EDAPHOLOG monitoring system: automatic, real‐time detection of soil microarthropods

Automatic Detection of Moths (Lepidoptera) with a Funnel Trap Prototype

Monitoring insect populations is essential to optimise pest control with the correct protection timing and the avoidance of unnecessary insecticide use. Modern real-time monitoring practices use automatic insect traps, which are expected to be able to estimate the population sizes of pest animals with high species specificity. There are many solutions to overcome this challenge; however, there are only a few data that consider their accuracy under field conditions. This study presents an opto-electronic device prototype (ZooLog VARL) developed by us. A pilot field study evaluated the precision and accuracy of the data filtering using an artificial neural network(ANN) and the detection accuracy of the new probes. The prototype comprises a funnel trap, sensor-ring, and data communication system. The main modification of the trap was a blow-off device that prevented the escape of flying insects from the funnel. These new prototypes were tested in the field during the summer and autumn of 2018, detecting the daily and monthly flight of six moth species (Agrotis segetum, Autographa gamma, Helicoverpa armigera, Cameraria ohridella, Grapholita funebrana, Grapholita molesta). The accuracy of ANN was always higher than 60%. In the case of species with larger body sizes, it reached 90%. The detection accuracy ranged from 84% to 92% on average. These probes detected the real-time catches of the moth species. Therefore, weekly and daily patterns of moth flight activity periods could be compared and displayed for the different species. This device solved the problem of multiple counting and gained a high detection accuracy in target species cases. ZooLog VARL probes provide the real-time, time-series data sets of each monitored pest species. Further evaluation of the catching efficiency of the probes is needed. However, the prototype allows us to follow and model pest dynamics and may make more precise forecasts of population outbreaks.

Temporal and spatial dynamics in soil acoustics and their relation to soil animal diversity

The observation and assessment of animal biodiversity using acoustic technology has developed considerably in recent years. Current eco-acoustic research focuses on automatic audio recorder arrays and acoustic indices, which may be used to study the spatial and temporal dynamics of local animal communities in high resolution. While such soundscapes have often been studied above ground, their applicability in soils has rarely been tested. For the first time, we applied acoustic and statistical methods to explore the spatial, diurnal, and seasonal dynamics of the soundscape in soils. We studied the dynamics of acoustic complexity in forest soils in the alpine Pfynwald forest in the Swiss canton of Valais and related them to meteorological and microclimatic data. To increase microclimatic variability, we used a long-term irrigation experiment. We also took soil samples close to the sensors on 6 days in different seasons. Daily and seasonal patterns of acoustic complexity were predicted to be associated with abiotic parameters—that is, meteorological and microclimatic conditions—and mediated by the dynamics of the diversity and activity of the soil fauna. Seasonal patterns in acoustic complexity showed the highest acoustic complexity values in spring and summer, decreasing in fall and winter. Diurnal acoustic complexity values were highest in the afternoon and lowest during the night. The measurement of acoustic diversity at the sampling site was significantly associated with soil communities, with relationships between taxa richness or community composition and acoustic complexity being strongest shortly before taking the soil samples. Our results suggest that the temporal and spatial dynamics of the diversity and community composition of soil organisms can be predicted by the acoustic complexity of soil soundscapes. This opens up the possibility of using soil soundscape analysis as a noninvasive and easy-to-use method for soil biodiversity monitoring programs.

Deep learning and computer vision will transform entomology

Most animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is sparse. Insect populations are challenging to study, and most monitoring methods are labor intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors can effectively, continuously, and noninvasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the laboratory. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behavior, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to exceptionally large datasets to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) validation of image-based taxonomic identification; 2) generation of sufficient training data; 3) development of public, curated reference databases; and 4) solutions to integrate deep learning and molecular tools.

