Development of a multiprimer metabarcoding approach to understanding trophic interactions in agroecosystems.

To understand trophic interactions and the precise ecological role of each predatory species, it is important to know which arthropod and plant resources are used by generalist predators in agroecosystems. Molecular approaches, such as the use of high-throughput sequencing (HTS), play a key role in identifying these resources. This study develops a multiprimer metabarcoding approach for screening the most common trophic interactions of two predatory arthropods with contrasting morphologies, Rhagonycha fulva (Coleoptera: Cantharidae) and Anthocoris nemoralis (Hemiptera: Anthocoridae) collected from a peach crop. To reduce the time and cost of this metabarcoding approach, we first evaluated the effect of using two different predator-pools of different size (10 and 23 individuals of the same species). We also used our system to analyze the performance of one and two primer pairs in the same library. Our results show that the analysis of 23 individuals together with the use of two primer pairs in the same library optimize the HTS analysis. Using these best-performing conditions, we then analyzed the entire bodies of field-collected predators as well as the washing solutions used to clean the insect bodies. We were able to identify both gut content (i.e., diet) and external pollen load (i.e., on the insects' bodies). This study also demonstrates the importance of washing predatory insects' bodies prior to HTS analysis when the target species have a considerable size (>10 mm) and hairy structures. This metabarcoding approach has significant potential for the study of trophic links in agriculture, revealing expected and unexpected trophic relationships.

Planting period is the main factor for controlling maize rough dwarf disease.

Maize rough dwarf virus (MRDV) is one of the main yield-limiting factors of maize in the Mediterranean. However, knowledge about the interactions between the agroecosystem and the virus-vector-host relationship continues to be limited. We used multi-model inference to test a landscape-scale approach together with variables measured in the field, and we estimated the effects of early and late planting on MRDV incidence. The results revealed that the virus incidence increased by 3% when the planting was delayed, and this increase was coincident with the first peak of the vector population. The variables at the field and landscape scales with a strong effect on virus incidence were the proportions of grasses in adjacent crops, in uncultivated areas, and in edges close to maize plants. Grass plant cover in the edges also affected virus incidence, but these effects varied with the planting period. These findings provide new insights into the causes of MRDV incidence and may provide some guidance to growers to reduce losses caused by the virus. Among the recommendations to be prioritized are early planting, management of grasses at field edges, and non-overlapping cultivation of maize and winter cereals in the same area.