Common resistance mechanisms are deployed by plants against sap-feeding herbivorous insects: insights from a meta-analysis and systematic review

Despite their abundance and economic importance, the mechanism of plant resistance to sap-feeding insects remains poorly understood. Here we deploy meta-analysis and data synthesis methods to evaluate the results from electrophysiological studies describing feeding behaviour experiments where resistance mechanisms were identified, focussing on studies describing host-plant resistance and non-host resistance mechanisms. Data were extracted from 108 studies, comprising 41 insect species across eight insect taxa and 12 host-plant families representing over 30 species. Results demonstrate that mechanisms deployed by resistant plants have common consequences on the feeding behaviour of diverse insect groups. We show that insects feeding on resistant plants take longer to establish a feeding site and have their feeding duration suppressed two-fold compared with insects feeding on susceptible plants. Our results reveal that traits contributing towards resistant phenotypes are conserved across plant families, deployed against taxonomically diverse insect groups, and that the underlying resistance mechanisms are conserved. These findings provide a new insight into plant–insect interaction and highlight the need for further mechanistic studies across diverse taxa

Sharing the burden: Cabbage stem flea beetle pest pressure and crop damage are lower in rapeseed fields surrounded by other rapeseed crops

The cabbage stem flea beetle (Psylliodes chrysocephala) is a significant pest of rapeseed (Brassica napus). Feeding by adult P. chrysocephala can cause severe leaf damage and larval infestation can reduce stem strength, both of which impact crop growth and development, causing substantial yield losses and economic damage. The structure of the agricultural landscape can regulate herbivorous pest populations through top-down and bottom-up processes. This has shown promise in regulating the populations of other herbivorous pests, but remains relatively unexplored for P. chrysocephala. Here we investigate how the structure of the agricultural landscape influences P. chrysocephala abundance (pest pressure) and associated crop damage. We also examine the effect of the landscape on natural enemies and their ability to regulate P. chrysocephala populations. We show that P. chrysocephala populations are primarily regulated through bottom-up processes. We identify adjacency to another rapeseed crop and the total proportion of rapeseed grown in the landscape as key factors influencing beetle pressure, crop damage, and larval infestation, but find no effect of host crop proportions grown in the previous year at the examined scales up to 1 km surrounding focal crops. We also observe positive effects of crop heterogeneity and semi-natural habitat proportions on natural enemy abundance and diversity; however, these increases had no direct impact on P. chrysocephala. Bottom-up processes appear to contribute to herbivorous pest regulation by diluting beetles in the landscape, and could represent an important mechanism for sustainably managing pest populations by adapting the proportions and neighbourhoods of rapeseed crops at small to large spatial scales

Low prevalence of secondary endosymbionts in aphids sampled from rapeseed crops in Germany

Peach-potato aphids, Myzus persicae Sulzer (Hemiptera:Aphididae), and cabbage aphids, Brevicoryne brassicae Linnaeus (Hemiptera:Aphididae), are herbivorous insects of significant agricultural importance. Aphids can harbour a range of non-essential (facultative) endosymbiotic bacteria that confer multiple costs and benefits to the host aphid. A key endosymbiont-derived phenotype is protection against parasitoid wasps, and this protective phenotype has been associated with several defensive enodsymbionts. In recent years greater emphasis has been placed on developing alternative pest management strategies, including the increased use of natural enemies such as parasitoids wasps. For the success of aphid control strategies to be estimated the presence of defensive endosymbionts that can potentially disrupt the success of biocontrol agents needs to be determined in natural aphid populations. Here, we sampled aphids and mummies (parasitised aphids) from an important rapeseed production region in Germany and used multiplex PCR assays to characterise the endosymbiont communities. We found that aphids rarely harboured facultative endosymbionts, with 3.6% of M. persicae and 0% of B. brassicae populations forming facultative endosymbiont associations. This is comparable with endosymbiont prevalence described for M. persicae populations surveyed in Australia, Europe, Chile, and USA where endosymbiont infection frequencies range form 0-2%, but is in contrast with observations from China where M. persicae populations have more abundant and diverse endosymbiotic communities (endosymbionts present in over 50% of aphid populations)

Can artificial intelligence be integrated into pest monitoring schemes to help achieve sustainable agriculture? An entomological, management and computational perspective

1. Recent years have seen significant advances in artificial intelligence (AI) technology. This advancement has enabled the development of decision support systems that support farmers with herbivorous pest identification and pest monitoring. 2. In these systems, the AI supports farmers through the detection, classification and quantification of herbivorous pests. However, many of the systems under development fall short of meeting the demands of the end user, with these shortfalls acting as obstacles that impede the integration of these systems into integrated pest management (IPM) practices. 3. There are four common obstacles that restrict the uptake of these AI-driven decision support systems. Namely: AI technology effectiveness, functionality under field conditions, the level of computational expertise and power required to use and run the system and system mobility. 4. We propose four criteria that AI-driven systems need to meet in order to overcome these challenges: (i) The system should be based on effective and efficient AI; (ii) The system should be adaptable and capable of handling ‘real-world’ image data collected from the field; (iii) Systems should be user-friendly, device-driven and low-cost; (iv) Systems should be mobile and deployable under multiple weather and climate conditions. 5. Systems that meet these criteria are likely to represent innovative and transformative systems that successfully integrate AI technology with IPM principles into tools that can support farmers