The rapid wide‐scale spread of fall armyworm (Spodoptera frugiperda) has caused serious crop losses globally. However, differences in the genetic background of subpopulations and the mechanisms of rapid adaptation behind the invasion are still not well understood. Here we report the assembly of a 390.38‐Mb chromosome‐level genome of fall armyworm derived from south‐central Africa using Pacific Bioscience (PacBio) and Hi‐C sequencing technologies, with scaffold N50 of 12.9 Mb and containing 22,260 annotated protein‐coding genes. Genome‐wide resequencing of 103 samples and strain identification were conducted to reveal the genetic background of fall armyworm populations in China. Analysis of genes related to pesticide‐ and Bacillus thuringiensis (Bt) resistance showed that the risk of fall armyworm developing resistance to conventional pesticides is very high. Laboratory bioassay results showed that insects invading China carry resistance to organophosphate and pyrethroid pesticides, but are sensitive to genetically modified maize expressing the Bt toxin Cry1Ab in field experiments. Additionally, two mitochondrial fragments were found to be inserted into the nuclear genome, with the insertion event occurring after the differentiation of the two strains. This study represents a valuable advance toward improving management strategies for fall armyworm.

Mikania micranthais one of the top 100 worst invasive species that can cause serious damage to natural ecosystems and substantial economic losses. Here, we present its 1.79 Gb chromosome-scale reference genome. Half of the genome is composed of long terminal repeat retrotransposons, 80% of which have been derived from a significant expansion in the past one million years. We identify a whole genome duplication event and recent segmental duplications, which may be responsible for its rapid environmental adaptation. Additionally, we show thatM. micranthaachieves higher photosynthetic capacity by CO₂absorption at night to supplement the carbon fixation during the day, as well as enhanced stem photosynthesis efficiency. Furthermore, the metabolites ofM. micranthacan increase the availability of nitrogen by enriching the microbes that participate in nitrogen cycling pathways. These findings collectively provide insights into the rapid growth and invasive adaptation.

Due to the monocultural basis of agricultural crops, mutated plant microbes with increased pathogenicity can easily spread in the field and lead to serious yield losses. As a major threat to a wide range of crop plants, oomycete pathogens continuously undergo adaptive evolution to overcome plant defense barriers. However, the genetic basis of their evolution at the molecular level remains largely unknown. Here, we investigated the nature variation and the population genomics of the soybean pathogen Phytophthora sojae by high-throughput genome re-sequencing. Genomic variation analysis revealed uneven “two-speed” evolutionary pattern with genes in gene-sparse regions (GSRs) showing higher rates of structural polymorphisms and positive selection. GSRs are enriched in effector genes and transposase-related genes. Our results also suggested that the NADH oxidase and MIP transporter gene families undergo rapid and diversifying selection. Furthermore, we demonstrated that P. sojae isolates possess varying numbers of RxLR effectors with diverse sequences, totaling 471 members. Among them, 42 core RxLR effectors are assumed to be important for infection. Finally, we observed that Avr genes exhibit abundant sequence variation in P. sojae isolates. Several novel variants lead to the evading of host resistance, including a complete deletion in Avr3c and amino acid mutations in Avr1a. Taken together, our results provide an adaptive landscape of P. sojae at single-nucleotide resolution, as well as resources for further resistance breeding and disease prevention against this important plant pathogen.

The use of yellow sticky traps is a green pest control method that utilizes the pests’ attraction to the color yellow. The use of yellow sticky traps not only controls pest populations but also enables monitoring, offering a more economical and environmentally friendly alternative to pesticides. However, the small size and dense distribution of pests on yellow sticky traps lead to lower detection accuracy when using lightweight models. On the other hand, large models suffer from longer training times and deployment difficulties, posing challenges for pest detection in the field using edge computing platforms. To address these issues, this paper proposes a lightweight detection method, YOLO-YSTs, based on an improved YOLOv10n model. The method aims to balance pest detection accuracy and model size and has been validated on edge computing platforms. This model incorporates SPD-Conv convolutional modules, the iRMB inverted residual block attention mechanism, and the Inner-SIoU loss function to improve the YOLOv10n network architecture, ultimately addressing the issues of missed and false detections for small and overlapping targets while balancing model speed and accuracy. Experimental results show that the YOLO-YSTs model achieved precision, recall, mAP₅₀, and mAP_50–95 values of 83.2%, 83.2%, 86.8%, and 41.3%, respectively, on the yellow sticky trap dataset. The detection speed reached 139 FPS, with GFLOPs at only 8.8. Compared with the YOLOv10n model, the mAP₅₀ improved by 1.7%. Compared with other mainstream object detection models, YOLO-YSTs also achieved the best overall performance. Through improvements to the YOLOv10n model, the accuracy of pest detection on yellow sticky traps was effectively enhanced, and the model demonstrated good detection performance when deployed on edge mobile platforms. In conclusion, the proposed YOLO-YSTs model offers more balanced performance in the detection of pest images on yellow sticky traps. It performs well when deployed on edge mobile platforms, making it of significant importance for field pest monitoring and integrated pest management.

Mikania micrantha is a noxious invasive plant causing enormous economic losses and ecological damage. Soil microbiome plays an important role in the invasion process of M. micrantha, while little is known about its rhizosphere microbiome composition and function. In this study, we identified the distinct rhizosphere microbial communities of M. micrantha, by comparing them with those of two coexisting native plants (Polygonum chinense and Paederia scandens) and the bulk soils, using metagenomics data from field sampling and pot experiment. As a result, the enrichment of phosphorus-solubilizing bacteria Pseudomonas and Enterobacter was consistent with the increased soil available phosphorus in M. micrantha rhizosphere. Furthermore, the pathogens of Fusarium oxysporum and Ralstonia solanacearum and pathogenic genes of type III secretion system (T3SS) were observed to be less abundant in M. micrantha rhizosphere, which might be attributed to the enrichment of biocontrol bacteria Catenulispora, Pseudomonas, and Candidatus Entotheonella and polyketide synthase (PKS) genes involved in synthesizing antibiotics and polyketides to inhibit pathogens. These findings collectively suggested that the enrichment of microbes involved in nutrient acquisition and pathogen suppression in the rhizosphere of M. micrantha largely enhances its adaptation and invasion to various environments.