Southern corn rust, caused by Puccinia polysora Underw., is a worldwide maize disease. With the changes in global climate and farming systems, southern corn rust has become one of the major diseases that seriously threaten the safety of maize production in China. The disease is airborne and presents regional epidemic characteristics in China; however, its population structure in different regions is still unclear. In this study, we used high-throughput sequencing techniques with a genotyping-by-sequencing approach to study the population structure of P. polysora in the pathogen's winter-reproductive regions. Population genetic analysis indicated that the P. polysora isolates from Ledong, Hainan, collected in July formed a distinct genetic group, indicating seasonal genetic differentiation within this region. However, the remaining isolates from Hainan, Guangdong, and Guangxi were clustered into two main genetic groups, with no significant genetic differentiation detected among the populations from these three provinces. This suggests frequent genetic exchange among P. polysora populations in Hainan, Guangdong, and Guangxi, leading to overall genetic homogeneity. These findings underscore the role of genetic connectivity in shaping the population structure of P. polysora in the pathogen's winter-reproductive regions, offering novel insights into its genetic dynamics. Furthermore, the results provide valuable information to support the development of effective strategies for managing P. polysora in China.

BACKGROUND:Stem borer pests can cause substantial economic losses in growing Poaceae crops by damaging their stalks, where their concealed boring habitats shield them from natural enemies, human detection, and limit pesticide penetration. Studying the influence of climate factors on these pests provides critical information for sustainable pest control and outbreak prediction under global changes. This study systematically investigated the field outbreak of important stem borers in corn, rice, and wheat: Ostrinia furnacalis Guenée (Lepidoptera: Crambidae), Chilo suppressalis Walker (Lepidoptera: Crambidae), Scirpophaga incertulas Walker (Lepidoptera: Pyralidae), and Meromyza saltatrix Linnaeus (Diptera: Chloropidae) in China from 1987 to 2019 in relations to climate change. RESULTS:Across the studied species, we found that monthly precipitation has regional and species-specific effects on the outbreaks of borers. Furthermore, we conducted meta-regressions of coefficients of models to summarize general effects of seasonal climate changes on borer pest outbreaks in 3 grain crops and sugarcane across all study regions. We found a universal phenomenon across study species in subtropical areas that annual outbreaks of borers were more severe with drier summers. CONCLUSION:This study not only provides critical knowledge for understanding stem borer responses to climate change in Poaceae crops but also establishes a forecasting framework for pest outbreaks. Our findings establish a scientific outbreak alarm for major pest outbreaks in main cereal crops and sugar crops. © 2026 Society of Chemical Industry.

Cotton leaf mites are pests that cause irreparable damage to cotton and pose a severe threat to the cotton yield, and the application of unmanned aerial vehicles (UAVs) to monitor the incidence of cotton leaf mites across a vast region is important for cotton leaf mite prevention. In this work, 52 vegetation indices were calculated based on the original five bands of spliced UAV multispectral images, and six featured indices were screened using Shapley value theory. To classify and identify cotton leaf mite infestation classes, seven machine learning classification models were used: random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB), light gradient boosting machine (LGBM), K-Nearest Neighbors (KNN), decision tree (DT), and gradient boosting decision tree (GBDT) models. The base model and metamodel used in stacked models were built based on a combination of four models, namely, the XGB, GBDT, KNN, and DT models, which were selected in accordance with the heterogeneity principle. The experimental results showed that the stacked classification models based on the XGB, KNN base model, and DT metamodel were the best performers, outperforming other integrated and single individual models, with an overall accuracy of 85.7% (precision: 93.3%, recall: 72.6%, and F1-score: 78.2% in the macro_avg case; precision: 88.6%, recall: 85.7%, and F1 score: 84.7% in the weighted_avg case). This approach provides support for using UAVs to monitor the cotton leaf mite prevalence over vast regions.

