Inter- and Intrafield Distribution of Cereal Leaf Beetle Species (Coleoptera: Chrysomelidae) in Belgian Winter Wheat.

Cereal leaf beetles (CLBs), a group of chrysomelid beetles of the genus Oulema (Coleoptera: Chrysomelidae), are well-known pest insects of small-grain cereals in many countries of the Northern hemisphere. Due to the small differences in morphology of species within this genus, classification up to species level remains a challenging task. Since an accurate view of species composition is important for developing targeted control strategies, the goal of this study was to unravel the Oulema species composition in Flanders' wheat fields. During three subsequent years at a series of different fields, Oulema species were collected and classified up to species level (2016: 28 fields, 2017: 30 fields, and 2018: 23 fields). This study reveals that the population consists of four different species: Oulema melanopus, Oulema duftschmidi, and Oulema obscura were most frequently encountered, while Oulema rufocyanea was only marginally present. Furthermore, the population was highly dynamic, as the population share of each species varied between different growing seasons and between the various sampling events within each season. The distance from the field edge had a minor influence on the species composition, but the abundance of beetles increased with the distance to the field edge. A discriminant analysis revealed that based on the measurements of various body parts, an accurate classification up to species level is possible. In conclusion, we observed that the population densities fluctuated within and between years, resulting in variable incidence of CLB in winter wheat fields in the Flanders region.

Potentials and Limitations of a Growing Degree Day Approach to Predict the Phenology of Cereal Leaf Beetles.

Cereal leaf beetles (CLBs) are described as an invasive pest of small grain cereals in many regions worldwide. Prediction models aimed to prevent yield losses caused by these feeding insects have been developed by researchers all over the world. As a foundation for many of these prediction models, it is known that a specific number of heat units, or growing degree days (GDDs), is required for an insect to complete a certain physiological process. In this paper, we overview the existing GDD models for CLBs. Furthermore, we used our Belgian input data to compare model predictions with our own observations. Though, the existing models were not able to predict the seasonal trends present in our data: the occurrence of various life stages were monitored earlier then the model predicted. Hence, a weighted GDD model was tested on the data as well: the accumulated GDDs during certain periods were balanced according to the significance of this period for the insect. Rainfall and/or relative humidity were included as well. Based on these selected variables, multiple linear regression models, ridge regression models, and regression trees were fitted. This approach performed considerably better compared to the simple accumulation of GDD. However, based on cross-year cross-location validation method, to gain insight in the future performance of the models, the accuracy was still too low to serve as an accurate warning tool.