Lidar reveals activity anomaly of malaria vectors during pan-African eclipse

Monitoring insect numbers and biodiversity with a vertical-beam entomological radar

Concerns about perceived widespread declines in insect numbers have led to recognition of a requirement for long-term monitoring of insect biodiversity. Here we examine whether an existing, radar-based, insect monitoring system developed for research on insect migration could be adapted to this role. The radar detects individual larger (greater than 10 mg) insects flying at heights of 150-2550 m and estimates their size and mass. It operates automatically and almost continuously through both day and night. Accumulation of data over a 'half-month' (approx. 15 days) averages out weather effects and broadens the source area of the wind-borne observation sample. Insect counts are scaled or interpolated to compensate for missed observations; adjustment for variation of detectability with range and insect size is also possible. Size distributions for individual days and nights exhibit distinct peaks, representing different insect types, and Simpson and Shannon-Wiener indices of biodiversity are calculated from these. Half-month count, biomass and index statistics exhibit variations associated with the annual cycle and year to year changes that can be attributed to drought and periods of high rainfall. While species-based biodiversity measures cannot be provided, the radar's capacity to estimate insect biomass over a wide area indicates utility for tracking insect population sizes. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Lidar as a potential tool for monitoring migratory insects

The seasonal migrations of insects involve a substantial displacement of biomass with significant ecological and economic consequences for regions of departure and arrival. Remote sensors have played a pivotal role in revealing the magnitude and general direction of bioflows above 150 m. Nevertheless, the takeoff and descent activity of insects below this height is poorly understood. Our lidar observations elucidate the low-height dusk movements and detailed information of insects in southern Sweden from May to July, during the yearly northward migration period. Importantly, by filtering out moths from other insects based on optical information and wingbeat frequency, we have introduced a promising new method to monitor the flight activities of nocturnal moths near the ground, many of which participate in migration through the area. Lidar thus holds the potential to enhance the scientific understanding of insect migratory behavior and improve pest control strategies.

An optical system to detect, surveil, and kill flying insect vectors of human and crop pathogens

Sustainable and effective means to control flying insect vectors are critically needed, especially with widespread insecticide resistance and global climate change. Understanding and controlling vectors requires accurate information about their movement and activity, which is often lacking. The Photonic Fence (PF) is an optical system that uses machine vision, infrared light, and lasers to identify, track, and interdict vectors in flight. The PF examines an insect's outline, flight speed, and other flight parameters and if these match those of a targeted vector species, then a low-power, retina-safe laser kills it. We report on proof-of-concept tests of a large, field-sized PF (30 mL × 3 mH) conducted with Aedes aegypti, a mosquito that transmits dangerous arboviruses, and Diaphorina citri, a psyllid which transmits the fatal huanglongbing disease of citrus. In tests with the laser engaged, < 1% and 3% of A. aegypti and D. citri, respectfully, were recovered versus a 38% and 19% recovery when the lacer was silenced. The PF tracked, but did not intercept the orchid bee, Euglossa dilemma. The system effectively intercepted flying vectors, but not bees, at a distance of 30 m, heralding the use of photonic energy, rather than chemicals, to control flying vectors.

Tracking technologies: advances driving new insights into monarch migration

Understanding the rules of how monarch butterflies complete their annual North American migration will be clarified by studying them within a movement ecology framework. Insect movement ecology is growing at a rapid pace due to the development of novel monitoring systems that allow ever-smaller animals to be tracked at higher spatiotemporal resolution for longer periods of time. New innovations in tracking hardware and associated software, including miniaturization, energy autonomy, data management, and wireless communication, are reducing the size and increasing the capability of next-generation tracking technologies, bringing the goal of tracking monarchs over their entire migration closer within reach. These tools are beginning to be leveraged to provide insight into different aspects of monarch biology and ecology, and to contribute to a growing capacity to understand insect movement ecology more broadly and its impact on human life.

