGoogle Scholar
  148. Brown D. Tracker, video analysis and modeling tool. 2017. http://physlets.org/tracker/ . Accessed 26 Feb 2018.Google Scholar
  149. Hedrick TL. Software techniques for two- and three-dimensional kinematic measurements of biological and biomimetic systems. Bioinspir Biomim. 2008;3(3):034001.PubMedView ArticleGoogle Scholar
  150. Hounslow K. OpenCV tutorial: real-time object tracking without colour. 2014. https://www.youtube.com/watch?v=X6rPdRZzgjg . Accessed 26 Feb 2018.Google Scholar
  151. Perez-Escudero A, Vicente-Page J, Hinz RC, Arganda S, de Polavieja GG. idTracker: tracking individuals in a group by automatic identification of unmarked animals. Nat Methods. 2014;11(7):743–8.PubMedView ArticleGoogle Scholar
  152. Spitzen J, Spoor CW, Kranenbarg S, Beeuwkes J, Grieco F, Noldus LPJJ, et al. Track3D: Visualization and flight analysis of Anopheles gambiae s.s. mosquitoes. In: Proceedings of Measuring Behavior 2008; 6th International Conference on Methods and Techniques in Behavioral Research, Maastricht, The Netherlands; 2008. p. 133–5.Google Scholar
  153. Poh AH, Moghavvemi M, Leong CS, Lau YL, Safdari Ghandari A, Apau A, et al. Collective behavior quantification on human odor effects against female Aedes aegypti mosquitoes - Open source development. PLoS One. 2017;12(2):e0171555.PubMedPubMed CentralView ArticleGoogle Scholar
  154. Gibson G. A behavioural test of the sensitivity of a nocturnal mosquito, Anopheles gambiae, to dim white, red and infra-red light. Physiol Entomol. 1995;20:224–8.View ArticleGoogle Scholar
  155. Hawkes F, Gibson G. Seeing is believing: the nocturnal malarial mosquito Anopheles coluzzii responds to visual host-cues when odour indicates a host is nearby. Parasit Vectors. 2016;9:320.PubMedPubMed CentralView ArticleGoogle Scholar
  156. Angarita-Jaimes NC, Parker JEA, Abe M, Mashauri F, Martine J, Towers CE, et al. A novel video-tracking system to quantify the behaviour of nocturnal mosquitoes attacking human hosts in the field. J R Soc Interface. 2016;13:20150974.PubMedPubMed CentralView ArticleGoogle Scholar
  157. Kurihara K, Hoshino S, Yamane K, Nakamura Y. Optical motion capture system with pan-tilt camera tracking and real time data processing. In: Proceedings 2002 IEEE International Conference on Robotics and Automation; 2002. p. 1241–8.Google Scholar
  158. Offenhauser WH Jr, Kahn MC. The sounds of disease-carrying mosquitoes. J Acoust Soc Am. 1949;21(3):259–63.View ArticleGoogle Scholar
  159. Brogdon WG. Measurement of flight tone differences between female Aedes aegypti and A. albopictus (Diptera: Culicidae). J Med Entomol. 1994;31(5):700–3.PubMedView ArticleGoogle Scholar
  160. Brogdon WG. Measurement of flight tone differentiates among members of the Anophels gambiae species complex (Diptera: Culicidae). J Med Entomol. 1998;35(5):681–4.PubMedView ArticleGoogle Scholar
  161. Weseka JW, Brogdon WG, Hawley WA, Besansky NJ. Flight tone of field-collected populations of Anopheles gambiae and An. arabiensis (Diptera: Culicidae). Physiol Entomol. 1998;23:289–94.View ArticleGoogle Scholar
  162. Raman DR, Gerhardt RR, Wilkerson JB. Detecting insect flight sounds in the field: Implications for acoustical counting of mosquitoes. Trans ASABE. 2007;50(4):1481–5.View ArticleGoogle Scholar
