From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring.

Insect monitoring has gained global public attention in recent years in the context of insect decline and biodiversity loss. Monitoring methods that can collect samples over a long period of time and independently of human influences are of particular importance. While these passive collection methods, e.g. suction traps, provide standardized and comparable data sets, the time required to analyze the large number of samples and trapped specimens is high. Another challenge is the necessary high level of taxonomic expertise required for accurate specimen processing. These factors create a bottleneck in specimen processing. In this context, machine learning, image recognition and artificial intelligence have emerged as promising tools to address the shortcomings of manual identification and quantification in the analysis of such trap catches. Aphids are important agricultural pests that pose a significant risk to several important crops and cause high economic losses through feeding damage and transmission of plant viruses. It has been shown that long-term monitoring of migrating aphids using suction traps can be used to make, adjust and improve predictions of their abundance so that the risk of plant viruses spreading through aphids can be more accurately predicted. With the increasing demand for alternatives to conventional pesticide use in crop protection, the need for predictive models is growing, e.g. as a basis for resistance development and as a measure for resistance management. In this context, advancing climate change has a strong influence on the total abundance of migrating aphids as well as on the peak occurrences of aphids within a year. Using aphids as a model organism, we demonstrate the possibilities of systematic monitoring of insect pests and the potential of future technical developments in the subsequent automated identification of individuals through to the use of case data for intelligent forecasting models. Using aphids as an example, we show the potential for systematic monitoring of insect pests through technical developments in the automated identification of individuals from static images (i.e. advances in image recognition software). We discuss the potential applications with regard to the automatic processing of insect case data and the development of intelligent prediction models.

Does the Aphid Alarm Pheromone (E)-ß-farnesene Act as a Kairomone under Field Conditions?

Insect natural enemies use several environmental cues for host/prey finding, and adjust their foraging behavior according to these signals. In insects, such cues are mainly chemical, derived from the host plant or the prey itself. The aphid alarm pheromone, (E)-ß-farnesene (EBF), is believed to be such a cue, because several aphid enemies are able to perceive EBF and show attractant behavior. These studies are, however, based mainly on electroantennogram or olfactometer assays, and often use unnaturally high pheromone concentrations. It is, therefore, unclear if EBF is used to locate prey in the field when only naturally released amounts are present. We monitored the frequencies and durations of plant visits by aphid natural enemies in the field using long-duration camera observations. By placing pheromone releasers emitting no, natural or exaggerated amounts of EBF next to small colonies of pea aphids (Acyrthosiphon pisum), we analyzed if EBF presence altered long-range foraging behavior of natural enemies. Thirteen potential groups of aphid natural enemies were observed in 720 hr of analyzed video data. There was no effect of EBF on the number of predator visits to an aphid colony, or on predator patch residence times. The number of plant visits increased at exaggerated EBF amounts but not at natural EBF levels. We conclude that while there may be potential for use of high EBF concentrations for agricultural pest management strategies, an ecological role of EBF as a kairomone in a natural context is doubtful.

Real-time monitoring of (E)-ß-farnesene emission in colonies of the pea aphid, Acyrthosiphon pisum, under lacewing and ladybird predation.

Aphids (Homoptera) are constantly under attack by a variety of predators and parasitoids. Upon attack, most aphids release alarm pheromone that induces escape behavior in other colony members, such as dropping off the host plant. In the pea aphid, Acyrthosiphon pisum Harris (Aphididae), the only component of this alarm pheromone is the sesquiterpene (E)-ß-farnesene (EBF). EBF is thought to act as a kairomone by attracting various species of parasitoids and predators including lacewings and ladybirds. Lately, it also was proposed that EBF is constantly emitted in low quantities and used by aphids as a social cue. No study has focused on emission dynamics of this compound over a long time period. Here, we present the first long-time monitoring of EBF emission in aphid colonies using real-time monitoring. We used a zNose(TM) to analyze the headspace of colonies of the pea aphid, under lacewing (Neuroptera: Chrysopidae) and ladybird (Coleoptera: Coccinellidae) predation, over 24 hr. We found no emission of EBF in the absence of predation. When either a ladybird adult or a lacewing larva was placed in an aphid colony, EBF was detected in the headspace of the colonies in the form of emission blocks; i.e., periods in which EBF was emitted alternating with periods without EBF emission. The number of emission blocks correlated well with the number of predation events that were determined at the end of each experiment. There was no circadian rhythm in alarm pheromone emission, and both predators were active during both night and day. Our results show that alarm pheromone emission pattern within an aphid colony is driven by the feeding behavior of a predator.

Modulation of aphid alarm pheromone emission of pea aphid prey by predators.

Recent studies on animal alarm signaling have shown that alarm calls generally are not uniform, but may vary depending on the type and intensity of threat. While alarm call variability has been studied intensively in birds and mammals, little is known about such variation in insects. We investigated variability in alarm signaling in aphids, group-living insect herbivores. Under attack, aphids release droplets containing a volatile alarm pheromone, (E)-ß-farnesene (EBF), that induces specific escape behavior in conspecifics. We used a handheld gas chromatograph (zNose™), which allows real-time volatile analysis, to measure EBF emission by pea aphids, Acyrthosiphon pisum, under attack from different predators, lacewing or ladybird larvae. We demonstrate that aphid alarm signaling is affected by the predator species attacking. Ladybirds generally elicited smaller EBF emission peaks and consumed aphids more quickly, resulting in lower total EBF emission compared to lacewing attacks. In 52 % of the replicates with lacewings and 23 % with ladybirds, no EBF was detectable in the headspace, although aphids secreted cornicle droplets after attack. We, therefore, examined EBF amounts contained in these droplets and the aphid body. While all aphid bodies always contained EBF, many secreted droplets did not. Our experiments show that alarm signaling in insects can be variable, and both the attacker as well as the attacked may affect alarm signal variation. While underlying mechanisms of such variation in aphid-predator interactions need to be investigated in more detail, we argue that at least part of this variation may be adaptive for the predator and the aphid.