Cotton aphids (Aphis gossypii Glover) pose a significant threat to cotton growth, exerting detrimental effects on both yield and quality. Conventional methods for pest and disease surveillance in agricultural settings suffer from a lack of real-time capability. The use of edge computing devices for real-time processing of cotton aphid-damaged leaves captured by field cameras holds significant practical research value for large-scale disease and pest control measures. The mainstream detection models are generally large in size, making it challenging to achieve real-time detection on edge computing devices with limited resources. In response to these challenges, we propose GVC-YOLO, a real-time detection method for cotton aphid-damaged leaves based on edge computing. Building upon YOLOv8n, lightweight GSConv and VoVGSCSP modules are employed to reconstruct the neck and backbone networks, thereby reducing model complexity while enhancing multiscale feature fusion. In the backbone network, we integrate the coordinate attention (CA) mechanism and the SimSPPF network to increase the model’s ability to extract features of cotton aphid-damaged leaves, balancing the accuracy loss of the model after becoming lightweight. The experimental results demonstrate that the size of the GVC-YOLO model is only 5.4 MB, a decrease of 14.3% compared with the baseline network, with a reduction of 16.7% in the number of parameters and 17.1% in floating-point operations (FLOPs). The mAP@0.5 and mAP@0.5:0.95 reach 97.9% and 90.3%, respectively. The GVC-YOLO model is optimized and accelerated by TensorRT and then deployed onto the embedded edge computing device Jetson Xavier NX for detecting cotton aphid damage video captured from the camera. Under FP16 quantization, the detection speed reaches 48 frames per second (FPS). In summary, the proposed GVC-YOLO model demonstrates good detection accuracy and speed, and its performance in detecting cotton aphid damage in edge computing scenarios meets practical application needs. This research provides a convenient and effective intelligent method for the large-scale detection and precise control of pests in cotton fields.

The fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae) (FAW), is an invasive and destructive polyphagous pest that poses a significant threat to global agricultural production. The FAW mainly damages maize, with a particular preference for V3–V5 (third to fifth leaf collar) plant stages in northern China. How the FAW moth precisely locates maize plants in the V3–V5 stage at night remains unclear. The aims of this study were to evaluate the visual and olfactory cues used by the FAW to identify its host plant, maize, in order to select attractants with better trapping efficacy. Hyperspectral analysis of maize plants at different growth stages using the ASD Fieldspec 4 spectrometer was performed using mimics (moths or maize leaves sealed with transparent plastic sheets) and black cloth-covered plants for single visual and single olfactory attraction experiments. Gas chromatography–mass spectrometry (GC-MS) was used to analyze volatiles emitted from V3–V5 stage maize leaves. Volatile organic chemicals (VOCs) were screened using electroantennography (EAG) and Y-tube. Attractor efficacy was validated using mimics + VOCs. Results showed very little variance in the spectral reflectance curve of the maize at different growth stages. Fifteen VOCs were identified in the V3–V5 stage leaves of three different maize varieties, of which cis-3-hexenyl acetate and myrcene were found in relatively high concentrations in these maize varieties. The frequency of visits attracted by single visual stimuli was significantly lower than that attracted by single olfactory or olfactory + visual cues. The attractiveness of foliar cis-3-hexenyl acetate increased as its concentration decreased. The combination of mimics + cis-3-hexenyl acetate (1 ng/μL) increased host detection efficiency and stimulated mating behavior. These results indicate that the nocturnal insect FAW primarily uses olfactory cues for host identification, with visual cues serving as a complementary modality. The synergistic effect of olfactory and visual cues increases the efficiency of host recognition. We found that cis-3-hexenol acetate at a concentration from maize leaves is a reliable olfactory signal for the FAW. When using host plant VOCs as attractants to control adult FAWs, the role of visual cues must be considered.

The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2-10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV.

