Daniel Joseph Leybourne

Many insects form intimate associations with microbial partners, spanning a continuum from obligate mutualisms to transient associations. These microbial partners confer a range of functional benefits, including protection from natural enemies, detoxification of plant secondary metabolites, and improved fitness. The prevalence and abundance of microbial symbionts can vary between insect populations, indicating that the insect-microbe relationship is influenced by external factors. However, little is known on the factors that determine the assembly, structure, and composition of the microbiome of herbivorous insects. We posit that the factors influencing the insect-microbe relationship occur at the landscape-scale, acting through a combination of bottom-up, top-down, and indirect effects between plants, herbivores, and their natural enemies. We test this in two agro-ecosystems by sampling individual insects from three sap-feeding and one leaf-chewing species. Our results explicitly show for the first time that habitat configurational complexity and the proportion of host-plant crop in the landscape, combined with natural enemy abundance, can act as direct and indirect drivers of microbial symbiosis in herbivorous insects. This highlights the wider implications landscape habitat and ecosystem structure has on species interactions across managed agricultural landscapes.

Aphids are small sap‐feeding insects that are used as a model system for host‐endosymbiont studies. Alongside an obligate bacterial endosymbiont, aphids associate with several facultative species, while these are not essential for aphid survival they provide phenotypic benefits to host aphids. Aphids are also highly efficient vectors of many plant viruses, and association with plant viruses also influences aphid phenotype and behaviour. Thus, both endosymbionts and viruses can play important roles at the population and community level. Here, we use multiple aphid populations and virus isolates to test the interactive effects of virus presence, virus isolate and facultative endosymbionts on aphid fitness and behaviour. We focussed on a persistently transmitted virus, the primary vector of this virus, and a defensive facultative endosymbiont. We observed isolate‐dependent virus effects on winged aphid production, reproductive fitness and aphid dispersal, while endosymbionts predominantly affected aphid feeding behaviour. Endosymbiont infection led to faster initiation of plant tissue probing and increased phloem ingestion. We also identified interactions between virus isolate and endosymbiont infection on early aphid population growth rates, where the endosymbiont effect varied depending on virus isolate present. We showed that virus isolate, endosymbiont infection and combinations of these can drive differences in aphid fitness, dispersal and feeding behaviour. This work highlighted the important role of within‐species diversity for understanding aphids as vectors of important crop viruses. Future strategies for controlling pests and diseases must embrace this complexity to maximise long‐term effectiveness of management approaches.

Aphids are important pests of cereal crops and cause economically significant damage through direct feeding and the transmission of plant viruses. In Europe, the aphid species of greatest concern are the grain aphid (Sitobion avenae Fabricius) and the bird cherry-oat aphid, (Rhopalosiphum padi Linnaeus). Often, cereal crops are dominated by a small number of prolific clonal populations and these populations can differ in phenotypic traits of agricultural importance. There are two heritable factors that influence aphid phenotype: aphid genetic diversity and the presence of endosymbionts.Here, multiple cereal aphid populations are used to determine how heritable factors influence aphid phenotype. Several agriculturally important phenotypic traits are examined, and both endosymbiont- and genotype-derived phenotypes are identified. For S. avenae, aphid genotype influences all phenotypic traits assessed, and association with the facultative endosymbiont Regiella insecticola influences alate morph production with co-infection of R. insecticola and Fukatsuia symbiotica increasing reproductive output. For R. padi, adult aphid morph (apterous or alate) is the key driver behind reproductive output, with a genotype × morph effect also found to influence development time.Overall, these results provide insight into the biological drivers behind phenotypic diversity in agriculturally important aphid species. Being able to associate heritable factors with key phenotypes can generate biological insights into the processes underpinning the dominance of specific aphid clones and can be used to develop pest and disease management strategies based around the phenotypic risk of the aphid populations present.