Ecotrons: Powerful and versatile ecosystem analysers for ecology, agronomy and environmental science

Ecosystems integrity and services are threatened by anthropogenic global changes. Mitigating and adapting to these changes require knowledge of ecosystem functioning in the expected novel environments, informed in large part through experimentation and modelling. This paper describes 13 advanced controlled environment facilities for experimental ecosystem studies, herein termed ecotrons, open to the international community. Ecotrons enable simulation of a wide range of natural environmental conditions in replicated and independent experimental units while measuring various ecosystem processes. This capacity to realistically control ecosystem environments is used to emulate a variety of climatic scenarios and soil conditions, in natural sunlight or through broad-spectrum lighting. The use of large ecosystem samples, intact or reconstructed, minimizes border effects and increases biological and physical complexity. Measurements of concentrations of greenhouse trace gases as well as their net exchange between the ecosystem and the atmosphere are performed in most ecotrons, often quasi continuously. The flow of matter is often tracked with the use of stable isotope tracers of carbon and other elements. Equipment is available for measurements of soil water status as well as root and canopy growth. The experiments ran so far emphasize the diversity of the hosted research. Half of them concern global changes, often with a manipulation of more than one driver. About a quarter deal with the impact of biodiversity loss on ecosystem functioning and one quarter with ecosystem or plant physiology. We discuss how the methodology for environmental simulation and process measurements, especially in soil, can be improved and stress the need to establish stronger links with modelling in future projects. These developments will enable further improvements in mechanistic understanding and predictive capacity of ecotron research which will play, in complementarity with field experimentation and monitoring, a crucial role in exploring the ecosystem consequences of environmental changes.

Automatic Field Detection of Western Corn Rootworm (Diabrotica virgifera virgifera; Coleoptera: Chrysomelidae) with a New Probe

The Western corn rootworm (WCR), Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae), is a significant invasive pest of maize plantations in Europe. Integrated pest management demands an adequate monitoring system which detects the activity of insects with high accuracy in real-time. In this study, we show and test a new electronic device (ZooLog KLP), which was developed to detect WCR in the field. The ZooLog KLP consists of a trapping element that attracts insects with its color and species-specific sex pheromone. The other part is an opto-electronic sensor-ring which detects the specimens when they fall into the trap. At detection, the time of catch is recorded and sent to a web interface. In this study, we followed WCR flight patterns for six weeks in two locations, using ZooLog KLP probes. We investigated sensor precision by comparing the number of catches to the number of detections. The tool reached high accuracy (95.84%) in recording WCR. We found a peak in flight activity in August and a bimodal daily pattern. This method may be beneficial in detecting the WCR during their activity, and this new device may serve as a prototype for real-time monitoring systems and improve the management of this pest.

Detecting Soil Microarthropods with a Camera-Supported Trap

There is an increasing need to monitor activity and population growth of arthropods; however, this is a time-consuming and financially demanding process. Using sensors to detect arthropods in the field can help to follow their dynamics in time. Improving our earlier device, we developed a new camera-supported probe to detect soil microarthropods. An opto-electronic sensor ring detects the caught microarthropod individuals what activates a camera. The camera takes pictures of a specimen when it arrives in the camera chamber. A vacuum device was built into the probe which pumps up the specimen from the probe to a sample container. Here, we describe the construction and operation of the probe. We investigated the precision of the process in a laboratory experiment using living microarthropods and evaluated the accuracy of the probes in a semi-natural investigation when environmental noise was present. Under semi-natural conditions, the percentages of success, i.e., the photographed specimens compared to the caught ones, were between 60% and 70% at the investigated taxa. The automatic camera shooting helped in distinguishing insects from irrelevant detections while collecting the trapped insects allowed species-level determination. This information together serves as a basis for the automatic visual recognition of microarthropod species.