Changes in land use is an important driver of insect pest population dynamics, but the long-term effects of land use may be contingent on changes in some factors. To identify potential effects of change in cropping pattern on agricultural pest population trends, data from large temporal and spatial scales are needed but are rarely available. Here, we used long-term (15 years) pest monitoring data across a regional scale and across independent gradients of land-use intensity at the landscape level (61 agro-landscapes with a radius of 2.0 km), to investigate the effects of the expansion of area devoted to major cereal crops on population trends of polyphagous Helicoverpa armigera in northern China. We found that an increased proportion of the land planted to maize and wheat in the landscape had an indirectly positive effect on the activity density of the summer population of H. armigera by increasing the population density of the preceding spring generations. Stable carbon isotope analysis suggested that maize acted as the source habitat for H. armigera population in the growing season. At the regional level, long-term expansion of maize and wheat production, as well as the contraction of cotton area, was associated with an increased density of H. armigera in spring generations across years, although temperature and precipitation factors also had significant effects on pest population sizes. These results across both temporal and spatial scales indicated that, in addition to Bt cotton contraction, increased cereal crops cultivation was an important driver of the H. armigera population increases in recent decades in northern China.

Introduction Invasive species pose a major threat to global biodiversity and agricultural productivity, yet the genomic mechanisms driving their rapid expansion into new habitats are not fully understood. The fall armyworm, Spodoptera frugiperda, originally from the Americas, has expanded its reach across the Old World, causing substantial reduction in crop yield. Although the hybridization between two genetically distinct strains has been well-documented, the role of such hybridization in enhancing the species’ invasive capabilities remains largely unexplored. Objectives This study aims to investigate the contributions of hybridization and natural selection to the rapid invasion of the fall armyworm. Methods We analyzed the whole-genome resequencing data from 432 individuals spanning its global distribution. We identified the genomic signatures of selection associated with invasion and explored their linkage with the Tpi gene indicating strain differentiation. Furthermore, we detected signatures of balancing selection in native populations for candidate genes that underwent selective sweeps during the invasion process. Results Our analysis revealed pronounced genomic differentiation between native and invasive populations. Invasive populations displayed a uniform genomic structure distinctly different from that of native populations, indicating hybridization between the strains during invasion. This hybridization likely contributes to maintaining high genetic diversity in invasive regions, which is crucial for survival and adaptation. Additionally, polymorphisms on genes under selection during invasion were possibly preserved through balancing selection in their native environments. Conclusion Our findings reveal the genomic basis of the fall armyworm’s successful invasion and rapid adaptation to new environments, highlighting the important role of hybridization in the dynamics of invasive species.

The Yangtze River Delta, located in East China, is an important passage on the eastern pathway of the northward migration of fall armyworm Spodoptera frugiperda (Smith) in China, connecting China's year-round breeding area and the Huang-Huai-Hai summer maize area. Clarifying the migration dynamics of S. frugiperda in the Yangtze River Delta is of great significance for the scientific control and prevention of S. frugiperda in the Yangtze River Delta, even in the Huang-Huai-Hai region and Northeast China. This study is based on the pest investigation data of S. frugiperda in the Yangtze River Delta from 2019 to 2021, combining it with the migration trajectory simulation approach and the synoptic weather analysis. The result showed that S. frugiperda migrated to the Yangtze River Delta in March or April at the earliest, and mainly migrated to the south of the Yangtze River in May, which can be migrated from Guangdong, Guangxi, Fujian, Jiangxi, Hunan and other places. In May and June, S. frugiperda migrated further into the Jiang-Huai region, and its source areas were mainly distributed in Jiangxi, Hunan, Zhejiang, Jiangsu, Anhui and Hubei provinces. In July, it mainly migrated to the north of Huai River, and the source areas of the insects were mainly distributed in Jiangsu, Anhui, Hunan, Hubei and Henan. From the south of the Yangtze River to the north of the Huai River, the source areas of S. frugiperda were constantly moving north. After breeding locally, S. frugiperda can not only migrate to other regions of the Yangtze River Delta, but also to its surrounding provinces of Jiangxi, Hunan, Hubei, Henan, Shandong and Hebei, and even cross the Shandong Peninsula into Northeast China such as Liaoning and Jilin provinces. Trajectory simulation showed that the emigrants of S. frugiperda from the Yangtze River Delta moved northward, westward and eastward as wind direction was quite diverse in June-August. This paper analyzes the migration dynamics of S. frugiperda in the Yangtze River Delta, which has important guiding significance for the monitoring, early warning and the development of scientific prevention and control strategies for whole country.