Discrimination of Hover Fly Species and Sexes by Wing Interference Signals

Remote automated surveillance of insect abundance and diversity is poised to revolutionize insect decline studies. The study reveals spectral analysis of thin-film wing interference signals (WISs) can discriminate free-flying insects beyond what can be accomplished by machine vision. Detectable by photonic sensors, WISs are robust indicators enabling species and sex identification. The first quantitative survey of insect wing thickness and modulation through shortwave-infrared hyperspectral imaging of 600 wings from 30 hover fly species is presented. Fringy spectral reflectance of WIS can be explained by four optical parameters, including membrane thickness. Using a Naïve Bayes Classifier with five parameters that can be retrieved remotely, 91% is achieved accuracy in identification of species and sexes. WIS-based surveillance is therefore a potent tool for remote insect identification and surveillance.

Arbovirus surveillance in mosquitoes: Historical methods, emerging technologies, and challenges ahead

Arboviruses cause millions of infections each year; however, only limited options are available for treatment and pharmacological prevention. Mosquitoes are among the most important vectors for the transmission of several pathogens to humans. Despite advances, the sampling, viral detection, and control methods for these insects remain ineffective. Challenges arise with the increase in mosquito populations due to climate change, insecticide resistance, and human interference affecting natural habitats, which contribute to the increasing difficulty in controlling the spread of arboviruses. Therefore, prioritizing arbovirus surveillance is essential for effective epidemic preparedness. In this review, we offer a concise historical account of the discovery and monitoring of arboviruses in mosquitoes, from mosquito capture to viral detection. We then analyzed the advantages and limitations of these traditional methods. Furthermore, we investigated the potential of emerging technologies to address these limitations, including the implementation of next-generation sequencing, paper-based devices, spectroscopic detectors, and synthetic biosensors. We also provide perspectives on recurring issues and areas of interest such as insect-specific viruses.

Monitoring the abundance of flying insects and atmospheric conditions during a 9-month campaign using an entomological optical sensor

Monitoring the dynamics of insect populations is key to assessing the impact of human activities on insect populations. However, traditional methodologies relying on physical traps have inherent limitations in accurately monitoring insect abundance. Here, we present findings from a 9-month campaign conducted in New Jersey, USA, utilizing a near-infrared optical sensor known as eBoss. From April to December 2022, the eBoss derived the aerial density (insect/m3) and biomass density (mg/m3) with a 1-min resolution from a total of 302,093 insect observations. The data collected were analyzed in relation to air temperature, relative humidity, and wind speed. The results revealed that the abundance of flying insects exhibited an initial increase from April to June, reaching a peak of 0.094 insect/m3 and 1.34 mg/m3, followed by a subsequent decline towards the end of the year. Our investigation showed a surge in insect abundance above 12.5 °C, with particularly high levels observed between 19 and 31 °C. The impact of relative humidity and wind speed on insect populations was also explored. Overall, this campaign demonstrated the efficacy of photonic sensors in gathering novel and extensive data for the field of entomology, paving the way for improved understanding and management of insect populations.

Spatial monitoring of flying insects over a Swedish lake using a continuous-wave lidar system

We have used a continuous-wave bi-static lidar system based on the Scheimpflug principle in measurements on flying insects above, and in the vicinity of, a small lake located in a forested area in Southern Sweden. The system, which operates on triangulation principles, has a high spatial resolution at close distance, followed by a subsequent decline in resolution further from the sensor, related to the compact system design with a separation of transmitter and receiver by only 0.81 m. Our study showed a strong increase in insect abundance especially at dusk, but also at dawn. Insect numbers decreased over water compared to over land, and larger insects were over-represented over water. Further, the average size of the insects increased at night compared to day time.

Remote Nanoscopy with Infrared Elastic Hyperspectral Lidar

Monitoring insects of different species to understand the factors affecting their diversity and decline is a major challenge. Laser remote sensing and spectroscopy offer promising novel solutions to this. Coherent scattering from thin wing membranes also known as wing interference patterns (WIPs) have recently been demonstrated to be species specific. The colors of WIPs arise due to unique fringy spectra, which can be retrieved over long distances. To demonstrate this, a new concept of infrared (950-1650 nm) hyperspectral lidar with 64 spectral bands based on a supercontinuum light source using ray-tracing and 3D printing is developed. A lidar with an unprecedented number of spectral channels, high signal-to-noise ratio, and spatio-temporal resolution enabling detection of free-flying insects and their wingbeats. As proof of principle, coherent scatter from a damselfly wing at 87 m distance without averaging (4 ms recording) is retrieved. The fringed signal properties are used to determine an effective wing membrane thickness of 1412 nm with ±4 nm precision matching laboratory recordings of the same wing. Similar signals from free flying insects (2 ms recording) are later recorded. The accuracy and the method's potential are discussed to discriminate species by capturing coherent features from free-flying insects.