Cereals are some of the most agriculturally important crops grown worldwide. Cereal aphids are small sap-feeding insects that infest cereals and vector devastating cereal pathogens, principally the yellow dwarf viruses, with severe infection reducing yields by 20 − 80%. Currently, farmers are advised to apply insecticide if a single cereal aphid is found within the crop. However, this is an oversimplification of the biology and ecology of the vector-virus system: Each aphid species can transmit a range of yellow dwarf virus species with variable efficiency, and intra-species diversity within a vector species (genotype) can influence this further. Accounting for this variation in decision-making processes has the potential to help with developing bespoke management options based on the composition of the local vector-virus population, and represents a novel avenue that could be explored to develop more sustainable management strategies. Here we describe a stage-structured model that can estimate vector dispersal and disease spread. We use data for the two main cereal aphid species and the two yellow dwarf virus species of greatest concern in Europe to examine how diversity at each level of the vector-virus system impacts disease spread. More broadly, our modelling framework represents a tool that can be used to explore vector-virus-host interactions in greater detail, and we use cereal systems as a case study. Our modelling scenarios highlight diversity at each scale as an important factor. We show that the two main vector species have contrasting dispersal patterns, and that this interacts with virus transmission efficacy to influence disease spread; genetic variation within a vector population is a key driver behind disease spread and disease risk; and for vector species that transmit multiple virus species, the specific vector-virus species combination is a key risk factor to consider.

The transmission efficiency of aphid-vectored plant viruses can differ between aphid populations. Intra-species diversity (genetic variation, endosymbionts) is a key determinant of aphid phenotype; however, the extent to which intra-species diversity contributes towards variation in virus transmission efficiency is unclear. Here, we use multiple populations of two key aphid species that vector barley yellow dwarf virus (BYDV) strain PAV (BYDV-PAV), the grain aphid ( Sitobion avenae ) and the bird cherry-oat aphid ( Rhopalosiphum padi ), and examine how diversity in vector populations influences virus transmission efficiency. We use Illumina sequencing to characterize genetic and endosymbiont variation in multiple Si. avenae and Rh. padi populations and conduct BYDV-PAV transmission experiments to identify links between intra-species diversity in the vector and virus transmission efficiency. We observe limited variation in the transmission efficiency of Si. avenae, with transmission efficiency consistently low for this species. However, for Rh. padi, we observe a range of transmission efficiencies and show that BYDV transmission efficiency is influenced by genetic diversity within the vector, identifying 542 single nucleotide polymorphisms that potentially contribute towards variable transmission efficiency in Rh. padi . Our results represent an important advancement in our understanding of the relationship between genetic diversity, vector–virus interactions, and virus transmission efficiency.

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. 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. 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. 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. 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.

Every year, plant parasitic nematodes, one of the major groups of plant pathogens, cause a significant loss of crops worldwide. To mitigate crop yield losses caused by nematodes, an efficient nematode monitoring method is essential for plant and crop disease management. In other respects, efficient nematode detection contributes to medical research and drug discovery, as nematodes are model organisms. With the rapid development of computer technology, computer vision techniques provide a feasible solution for quantifying nematodes or nematode infections. In this paper, we survey and categorise the studies and available datasets on nematode detection through deep-learning models. To stimulate progress in related research, this survey presents the potential state-of-the-art object detection models, training techniques, optimisation techniques, and evaluation metrics for deep learning beginners. Moreover, seven state-of-the-art object detection models are validated on three public datasets and the AgriNema dataset for plant parasitic nematodes to construct a baseline for nematode detection.

Wheat is one of the most important arable crops grown worldwide, providing a significant proportion of the daily calorific intake for countries across the globe. Wheat crops are attacked by a diverse range of herbivorous invertebrates, pests, that cause significant yield loss. It is anticipated that yield loss caused by pests will increase in response to a changing climate. Currently, these pests are primarily controlled using pesticides; however, there is an increased need for more sustainable pest management solutions. Economic thresholds represent one avenue that can support the sustainable management of pests. Briefly, thresholds are the number of pests above which there is sufficient risk of yield loss. Here, we review the economic thresholds and prediction methods available for sustainable pest management in wheat. We focus on five economically damaging pests affecting wheat crops in the UK and Europe. For each, we highlight the key period of crop risk to pest attack, identify economic thresholds, and provide an overview of current decision support models that can help estimate crop risk and advise sustainable pest management; we end by proposing areas for future improvement for each pest. Furthermore, we take a novel approach by discussing economic thresholds and their applications to sustainable pest management within the context of crop physiology and the capacity for crops to tolerate pest damage, a consideration that is often overlooked when developing pest management strategies. We use the stem-boring pest, the gout fly, as a case study and use the economic injury level equation to conduct a theoretical assessment of the appropriateness of the current gout fly threshold. This theoretical assessment indicates that wheat crops can tolerate greater gout fly damage than currently considered, and shows that by incorporating crop physiology into sustainable pest tolerance schemes we can work towards developing more appropriate physiological-based pest thresholds.