Southern corn rust (SCR) caused by Puccinia polysora is one of the most devastating diseases in the world. In recent years, SCR has been upgraded from a minor to a major disease around the world, including in China. However, little is known about its population genetics and structure in China. In this study, we analyzed 288 isolates collected from various localities during 2017 in seven Chinese provinces: Guangxi, Guangdong, Anhui, Hunan, Shandong, Henan, and Shaanxi. The isolates were analyzed using nine microsatellite markers. The population structure, genetic diversity, and reproduction mode of P. polysora were investigated based on genotype data. Strong genotypic diversity was detected and clonal reproduction was dominant. The populations collected from the pathogen's winter-reproductive regions harbored more genotypes than those collected from the pathogen's epidemic regions. The spatial differences in genotypic richness, and evenness among the populations were significant, and showed a decreasing trend from south to north. Most isolates were clustered into two clonal groups. Two high-frequency multilocus genotypes (MLGs), MLG1 and MLG2, were widely distributed in all populations. Our analyses confirmed that P. polysora employed clone dispersal from the pathogen's winter-reproductive regions to the pathogen's epidemic regions, and in addition to the sources from the pathogen's winter-reproductive regions, the pathogen in Anhui and Hunan might also have other sources from areas such as Taiwan, China, or/and Southeast Asia, and the pathogen went through a genetic bottleneck during its dispersal. These findings provide initial insights into the reproduction mode and dispersal pathways of P. polysora in China.

AbstractBackgroundIn warm regions or seasons of the year, the planetary boundary layer is occupied by a huge variety and quantity of insects, but the southward migration of insects (in East Asia) in autumn is still poorly understood.MethodsWe collated daily catches of the oriental armyworm (Mythimna separata) moth from 20 searchlight traps from 2014 to 2017 in China. In order to explore the autumn migratory connectivity ofM. separatain East China, we analyzed the autumn climate and simulated the autumn migration process of moths.ResultsThe results confirmed that northward moth migration in spring and summer under the East Asian monsoon system can bring rapid population growth. However, slow southerly wind (blowing towards the north) prevailed over the major summer breeding area in North China (33°–40° N) due to a cold high-pressure system located there, and this severely disrupts the autumn ‘return’ migration of this pest. Less than 8% of moths from the summer breeding area successfully migrated back to their winter-breeding region, resulting in a sharp decline of the population abundance in autumn. As northerly winds (blowing towards the south) predominate at the eastern periphery of a high-pressure system, the westward movement of the high-pressure system leads to more northerlies over North China, increasing the numbers of moths migrating southward successfully. Therefore, an outbreak year ofM. separatalarvae was associated with a more westward position of the high-pressure system during the previous autumn.ConclusionThese results indicate that the southward migration in autumn is crucial for sustaining pest populations ofM. separata, and the position of the cold high-pressure system in September is a key environmental driver of the population size in the next year. This study indicates that the autumn migration of insects in East China is more complex than previously recognized, and that the meteorological conditions in autumn are an important driver of migratory insects’ seasonal and interannual population dynamics.