Future of DNA-based insect monitoring

Insects are crucial for ecosystem health but climate change and pesticide use are driving massive insect decline. To mitigate this loss, we need new and effective monitoring techniques. Over the past decade there has been a shift to DNA-based techniques. We describe key emerging techniques for sample collection. We suggest that the selection of tools should be broadened, and that DNA-based insect monitoring data need to be integrated more rapidly into policymaking. We argue that there are four key areas for advancement, including the generation of more complete DNA barcode databases to interpret molecular data, standardisation of molecular methods, scaling up of monitoring efforts, and integrating molecular tools with other technologies that allow continuous, passive monitoring based on images and/or laser imaging, detection, and ranging (LIDAR).

A Deep-Learning-Based Detection Approach for the Identification of Insect Species of Economic Importance

Artificial Intelligence (AI) and automation are fostering more sustainable and effective solutions for a wide spectrum of agricultural problems. Pest management is a major challenge for crop production that can benefit from machine learning techniques to detect and monitor specific pests and diseases. Traditional monitoring is labor intensive, time demanding, and expensive, while machine learning paradigms may support cost-effective crop protection decisions. However, previous studies mainly relied on morphological images of stationary or immobilized animals. Other features related to living animals behaving in the environment (e.g., walking trajectories, different postures, etc.) have been overlooked so far. In this study, we developed a detection method based on convolutional neural network (CNN) that can accurately classify in real-time two tephritid species (Ceratitis capitata and Bactrocera oleae) free to move and change their posture. Results showed a successful automatic detection (i.e., precision rate about 93%) in real-time of C. capitata and B. oleae adults using a camera sensor at a fixed height. In addition, the similar shape and movement patterns of the two insects did not interfere with the network precision. The proposed method can be extended to other pest species, needing minimal data pre-processing and similar architecture.

Insect biomass density: measurement of seasonal and daily variations using an entomological optical sensor

Insects are major actors in Earth's ecosystems and their recent decline in abundance and diversity is alarming. The monitoring of insects is paramount to understand the cause of this decline and guide conservation policies. In this contribution, an infrared laser-based system is used to remotely monitor the biomass density of flying insects in the wild. By measuring the optical extinction caused by insects crossing the 36-m long laser beam, the Entomological Bistatic Optical Sensor System used in this study can evaluate the mass of each specimen. At the field location, between July and December 2021, the instrument made a total of 262,870 observations of insects for which the average dry mass was 17.1 mg and the median 3.4 mg. The daily average mass of flying insects per meter cube of air at the field location has been retrieved throughout the season and ranged between near 0 to 1.2 mg/m³. Thanks to its temporal resolution in the minute range, daily variations of biomass density have been observed as well. These measurements show daily activity patterns changing with the season, as large increases in biomass density were evident around sunset and sunrise during Summer but not during Fall.

The use of drones for mosquito surveillance and control

In recent years, global health security has been threatened by the geographical expansion of vector-borne infectious diseases such as malaria, dengue, yellow fever, Zika and chikungunya. For a range of these vector-borne diseases, an increase in residual (exophagic) transmission together with ecological heterogeneity in everything from weather to local human migration and housing to mosquito species' behaviours presents many challenges to effective mosquito control. The novel use of drones (or uncrewed aerial vehicles) may play a major role in the success of mosquito surveillance and control programmes in the coming decades since the global landscape of mosquito-borne diseases and disease dynamics fluctuates frequently and there could be serious public health consequences if the issues of insecticide resistance and outdoor transmission are not adequately addressed. For controlling both aquatic and adult stages, for several years now remote sensing data have been used together with predictive modelling for risk, incidence and detection of transmission hot spots and landscape profiles in relation to mosquito-borne pathogens. The field of drone-based remote sensing is under continuous change due to new technology development, operation regulations and innovative applications. In this review we outline the opportunities and challenges for integrating drones into vector surveillance (i.e. identification of breeding sites or mapping micro-environmental composition) and control strategies (i.e. applying larval source management activities or deploying genetically modified agents) across the mosquito life-cycle. We present a five-step systematic environmental mapping strategy that we recommend be undertaken in locations where a drone is expected to be used, outline the key considerations for incorporating drone or other Earth Observation data into vector surveillance and provide two case studies of the advantages of using drones equipped with multispectral cameras. In conclusion, recent developments mean that drones can be effective for accurately conducting surveillance, assessing habitat suitability for larval and/or adult mosquitoes and implementing interventions. In addition, we briefly discuss the need to consider permissions, costs, safety/privacy perceptions and community acceptance for deploying drone activities.

