Food web engineering: ecology and evolution to improve biological pest control

Host-parasitoid trophic webs in complex agricultural systems

The composition and dynamics of ecological communities are complex because of the presence of large numbers of organisms, belonging to many different species, each with their own evolutionary history, and their numerous interactions. The construction and analysis of trophic webs summarizes interactions across trophic levels and links community structure to properties such as ecosystem services. We focus on agro-ecological communities, which may be simpler than natural communities but nonetheless present considerable challenges to describe and understand. We review the characteristics and study of communities comprised of plants, phytophagous insects and insect parasitoids with particular regard to the maintenance of sustainable agro-ecological communities and ecosystem services, especially biological pest control. We are constrained to largely overlook other members of these communities, such as hyper-parasitoids, predators, parasites and microbes. We draw chiefly on recent literature while acknowledging the importance of many advances made during the immediately preceding decades. Trophic web construction and analysis can greatly improve the understanding of the role and impact of herbivores and natural enemies in agro-ecological communities and the various species interactions, such as apparent competition, which assists biocontrol strategies. The study of trophic webs also helps in predicting community ecology consequences of externally-driven changes to agro-ecosystems.

Advances in data-collection tools and analytics for crop pest and disease management

Innovative methods in data collection and analytics for pest and disease management are advancing together with computational efficiency. Tools, such as the open-data kit, research electronic data capture, fall armyworm monitoring, and early warning- system application and remote sensing have aided the efficiency of all types of data collection, including text, location, images, audio, video, and others. Concurrently, data analytics have also evolved with the application of artificial intelligence and machine learning (ML) for early warning and decision-support systems. ML has repeatedly been used for the detection, diagnosis, modeling, and prediction of crop pests and diseases. This paper thus highlights the innovations, implications, and future progression of these technologies for sustainability.

Biological control needs evolutionary perspectives of ecological interactions

While ecological interactions have been identified as determinant for biological control efficiency, the role of evolution remains largely underestimated in biological control programs. With the restrictions on the use of both pesticides and exotic biological control agents (BCAs), the evolutionary optimization of local BCAs becomes central for improving the efficiency and the resilience of biological control. In particular, we need to better account for the natural processes of evolution to fully understand the interactions of pests and BCAs, including in biocontrol strategies integrating human manipulations of evolution (i.e., artificial selection and genetic engineering). In agroecosystems, the evolution of BCAs traits and performance depends on heritable phenotypic variation, trait genetic architecture, selection strength, stochastic processes, and other selective forces. Humans can manipulate these natural processes to increase the likelihood of evolutionary trait improvement, by artificially increasing heritable phenotypic variation, strengthening selection, controlling stochastic processes, or overpassing evolution through genetic engineering. We highlight these facets by reviewing recent studies addressing the importance of natural processes of evolution and human manipulations of these processes in biological control. We then discuss the interactions between the natural processes of evolution occurring in agroecosystems and affecting the artificially improved BCAs after their release. We emphasize that biological control cannot be summarized by interactions between species pairs because pests and biological control agents are entangled in diverse communities and are exposed to a multitude of deterministic and stochastic selective forces that can change rapidly in direction and intensity. We conclude that the combination of different evolutionary approaches can help optimize BCAs to remain efficient under changing environmental conditions and, ultimately, favor agroecosystem sustainability.