  163. Mukundarajan H, Hol FJH, Castillo EA, Newby C, Prakash M. Using mobile phones as acoustic sensors for high-throughput mosquito surveillance. elife. 2017;6:e27854.PubMedPubMed CentralView ArticleGoogle Scholar
  164. Moore A, Miller JR, Tabashnik BE, Gage SH. Automated identification of flying insects by analysis of wingbeat frequencies. J Econ Entomol. 1986;79(6):1703–6.View ArticleGoogle Scholar
  165. Caprio MA, Huang JX, Faver MK, Moore A. Characterization of male and female wingbeat frequencies in the Anopheles quadrimaculatus complex in Mississippi. J Am Mosq Control Assoc. 2001;17(3):186–9.PubMedGoogle Scholar
  166. Ouyang T-H, Yang E-C, Jiang J-A, Lin T-T. Mosquito vector monitoring system based on optical wingbeat classification. Comput Electron Agric. 2015;118:47–55.View ArticleGoogle Scholar
  167. Potamitis I, Rigakis I, Fysarakis K. Insect biometrics: optoacoustic signal processing and its applications to remote monitoring of McPhail type traps. PLoS One. 2015;10(11):e0140474.PubMedPubMed CentralView ArticleGoogle Scholar
  168. Favret C, Sieracki JM. Machine vision automated species identification scaled towards production levels. Syst Entomol. 2016;41(1):133–43.View ArticleGoogle Scholar
  169. Wang J, Lin C, Ji L, Liang A. A new automatic identification system of insect images at the order level. Knowl-Based Syst. 2012;33:102–10.View ArticleGoogle Scholar
  170. Yang HP, Ma CS, Wen H, Zhan QB, Wang XL. A tool for developing an automatic insect identification system based on wing outlines. Sci Rep. 2015;5:12786.PubMedPubMed CentralView ArticleGoogle Scholar
  171. Pruszynski C. The BG-Counter: A new surveillance trap that remotely measures mosquito density in real-time. In: Sickerman SL, editor. Wing Beats. 27(1). Montgomery: Publications Press, Inc.; 2016.Google Scholar
  172. Project Premonition. Robotic mosquito traps that identify and capture interesting mosquitoes in milliseconds. https://www.microsoft.com/en-us/research/project/project-premonition/ . Accessed 26 Feb 2018.
  173. Straw AD, Branson K, Neumann TR, Dickinson MH. Multi-camera real-time three-dimensional tracking of multiple flying animals. J R Soc Interface. 2011;8(56):395–409.PubMedView ArticleGoogle Scholar
  174. Fry SN, Rohrseitz N, Straw AD, Dickinson MH. TrackFly: Virtual reality for a behavioral system analysis in free-flying fruit flies. J Neurosci Methods. 2008;171(1):110–7.PubMedView ArticleGoogle Scholar
  175. Bomphrey RJ, Nakata T, Phillips N, Walker SM. Smart wing rotation and trailing-edge vortices enable high frequency mosquito flight. Nature. 2017;544(7648):92–5.PubMedPubMed CentralView ArticleGoogle Scholar
  176. Muijres FT, Chang SW, van Veen WG, Spitzen J, Biemans BT, Koehl MAR, Dudley R. Escaping blood-fed malaria mosquitoes minimize tactile detection without compromising on take-off speed. J Exp Biol. 2017;220(20):3751–62.PubMedView ArticleGoogle Scholar
  177. Sperling S, Cordel M, Gordon S, Knols B, Rose A. Eave tubes for malaria control in africa: videographic observations of mosquito behaviour in Tanzania with a simple and rugged video surveillance system. Malar World J. 2017;8:1–10.Google Scholar
  178. Cook SM, Khan ZR, Pickett JA. The use of push-pull strategies in integrated pest management. Annu Rev Entomol. 2007;52:375–400.PubMedView ArticleGoogle Scholar
