Insects最新文献

Mating behavior plays a critical role in the reproductive success and population dynamics of insects. Praon volucre is a widespread, broadly oligophagous aphid parasitoid and an important natural enemy in agricultural and natural ecosystems. The present study provides the first detailed characterization of the courtship and mating sequence of P. volucre, examining the effects of host association and behavioral lateralization on mating performance. Behavioral observations were conducted on virgin P. volucre adults emerging from two aphid hosts, Macrosiphum euphorbiae feeding on Citrus aurantium and Aulacorthum solani feeding on Malva neglecta. Males from both host-derived populations exhibited a left-side mounting bias, which was associated with faster mate detection, reduced courtship duration, and higher mating success. Mating behavior remained largely stable across host origins, indicating a high degree of behavioral stability. This behavioral robustness, combined with enhanced mating efficiency driven by lateralization, underscores the suitability of P. volucre as a biological control agent. The results have practical implications for mass-rearing and release programs, suggesting that a potential host switching during rearing is unlikely to compromise reproductive performance.

Insecticidal proteins derived from Bacillus thuringiensis (Bt) have been effectively employed in controlling lepidopteran pests, notably in transgenic crops targeting Spodoptera species. However, concerns have arisen regarding the long-term efficacy due to the emergence of tolerant and resistant insect populations. Prior research suggested that repeated exposures to Bt, which contains a mixture of spores and crystals, may contribute to the development of tolerance; however, the specific effects of sequential exposure to purified Cry1 and Vip3Aa proteins remain unclear. This study aimed to assess whether prior exposure of Spodoptera exigua neonate larvae to sublethal concentrations of Cry1Ab, Cry1Ca or Vip3Aa proteins would heighten their tolerance upon subsequent exposure, and whether such effects would extend to their offspring. Pre-exposure to Cry1Ab or Vip3Aa did not affect larval responses to the toxin. For Cry1Ca, a slight increase was observed under one treatment condition, but the effect was not considered biologically relevant. Transgenerational analysis revealed no enhancement of tolerance; rather, there was a negative impact on the offspring's response in some cases. These findings indicate that although previous studies have documented that sublethal contact with bacterial preparations may significantly affect insect tolerance, exposure to sublethal doses of purified Cry1 and Vip3Aa proteins is unlikely to lead to the development of tolerance in S. exigua.

Chinese honey bees (Apis cerana cerana Fabricius, 1793) are crucial native pollinators in China, with substantial ecological and economic value. Their morphological traits may vary along altitudinal gradients, particularly in hilly regions such as Wanyuan City, Sichuan Province, which provides typical suitable habitat for a locally thriving ecotype known as the Wanyuan honey bee. To elucidate its adaptive variation across environmental gradients, this study investigated the morphological and genetic diversity of this ecotype along an altitudinal transect in Wanyuan. A total of 656 worker individuals from 15 sampling sites (565-1611 m) were analyzed for 13 morphological traits and mitochondrial DNA (tRNAleu-COII fragment) sequences. Results revealed significant altitudinal clines in morphology: Honey bees from mid-altitude sites exhibited larger body size for several traits, while low-altitude bees possessed a significantly higher number of hindwing hamuli. Key taxonomic indices like the cubital index and proboscis length also varied significantly with altitude. Genetic analysis identified 25 haplotypes with high haplotype diversity and nucleotide diversity, indicating substantial genetic variation. Population differentiation was generally low, with one site (Yinbazhai, 900 m) showing relatively higher distinctiveness. The detected high gene flow suggests frequent genetic exchange among most populations. These findings demonstrate that the Wanyuan honey bee exhibits clear altitudinal adaptation in morphology while maintaining high genetic diversity and connectivity. This study provides a crucial scientific basis for the conservation and sustainable management of this genetic resource by highlighting the importance of its population-specific adaptations and genetic structure.