【Objective】To provide supports for improving the monitoring and warning level of the beet webworm, Loxostege sticticalis, the seasonal spatiotemporal distribution in Northern China, the sources and relationship between L. sticticalis in North and Northeast China, within and outside China were studied in 2020.【Method】The searchlight traps were assembled to daily monitoring in Beijing, Tianjin, Hebei, Liaoning, Shanxi and Inner Mongolia. Based on the monitoring data of searchlight traps at the plant protection stations in the 6 provinces (municipality, Autonomous Region), the population fluctuations and migration pattern of L. sticticalis in Northern China were analyzed by using GrADS and R. The FNL data were processed by GrADS software, to obtain wind field information and draw the map. A three-dimensional particle trajectory analysis program based on WRF model was used to simulate the migration route of L. sticticalis, and the trajectory simulation results were plotted by ggplot2 3.3.0 package of R3.8.【Result】The overwintering generation of L. sticticalis adults mainly occurred in Shanxi, Hebei and Inner Mongolia, and a large number of adults of the 1st generation were found in Huade of Inner Mongolia and Kangbao of Hebei in 2020. During the typical migration period, North China and Northeast China were affected by frontal and cyclonic processes. The southward or southwest low-level jet at the front of the northeast cyclone provided favorable conditions for the long-distance migration of L. sticticalis into the northeast region, but the northwest airflow at the back of it blocked the migration path. The results showed that most of the overwintering adults stayed in North China and some of them migrated to Northeast China and the border of China, Mongolia and Russia with the help of southwest airflow. In late May, the main source in Hinggan League of Inner Mongolia came from the overwintering area of L. sticticalis in North China. In early June, part of them came from the overwintering area of North China, part of them came from the junction of China, Mongolia and Russia. In late June, the main source came from North China. The adults of the 1st generation of L. sticticalis mainly came from the 1st generation larva occurrence areas in central and western Inner Mongolia and the border between China and Mongolia. Affected by the frontal weather, they gathered and landed in Huade of Inner Mongolia and Kangbao of Hebei, and further migrated to the northeast.【Conclusion】2020 is a typical year of outbreak since the population of L. sticticalis has risen again in 2018. Strong air currents are important reasons for its successful migration, the convergence of wind shear and cyclone center causes the migrating adults to gather and land in large area, resulting in a sudden increase of local insect population. Both the overwintering generation and the 1st generation of L. sticticalis in North China and Northeast China were closely related to the foreign sources. It is of great significance to carry out the regional monitoring and prediction and forecasting in advance for L. sticticalis.

监测与预报是农作物病虫害防治的基础工作,也是种植业领域生物安全风险防控的前沿阵地.围绕《农作物病虫害防治条例》和《中华人民共和国生物安全法》两部上位法对农作物病虫害监测与预报工作的法律要求,阐述了出台《农作物病虫害监测与预报管理办法》是推进植保全程法治化的重要内容、贯彻生物安全风险防控的重要抓手,并从监测与预报工作的权责归属、监测网络建设原则与要求、测报技术全流程制度化等方面,论述了《办法》在构建主体明晰、分工协作的权责体系,落实"织牢织密监测网络"要求、强化设施设备等条件建设,全流程制度化提升技术工作等方面的作用,以期为全面理解和贯彻落实《办法》、依法依规开展农作物病虫害监测与预报工作提供借鉴和参考.