Development of a Fixed-Wing Drone System for Aerial Insect Sampling

Most current insect research techniques are ground-based and provide scarce information about flying insects in the planetary boundary layer (PBL), which remains a poorly studied ecological niche. To address this gap, we developed a new insect-sampling method consisting of a fixed-wing drone platform with net traps attached to the fuselage, a mobile design that has optimal aerodynamic characteristics for insect capture in the PBL. We tested the proposed device on 16 flights in Doñana National Park (Spain) with two different trap designs fitted on the fuselage nose and wing. We collected 34 insect specimens belonging to four orders with a representation of twelve families at mean altitudes below 23 m above ground level and sampling altitudes between 9 and 365 m. This drone insect-sampling design constitutes a low-cost and low-impact method for insect monitoring in the PBL, especially in combination with other remote sensing technologies that directly quantify aerial insect abundance but do not provide taxonomic information, opening interesting possibilities for ecology and entomological research, with the possibility of transfer to economically important sectors, such as agriculture and health.

Emerging technologies revolutionise insect ecology and monitoring

Insects are the most diverse group of animals on Earth, but their small size and high diversity have always made them challenging to study. Recent technological advances have the potential to revolutionise insect ecology and monitoring. We describe the state of the art of four technologies (computer vision, acoustic monitoring, radar, and molecular methods), and assess their advantages, current limitations, and future potential. We discuss how these technologies can adhere to modern standards of data curation and transparency, their implications for citizen science, and their potential for integration among different monitoring programmes and technologies. We argue that they provide unprecedented possibilities for insect ecology and monitoring, but it will be important to foster international standards via collaboration.

Potential for identification of wild night-flying moths by remote infrared microscopy

There are hundreds of thousands of moth species with crucial ecological roles that are often obscured by their nocturnal lifestyles. The pigmentation and appearance of moths are dominated by cryptic diffuse shades of brown. In this study, 82 specimens representing 26 moth species were analysed using infrared polarimetric hyperspectral imaging in the range of 0.95–2.5 µm. Contrary to previous studies, we demonstrate that since infrared light does not resolve the surface roughness, wings appear glossy and specular at longer wavelengths. Such properties provide unique reflectance spectra between species. The reflectance of the majority of our species could be explained by comprehensive models, and a complete parametrization of the spectral, polarimetric and angular optical properties was reduced to just 11 parameters with physical units. These parameters are complementary and, compared with the within-species variation, were significantly distinct between species. Counterintuitively to the aperture-limited resolution criterion, we could deduce microscopic features along the surface from their infrared properties. These features were confirmed by electron microscopy. Finally, we show how our findings could greatly enhance opportunities for remote identification of free-flying moth species, and we hypothesize that such flat specular wing targets could be expected to be sensed over considerable distances.

Automating insect monitoring using unsupervised near-infrared sensors

Insect monitoring is critical to improve our understanding and ability to preserve and restore biodiversity, sustainably produce crops, and reduce vectors of human and livestock disease. Conventional monitoring methods of trapping and identification are time consuming and thus expensive. Automation would significantly improve the state of the art. Here, we present a network of distributed wireless sensors that moves the field towards automation by recording backscattered near-infrared modulation signatures from insects. The instrument is a compact sensor based on dual-wavelength infrared light emitting diodes and is capable of unsupervised, autonomous long-term insect monitoring over weather and seasons. The sensor records the backscattered light at kHz pace from each insect transiting the measurement volume. Insect observations are automatically extracted and transmitted with environmental metadata over cellular connection to a cloud-based database. The recorded features include wing beat harmonics, melanisation and flight direction. To validate the sensor’s capabilities, we tested the correlation between daily insect counts from an oil seed rape field measured with six yellow water traps and six sensors during a 4-week period. A comparison of the methods found a Spearman’s rank correlation coefficient of 0.61 and a p-value = 0.0065, with the sensors recording approximately 19 times more insect observations and demonstrating a larger temporal dynamic than conventional yellow water trap monitoring.