Beekeeping is a widespread economic activity in rural Tanzania, supporting over 2 million livelihoods. The country's forests and woodlands, covering approximately 55% of its land area, provide habitat for an estimated 9.2 million honeybee colonies. This positions Tanzania as the second-largest honey producer in Africa and tenth globally. Absence of current information and effective policies hinders exploitation of the industry's potential. This review presents scientific insights into Tanzania's beekeeping sector, focusing on honeybee species, bee products, management practices, and conservation. Among three documented subspecies of Apis mellifera (Linnaeus, 1758), A. m. scutellata is the most widespread and commonly managed by indigenous beekeepers. Tanzania annually produces over 31,000 tonnes of honey and 1800 tonnes of beeswax, generating approximately USD 77.5 million and contributing about 1% to national GDP. The industry supports livelihoods, food security, and biodiversity conservation. Its sustained growth requires effective legal and administrative support, expanded scientific research, enhanced innovation, coordinated partnerships, and integrated nationwide initiatives.

Natural enemies play an important role in the integrated pest management (IPM) of rice crops. Chemical control is commonly used for pest management in rice; however, the compatibility between biological and chemical control within this system has not been thoroughly investigated. This study aimed to evaluate both the direct and indirect toxicity of nine insecticides-tetraniliprole, triflumezopyrim, chlorantraniliprole, pymetrozine, spinetoram, nitenpyram, imidacloprid, emamectin benzoate, and avermectin-against six important predators of rice pests: Cyrtorhinus lividipennis, Paederus fuscipes, Ummeliata insecticeps, Tetragnatha maxillosa, Mendoza cancestrinnii, and Pardosa pseudoannulata under laboratory conditions. The results indicated that tetraniliprole, triflumezopyrim, and chlorantraniliprole exhibited negligible direct toxicity (mortality < 30%) to all six predators and did not significantly affect their predatory activity. In contrast, spinetoram, avermectin, emamectin benzoate, nitenpyram, and imidacloprid showed high direct toxicity (mortality > 99%), significantly reduced predatory activity, and were classified as high to extremely high risk for C. lividipennis. Nitenpyram showed strong direct toxicity (mortality > 99%) to P. fuscipes and was categorized as high risk. Avermectin and emamectin benzoate exhibited high direct and indirect toxicity to all four spider species, significantly reducing predatory activity, and were graded from medium to extremely high risk. Spinetoram reduced predatory activity across all four spider species and exhibited direct toxic effects, posing a high risk to U. insecticeps. In conclusion, pymetrozine, spinetoram, nitenpyram, imidacloprid, emamectin benzoate, and avermectin exerted lethal or sublethal effects on all six predators. Conversely, tetraniliprole, triflumezopyrim, and chlorantraniliprole were regarded as safer insecticides for all six predators.

Volatile organic substances (VOCs) emitted by plants play an important role in the recognition and selection of host plants by insects. For polyphagous insects with a broad host range, like the brown marmorated stink bug Halyomorpha halys, not much is known about the plant volatiles that influence host choice. In order to determine which odour stimuli could influence host selection, monitoring was carried out using pheromone traps in orchards with various host plants. The headspace of the phenological stages of plants on which H. halys occurred in large numbers was sampled and analysed with gas chromatography coupled with mass spectrometry (GC-MS). The volatile profiles of the different host plants varied significantly. Some compounds occurred in high relative proportions across all taxa. Those compounds were tested by H. halys using electroantennography. H. halys' antennae responded significantly to all of the selected compounds. In a Y-tube olfactometer, H. halys showed a significant attraction to 1 µg hexanal, 100 µg (E)-4,8-dimethyl-1,3,7-nonatriene (DMNT), and a volatile mixture. Due to the limited sustainable strategies for plant protection against this polyphagous insect, adding attractive plant volatiles to lures could improve the effectiveness of alternative volatile-based plant protection strategies, such as traps or capsules, or promote their development.

Artificial diets are increasingly utilized in sericulture, yet they often yield cocoons with properties that differ from those produced by mulberry leaf-fed silkworms. A reliable method to distinguish between these two types of cocoons remains lacking, compromising quality control in silk-related industries. In this study, we report a multidimensional analysis method integrating chemical, structural, and biomarker analyses to distinguish cocoons produced by mulberry leaf-fed (Mul-fed) silkworms from those produced by artificial diet-fed (Art-fed) silkworms. The SEM images showed that after the process of biomarker extraction, Mul-fed cocoons had a more complete morphological structure than Art-fed cocoons, and the sericin layer of Mul-fed cocoons was less damaged. Thermogravimetric and amino acid analyses revealed no significant differences between the two types of cocoons. Biomarker analyses via ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) revealed that quercitrin and quercetin were enriched in Mul-fed cocoons, while daidzein and genistein were enriched in cocoons produced by artificial diet-fed silkworms. Furthermore, materials extracted from Mul-fed cocoons demonstrated significantly superior bioactivity than those from Art-fed cocoons in in vitro assays. This study provides a reliable and accurate method for assessing cocoon quality and distinguishing cocoons from different feeding methods, laying a robust basis for quality evaluation and silk product development.