【Background】As a major migratory insect pest putting the whole world on alert, the fall armyworm Spodoptera frugiperda warned by Food and Agriculture Organization of the United Nations (FAO) poses a serious threat to the agriculture production (including maize) of China from the end of 2018. Taking advantages of the long-distance transport of seasonal monsoons and its self-powered migratory capacity, S. frugiperda performs two migration pathways to fly across the eastern and western China separately, which has caused regional dispersal and severe infestation. According to 2-year systematic investigation in China, the west migration route of S. frugiperda ends in Northwestern China, especially in Ningxia and Alxa Left Banner of Inner Mongolia. However, little is known about the source areas of S. frugiperda population invading Northwestern China, and few reports explore the migration route of this devastating pest through the whole western China.【Objective】The objective of this study is to accurately analyze on the key atmospheric factors driving the immigration of S. frugiperda into the Northwestern China, source regions of the first populations to arrive, and Asian monsoon-induced migration pathways of S. frugiperda, which can provide fundamental evidence for the early warning and regional management and control of this invasive pest in China.【Method】Based on the invasion dynamics of S. frugiperda in Ningxia of the Northwestern China and meteorological data, a meso-scale numerical model, insect's flight trajectory calculating program, and Geographic Information System (GIS) were used to identify the atmospheric transport backgrounds, simulate the succussive 1-3 night migration routes and trace their source regions of S. frugiperda in the Northwestern China.【Result】The southerly summer monsoon from July to September each year was the key factor for the successive and successful immigration of S. frugiperda into Ningxia and other regions of Northwestern China, of which their major source populations of S. frugiperda were located in southeastern Gansu and eastern Sichuan, while some were from western Shaanxi. In addition, southwestern Chongqing, northeastern Yunnan and part of western Shanxi could possibly provide population source of S. frugiperda.【Conclusion】Under the influence of southerly Asian summer monsoons, S. frugiperda can fly towards the north for 1-3 successive nights via its west migration pathway “Yunnan-Sichuan and Chongqing-Shaanxi and Gansu-Ningxia” in China, which originates from Myanmar and ends in Inner Mongolia, China. In particular, the government should be vigilant against the occurrence and damage of this devastating pest in the maize-cropping regions of Northwestern China while the preponderance of early southerly wind is advanced and wind speed gets strong during July to September.

The fall armyworm (FAW), Spodoptera frugiperda (J.E. Smith), spread rapidly in Africa and Asia recently, causing huge economic losses in crop production. Fall armyworm caterpillars were first detected in South Korea and Japan in June 2019. Here, the migration timing and path for FAW into the countries were estimated by a trajectory simulation approach implementing the insect's flight behavior. The result showed that FAWs found in both South Korea and Japan were estimated to have come from eastern China by crossing the Yellow Sea or the East China Sea in 10-36 h in three series of migrations. In the first series, FAW moths that arrived on Jeju Island during 22-24 May were estimated to be from Zhejiang, Anhui and Fujian Provinces after 1-2 nights' flights. In the second series, it was estimated that FAW moths landed in southern Korea and Kyushu region of Japan simultaneously or successively during 5-9 June, and these moths mostly came from Guangdong and Fujian Provinces. The FAW moths in the third series were estimated to have immigrated from Taiwan Province onto Okinawa Islands during 19-24 June. During these migrations, southwesterly low-level jets extending from eastern China to southern Korea and/or Japan were observed in the northwestern periphery of the western Pacific Subtropical High. These results, for the first time, suggested that the overseas FAW immigrants invading Korea and Japan came from eastern and southern China. This study is helpful for future monitoring, early warning and the source control of this pest in the two countries.

2019年12月-2020年3月初调查结果显示,草地贪夜蛾在我国云南、广东、海南、四川、广西、福建、贵州7省(区)的47个市(州)183个县(市、区)发生,玉米是其冬季主要寄主作物,局部地区可见为害小麦和甘蔗;福建、广东、广西、贵州在幼虫冬繁区以外地区诱到成虫.浙江、湖南、江西、重庆等4省(市)的16个市27个县(市、区)在30个点查到活虫(蛹).验证了我国草地贪夜蛾冬繁区即周年繁殖区位于28°N以南,即1月份平均温度10℃等温线以南区域;越冬区在28°N-31°N之间,即1月份平均温度6℃等温线到10℃等温线之间.