  179. Cummins B, Cortez R, Foppa IM, Walbeck J, Hyman JM. A spatial model of mosquito host-seeking behavior. PLoS Comput Biol. 2012;8(5):e1002500.PubMedPubMed CentralView ArticleGoogle Scholar
  180. Killeen GF, Govella NJ, Lwetoijera DW, Okumu FO. Most outdoor malaria transmission by behaviourally-resistant Anopheles arabiensis is mediated by mosquitoes that have previously been inside houses. Malar J. 2016;15(1):225.PubMedPubMed CentralView ArticleGoogle Scholar
  181. Rund SS, Lee SJ, Bush BR, Duffield GE. Strain- and sex-specific differences in daily flight activity and the circadian clock of Anopheles gambiae mosquitoes. J Insect Physiol. 2012;58(12):1609–19.PubMedView ArticleGoogle Scholar
  182. Chapman JW, Drake VA, Reynolds DR. Recent insights from radar studies of insect flight. Annu Rev Entomol. 2011;56:337–56.PubMedView ArticleGoogle Scholar
  183. Kim J, Jung M, Kim HG, Lee DH. Potential of harmonic radar system for use on five economically important insects: radar tag attachment on insects and its impact on flight capacity. J Asia-Pacific Entomol. 2016;19(2):371–5.Google Scholar
  184. Wang R, Hu C, Fu X, Long T, Zeng T. Micro-Doppler measurement of insect wing-beat frequencies with W-band coherent radar. Sci Rep. 2017;7(1):1396.PubMedPubMed CentralView ArticleGoogle Scholar
  185. Verhulst NO, Loonen JA, Takken W. Advances in methods for colour marking of mosquitoes. Parasit Vectors. 2013;6:200.PubMedPubMed CentralView ArticleGoogle Scholar
  186. Hamer GL, Donovan DJ, Hood-Nowotny R, Kaufman MG, Goldberg TL, Walker ED. Evaluation of a stable isotope method to mark naturally-breeding larval mosquitoes for adult dispersal studies. J Med Entomol. 2012;49(1):61–70.PubMedPubMed CentralView ArticleGoogle Scholar
  187. Sutcliffe J, Colborn KL. Video studies of passage by Anopheles gambiae mosquitoes through holes in a simulated bed net: effects of hole size, hole orientation and net environment. Malar J. 2015;14(1):199.Google Scholar
  188. Sutcliffe J, Ji X, Yin S. How many holes is too many? A prototype tool for estimating mosquito entry risk into damaged bed nets. Malar J. 2017;16(1):304.PubMedPubMed CentralView ArticleGoogle Scholar
  189. Fu S, Biwole PH, Mathis C. Particle Tracking Velocimetry for indoor airflow field: a review. Build Environ. 2015;87:34–44.Google Scholar
  190. Dickerson AK, Shankles PG, Berry BE Jr, Hu DL. Fog and dense gas disrupt mosquito flight due to increased aerodynamic drag on halteres. J Fluids Struct. 2015;55:451–62.View ArticleGoogle Scholar
  191. Lynd A, McCall PJ. Clustering of host-seeking activity of Anopheles gambiae mosquitoes at the top surface of a human-baited bed net. Malar J. 2013;12(1):267.PubMedPubMed CentralView ArticleGoogle Scholar
  192. Parker JEA, Angarita Jaimes NC, Gleave K, Mashauri F, Abe M, Martine J, Towers CE, et al. Host-seeking activity of a Tanzanian population of Anopheles arabiensis at an insecticide treated bed net. Malar J. 2017;16(1):270.PubMedPubMed CentralView ArticleGoogle Scholar
  193. Hawkes FM, Dabiré RK, Sawadogo SP, Torr SJ, Gibson G. Exploiting Anopheles responses to thermal, odour and visual stimuli to improve surveillance and control of malaria. Sci Rep. 2017;7(1):17283.PubMedPubMed CentralView ArticleGoogle Scholar

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