An Opto-electronic Sensor-ring to Detect Arthropods of Significantly Different Body Sizes

Arthropods, including pollinators and pests, have high positive and negative impacts on human well-being and the economy, and there is an increasing need to monitor their activity and population growth. The monitoring of arthropod species is a time-consuming and financially demanding process. Automatic detection can be a solution to this problem. Here, we describe the setup and operation mechanism of an infrared opto-electronic sensor-ring, which can be used for both small and large arthropods. The sensor-ring consists of 16 infrared (IR) photodiodes along a semicircle in front of an infrared LED. Using 3D printing, we constructed two types of sensor-ring: one with a wider sensing field for detection of large arthropods (flying, crawling, surface-living) in the size range of 2-35 mm; and another one with a narrower sensing field for soil microarthropods in the size range of 0.1-2 mm. We examined the detection accuracy and reliability of the two types of sensor-ring in the laboratory by using particles, and dead and living arthropods at two different sensitivity levels. For the wider sensor-ring, the 95% detectability level was reached with grain particles of 0.9 mm size. This result allowed us to detect all of the macroarthropods that were applied in the tests and that might be encountered in pest management. In the case of living microarthropods with different colors and shapes, when we used the narrower sensor-ring, we achieved the 95% detectability level at 1.1 mm, 0.9 mm, and 0.5 mm in the cases of F. candida, H. nitidus, and H. aculeifer, respectively. The unique potential of arthropod-detecting sensors lies in their real-time measurement system; the data are automatically forwarded to the server, and the end-user receives pest abundance data daily or even immediately. This technological innovation will allow us to make pest management more effective.

Effects of single and repeated drought on soil microarthropods in a semi-arid ecosystem depend more on timing and duration than drought severity

Soil moisture is one of the most important factors affecting soil biota. In arid and semi-arid ecosystems, soil mesofauna is adapted to temporary drought events, but, until now, we have had a limited understanding of the impacts of the different magnitudes and frequencies of drought predicted to occur according to future climate change scenarios. The present study focuses on how springtails and mites respond to simulated repeated drought events of different magnitudes in a field experiment in a Hungarian semi-arid sand steppe. Changes in soil arthropod activities were monitored with soil trapping over two years in a sandy soil. In the first year (2014), we applied an extreme drought pretreatment, and in the consecutive year, we applied less devastating treatments (severe drought, moderate drought, water addition) to these sites. In the first year, the extreme drought pretreatment tended to have a negative effect (either significantly or not significantly) on the capture of all Collembola groups, whereas all mite groups increased in activity density. However, in the consecutive year, between the extreme drought and control treatments, we only detected differences in soil microbial biomass. In the cases of severe drought, moderate drought and water addition, we did not find considerable changes across the microarthropods, except in the case of epedaphic Collembola. In the cases of the water addition and drought treatments, the duration and timing of the manipulation seemed to be more important for soil mesofauna than their severity (i.e., the level of soil moisture decrease). We suggest that in these extreme habitats, soil mesofauna are able to survive extreme conditions, and their populations recover rapidly, but they may not be able to cope with very long drought periods.

The Dark Side of Animal Phenology

Research exploring the timing of recurring biological events has shown that anthropogenic climate change dramatically alters the phenology of many plants and animals. However, we still lack studies on how climate change might alter the phenology of soil invertebrates as well as how this can subsequently affect ecosystem functions.

An Opto-Electronic Sensor for Detecting Soil Microarthropods and Estimating Their Size in Field Conditions

Methods to estimate density of soil-dwelling arthropods efficiently, accurately and continuously are critical for investigating soil biological activity and evaluating soil management practices. Soil-dwelling arthropods are currently monitored manually. This method is invasive, and time- and labor-consuming. Here we describe an infrared opto-electronic sensor for detection of soil microarthropods in the size range of 0.4–10 mm. The sensor is built in a novel microarthropod trap designed for field conditions. It allows automated, on-line, in situ detection and body length estimation of soil microarthropods. In the opto-electronic sensor the light source is an infrared LED. Two plano-convex optical lenses are placed along the virtual optical axis. One lens on the receiver side is placed between the observation space at 0.5–1 times its focal length from the sensor, and another emitter side lens is placed between the observation space and the light source in the same way. This paper describes the setup and operating mechanism of the sensor and the control unit, and through basic tests it demonstrates its potential in automated detection of soil microarthropods. The sensor may be used for monitoring activities, especially for remote observation activities in soil and insect ecology or pest control.