(1) Background: Bees of the genus Apis play a fundamental role in ecosystems thanks to their pollination activities and their long evolutionary history. This has resulted in species diversifying and spreading across Asia, Africa, and Europe. This review contextualises the genus within biogeographic and evolutionary frameworks, emphasising the importance of understanding the origins, adaptations, distribution and differences between species. (2) Methods: Recent studies on the biology, taxonomy and ecology of Apis species were analysed, including research on social behaviour, communication, genetics, morphology and environmental adaptations, as well as contributions using modern evolutionary and phylogeographic analytical methods. (3) Results: The gathered evidence shows that anthropogenic factors, including climate change, habitat loss, intensive agriculture, pollutants, competition with other bees and the spread of parasites and pathogens, significantly affect the stability of Apis populations and increase the vulnerability of wild species. (4) Conclusions: This review emphasises the importance of integrating ecological, genetic and management knowledge to develop effective conservation strategies that aim to reduce the impact of human activities and preserve the resilience of Apis species and the vital ecosystem services they provide.

Phytoseiid mites, as effective natural enemies, often experience various environmental stresses, especially extreme HTs under global warming and climate change. However, Neoseiulus californicus from the phytoseiid mite family could endure relatively HT (35-45 °C) exposure. To gain insights into its molecular mechanisms underlying heat adaptation, we conducted a comparative analysis of the transcriptomes exposed at 25 and 45 °C. There were 3117 and 7368 differentially expressed genes (DEGs) identified under the 0.5 and 4 h heat treatments, respectively. The functional enrichment analysis illustrated that DEGs were linked to "catalytic activity", "metabolic process", and the "Calcium signaling pathway". Further DEG annotation and analysis illustrated that the expression of proteins encoding heat shock proteins (HSPs) and protein turnover were significantly induced. We also identified the unigene DN1689_c0 encoding the HSP70 gene (NcHSP70), which exhibited the strongest transcriptional response to heat stress. NcHSP70 inhibition by RNAi suppression had a significant impact on the survival of N. californicus. The ATPase effect of the purified recombinant NcHSP70 protein after HT treatment was significantly elevated. These findings increase our comprehension of the complex molecular mechanisms underlying HT adaptation and determine the important role of NcHSP70 in the heat resistance of N. californicus.

Locust outbreaks cause a significant threat to global food security and ecosystem stability, with particularly severe consequences in grassland regions, where grasshoppers also exert considerable ecological pressure. In comparison to grasshoppers, locusts typically occur at much larger spatial scales, as their strong migratory ability and collective movement behavior lead to greater spatial connectivity and autocorrelation. The forecasting of both locust and grasshopper outbreaks remains a formidable scientific challenge, primarily due to the complex, nonlinear spatiotemporal interactions among environmental drivers such as weather, vegetation, and soil conditions. This review compares the evolution of prediction methodologies for locust and grasshopper outbreaks, focusing on the application of deep learning (DL) methods to ecological forecasting tasks. It traces the development from traditional statistical models to classical machine learning, and ultimately to DL, assessing the strengths and limitations of key DL architectures-including Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs)-in modeling the intricate dynamics of locust populations. While most studies have concentrated on locust outbreaks, this review emphasizes the adaptation of these models to grassland ecosystems, such as those in Inner Mongolia, where grasshopper outbreaks exhibit similarities to locust plagues but have been largely overlooked in DL research. Despite the potential of DL, challenges such as data scarcity, limited model generalizability across regions, and the "black box" issue of low interpretability remain. To address these issues, we propose future research directions that integrate Explainable AI (XAI), transfer learning, and generative models like GANs to development more robust, transparent, and ecologically grounded forecasting tools. By promoting the use of efficient architectures like GRUs within customized frameworks, this review aims to guide the development of effective early warning systems for sustainable locust management in vulnerable grassland ecosystems.