Continuous monitoring of aerial density and circadian rhythms of flying insects in a semi-urban environment

Although small in size, insects are a quintessential part of terrestrial ecosystems due to their large number and diversity. While captured insects can be thoroughly studied in laboratory conditions, their population dynamics and abundance in the wild remain largely unknown due to the lack of accurate methodologies to count them. Here, we present the results of a field experiment where the activity of insects has been monitored continuously over 3 months using an entomological stand-off optical sensor (ESOS). Because its near-infrared laser is imperceptible to insects, the instrument provides an unbiased and absolute measurement of the aerial density (flying insect/m3) with a temporal resolution down to the minute. Multiple clusters of insects are differentiated based on their wingbeat frequency and ratios between wing and body optical cross-sections. The collected data allowed for the study of the circadian rhythm and daily activities as well as the aerial density dynamic over the whole campaign for each cluster individually. These measurements have been compared with traps for validation of this new methodology. We believe that this new type of data can unlock many of the current limitations in the collection of entomological data, especially when studying the population dynamics of insects with large impacts on our society, such as pollinators or vectors of infectious diseases.

Identification of Flying Insects in the Spatial, Spectral, and Time Domains with Focus on Mosquito Imaging

Insects constitute a very important part of the global ecosystem and include pollinators, disease vectors, and agricultural pests, all with pivotal influence on society. Monitoring and control of such insects has high priority, and automatic systems are highly desirable. While capture and analysis by biologists constitute the gold standard in insect identification, optical and laser techniques have the potential for high-speed detection and automatic identification based on shape, spectroscopic properties such as reflectance and fluorescence, as well as wing-beat frequency analysis. The present paper discusses these approaches, and in particular presents a novel method for automatic identification of mosquitos based on image analysis, as the insects enter a trap based on a combination of chemical and suction attraction. Details of the analysis procedure are presented, and selectivity is discussed. An accuracy of 93% is achieved by our proposed method from a data set containing 122 insect images (mosquitoes and bees). As a powerful and cost-effective method, we finally propose the combination of imaging and wing-beat frequency analysis in an integrated instrument.

Bark beetles as lidar targets and prospects of photonic surveillance

Forestry is raising concern about the outbreaks of European spruce bark beetle, Ips typographus, causing extensive damage to the spruce forest and timber values. Precise monitoring of these beetles is a necessary step towards preventing outbreaks. Current commercial monitoring methods are catch-based and lack in both temporal and spatial resolution. In this work, light scattering from beetles is characterized, and the feasibility of entomological lidar as a tool for long-term monitoring of bark beetles is explored. Laboratory optical properties, wing thickness, and wingbeat frequency of bark beetles are reported, and these parameters can infer target identity in lidar data. Lidar results from a Swedish forest with controlled bark beetle release event are presented. The capability of lidar to simultaneously monitor both insects and a pheromone plume mixed with chemical smoke governing the dispersal of many insects is demonstrated. In conclusion, entomological lidar is a promising tool for monitoring bark beetles.

Real-time dispersal of malaria vectors in rural Africa monitored with lidar

Lack of tools for detailed, real-time observation of mosquito behavior with high spatio-temporal resolution limits progress towards improved malaria vector control. We deployed a high-resolution entomological lidar to monitor a half-kilometer static transect positioned over rice fields outside a Tanzanian village. A quarter of a million in situ insect observations were classified, and several insect taxa were identified based on their modulation signatures. We observed distinct range distributions of male and female mosquitoes in relation to the village periphery, and spatio-temporal behavioral features, such as swarming. Furthermore, we observed that the spatial distributions of males and females change independently of each other during the day, and were able to estimate the daily dispersal of mosquitoes towards and away from the village. The findings of this study demonstrate how lidar-based monitoring could dramatically improve our understanding of malaria vector ecology and control options.