Tribolium castaneum has a long history as a model species in many distinct subject areas, but improved connections among the genetics, genomics, behavioral, ecological, and pest management fields are needed to fully realize this species’ potential as a model. Tribolium castaneum was the first beetle whose genome was sequenced, and a new genome assembly and enhanced annotation, combined with readily available genomic research tools, have facilitated its increased use in a wide range of functional genomics research. Research into T. castaneum’s sensory systems, response to pheromones and kairomones, and patterns of movement and landscape utilization has improved our understanding of behavioral and ecological processes. Tribolium castaneum has also been a model in the development of pest monitoring and management tactics, including evaluation of insecticide resistance mechanisms. Application of functional genomics approaches to behavioral, ecological, and pest management research is in its infancy but offers a powerful tool that can link mechanism with function and facilitate exploitation of these relationships to better manage this important food pest.

In this study, a basic insect detection system consisting of a manual-focus camera, a Jetson Nano—a low-cost, low-power single-board computer, and a trained deep learning model was developed. The model was validated through a live visual feed. Detecting, classifying, and monitoring insect pests in a grain storage or food facility in real time is vital to making insect control decisions. The camera captures the image of the insect and passes it to a Jetson Nano for processing. The Jetson Nano runs a trained deep-learning model to detect the presence and species of insects. With three different lighting situations: white LED light, yellow LED light, and no lighting condition, the detection results are displayed on a monitor. Validating using F1 scores and comparing the accuracy based on light sources, the system was tested with a variety of stored grain insect pests and was able to detect and classify adult cigarette beetles and warehouse beetles with acceptable accuracy. The results demonstrate that the system is an effective and affordable automated solution to insect detection. Such an automated insect detection system can help reduce pest control costs and save producers time and energy while safeguarding the quality of stored products.

The commercial availability of low-gluten or gluten-free flours has been increasing due to consumer demands, which raises new challenges for the management of stored product insects since little is known about the susceptibility of these flours to infestation. Here we measured oviposition and development of Tribolium castaneum, the red flour beetle, a major pest of wheat and rice mills, on 18 different commercially available flours (almond, amaranth, barley, buckwheat, cassava, coconut, corn, garbanzo, millet, oat, potato, quinoa, rice, rye, sorghum, spelt, teff, and wheat) to assess the level of risk. The average number of eggs laid was highest for teff flour, with wheat, rice, buckwheat, sorghum, barley, rye, and spelt flour also having high oviposition. The lowest oviposition was for potato, quinoa, amaranth and cassava. Holding the eggs laid in these flours and evaluating the ability to develop to the adult stage demonstrated that the average number of adult progeny was highest for teff and wheat, followed by buckwheat, rye, oat, spelt, and millet. In an experiment where single eggs were placed directly in flour, the highest percentage development was in barley, buckwheat, sorghum, spelt, teff, and wheat. Time for 50% of single eggs to develop to adults was quickest for sorghum, spelt, teff, and wheat, while sorghum, buckwheat, corn, spelt, and barley had the quickest development of 90% of eggs to reach adults. There was substantial variation among the different flours which indicates variation in risk of insect infestation. As consumer interest in these flours continues to grow and these alternative flours become more prevalent in food facilities, understanding what diets insects successfully infest is critical to developing management tools.

The lesser grain borer, Rhyzopertha dominica (F.) (Coleoptera: Bostrichidae), is a major global pest of cereal grains. Infestations are difficult to control as larvae feed inside grain kernels, and many populations are resistant to both contact insecticides and fumigants. We sequenced the genome of R. dominica to identify genes responsible for important biological functions and develop more targeted and efficacious management strategies. The genome was assembled from long read sequencing and long-range scaffolding technologies. The genome assembly is 479.1 Mb, close to the predicted genome size of 480.4 Mb by flow cytometry. This assembly is among the most contiguous beetle assemblies published to date, with 139 scaffolds, an N₅₀ of 53.6 Mb, and L₅₀ of 4, indicating chromosome-scale scaffolds. Predicted genes from biologically relevant groups were manually annotated using transcriptome data from adults and different larval tissues to guide annotation. The expansion of carbohydrase and serine peptidase genes suggest that they combine to enable efficient digestion of cereal proteins. A reduction in the copy number of several detoxification gene families relative to other coleopterans may reflect the low selective pressure on these genes in an insect that spends most of its life feeding internally. Chemoreceptor genes contain elevated numbers of pseudogenes for odorant receptors that also may be related to the recent ontogenetic shift of R. dominica to a diet consisting primarily of stored grains. Analysis of repetitive sequences will further define the evolution of bostrichid beetles compared to other species. The data overall contribute significantly to coleopteran genetic research.

The lesser grain borer, Rhyzopertha dominica (F.), is a highly diverse feeder and widely distributed throughout the United States in agricultural and non-agricultural landscapes. Six four-funnel Lindgren traps were deployed in feed mill, grain elevator, and native prairie landscapes, to determine the most attractive food and pheromone combination (attractant) and patterns in seasonal captures. Traps were baited with combinations of wheat (crimped, high moisture, pre-fed) with or without an R. dominica specific aggregation pheromone in 2017 and 2018. Traps were deployed for 48 h, collected, and the number of R. dominica counted. Rhyzopertha dominica was captured among all landscapes with all attractants. There was a significant correlation between temperature and R. dominica captures, with peak captures occurring during the warmest months. Significantly more R. dominica adults were captured in traps containing the pheromone. In 2017, pheromone traps captured 818% more R. dominica and 543% more than in 2018. The pheromone component in the trap was more attractive than any natural stored wheat condition and should be included in future studies. Understanding the seasonal patterns and changes in capture rates in agricultural and non-agricultural landscapes may be useful in determining times of increased immigration pressure into the newly harvested grain.

Mating disruption is a commercially available management tactic for pyralid moths, which are pests of stored products. However, evaluations of efficacy have had limited replication, which limits the ability to draw conclusions about its effectiveness or the impact of different variables on its efficacy. We evaluated the mating disruption of Plodia interpunctella in 33 retail pet supply stores (6415 to 17,384 m³) and the impact of factors such as insect density and application rate on efficacy. Prior to starting MD, the average capture of P. interpunctella was 40.2 ± 3.6 moths/trap/month. Immediately after starting treatment, there was a sharp drop in captures (67.8 ± 4.8%) and then a more gradual overall downward. Overall, under mating disruption, the average reduction was 85.0 ± 3.0%. Geographic location, initial moth density, and pheromone application rate did not significantly impact efficacy. Analysis of the relationships between moth captures and mating disruption dispenser density indicated that competitive mechanisms were the primary mechanisms involved. This was the largest replicated assessment of MD for the management of a post-harvest pest and provides valuable foundational and applied insights into the process. Our results show that a standardized MD program can provide pest suppression in retail stores, but it takes time to be fully effective. Finally, identifying the primary mechanism for efficacy provides important information needed for further refinement of MD programs.