The fall armyworm (FAW, Lepidoptera: Noctuidae), Spodoptera frugiperda (J. E. Smith), invaded China in mid-December 2018; since then, it has become a great threat to Chinese agricultural production. Qinling Mountains–Huaihe River region (QM–HRR) is the transitional zone between northern and southern China, an important region for both corn and wheat production. Based on the actual occurrence of QM–HRR invaded by FAW in 2019, daily mean surface air temperature and nocturnal wind conditions at 925 hPa were examined, and migratory routes of FAW moths originated in QM–HRR were modeled by a forward-trajectory-analysis approach. The results indicated that migratory activities of FAW adults emerged in QM–HRR were initiated from late June. The moths from western QM–HRR, where has complex topographic terrain, mainly flied to Ningxia and Inner Mongolia before mid September. However, FAW moths from the eastern QM–HRR primarily engaged in high-altitude northward transport assisted by the prevailing southerly winds before mid August, and the North China Plain was identified as the main destination of FAW. Meanwhile, the migration trajectories of FAW moths had a possibility to reach the Northeast China Plain. From mid August, FAW moths in eastern QM–HRR largely migrated southward and returned to the Yangtze River Valley. This study provides detailed information on the occurrence and migration routes of FAW moths from QM–HRR and will be helpful for early warning and development of integrated pest management strategies for the control of this exotic insect pest.

明确草地贪夜蛾的寄主偏好性有利于更好地掌握其田间种群发生动态,指导农业防控.本文分别以玉米、小麦及其田间常见的禾本科杂草为测试对象,研究草地贪夜蛾成虫、幼虫对嗜食寄主及禾本科杂草的选择性和初孵幼虫取食不同植物的存活率.结果表明:草地贪夜蛾成虫产卵具有明显的选择性,在玉米上日均产卵量为(315.59士49.87)粒,占在所有供试植物上总产卵数量的71.17％,显著高于其在玉米田禾本科杂草上的日均产卵量,其在玉米田不同种类杂草上的产卵量差异不显著.草地贪夜蛾成虫在小麦上的日均产卵量为(243.40士18.24)粒,占所有供试植物上总产卵数量的46.98％,且显著高于在麦田禾本科杂草上的产卵量.1～2龄幼虫对玉米和马唐的取食选择率无显著差异,但显著高于玉米田其他杂草;1～3龄幼虫对小麦和节节麦的取食选择率无显著差异,但显著高于麦田其他杂草;对供试杂草的取食选择率随龄期增高逐渐降低,对玉米和小麦的选择性随龄期增加而增强;草地贪夜蛾1龄幼虫在玉米及玉米田禾本科杂草上的存活率无显著差异,最低为95.89％;在小麦及麦田禾本科杂草上,以雀麦上的成活率最低,为85.22％,显著低于小麦和其他禾本科杂草.

Southern corn rust is a destructive maize disease caused by Puccinia polysora Underw that can lead to severe yield losses. However, genomic information and microsatellite markers are currently unavailable for this disease. In this study, we generated a total of 27,295,216 high-quality cDNA sequence reads using Illumina sequencing technology. These reads were assembled into 17,496 unigenes with an average length of 1015 bp. The functional annotation indicated that 8113 (46.37%), 1933 (11.04%) and 5516 (31.52%) unigenes showed significant similarity to known proteins in the NCBI Nr, Nt and Swiss-Prot databases, respectively. In addition, 2921 (16.70%) unigenes were assigned to KEGG database categories; 4218 (24.11%), to KOG database categories; and 6,603 (37.74%), to GO database categories. Furthermore, we identified 8,798 potential SSRs among 6653 unigenes. A total of 9 polymorphic SSR markers were developed to evaluate the genetic diversity and population structure of 96 isolates collected from Guangdong Province in China. Clonal reproduction of P. polysora in Guangdong was dominant. The YJ (Yangjiang) population had the highest genotypic diversity and the greatest number of the multilocus genotypes, followed by the HY (Heyuan), HZ (Huizhou) and XY (Xinyi) populations. These results provide valuable information for the molecular genetic analysis of P. polysora and related species.