Insects

Insect insulin signaling plays a central role in regulating development, metamorphosis, and reproduction, yet its mechanistic functions in the tomato leafminer, Tuta absoluta, a globally significant pest, remain poorly understood. This study aimed to elucidate the role of the serine/threonine kinase Akt (TaAkt) in coordinating metamorphosis and female reproductive processes. The TaAkt gene was cloned and characterized, and its spatiotemporal expression was analyzed across various developmental stages and tissues. RNA interference (RNAi) was employed to knock down TaAkt in late pupae and newly emerged females, followed by assessment of pupal-adult eclosion, chitin metabolism, 20-hydroxyecdysone (20E) titer, ovarian development, juvenile hormone (JH) levels, vitellogenin synthesis, and fecundity. Knockdown of TaAkt significantly reduced 20E titers and downregulated the expression of ecdysone biosynthesis and signaling genes, leading to pupal mortality, defective molting, and reduced chitin content. In adult females, TaAkt silencing impaired ovarian growth, decreased JH levels, suppressed vitellogenin production, and reduced egg number and hatching rates. These findings demonstrate that TaAkt exerts pleiotropic control over both metamorphic and reproductive processes in T. absoluta. The study identifies TaAkt as a promising molecular target for RNAi-based pest management strategies, offering a potential approach to simultaneously suppress survival and reproductive capacity in this economically important pest.

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.

This study aimed to evaluate the effectiveness of a sustainable pest management program for controlling the coffee berry borer (CBB), Hypothenemus hampei, at La Catalina coffee farm (Pereira, Risaralda, Colombia) and compare it with the historical conventional control approach (2012–2022), a period during which the management of CBB was based primarily on the application of synthetic chemical insecticides. The working hypothesis was that integrating biological control agents (Phymastichus coffea, Prorops nasuta, and Beauveria bassiana) with cultural and monitoring practices would significantly reduce infestation levels and insecticide dependence while maintaining or improving economic profitability. From 2023 to 2024, GIS-based hotspot mapping, targeted parasitoid release, and fungal application triggered when infestation thresholds were reached were incorporated into sustainable pest management. Infestation, flight activity, and parasitism rates were monitored, and climatic variables were analyzed to determine their relationships with pest dynamics. The results showed that a sustainable pest management program reduced field infestation from a historical average of 3.3 ± 0.15% to 1.7 ± 0.2%, remaining below the 2% action threshold (F-test, p < 0.05). Prorops nasuta reduced the number of CBB life stages by 32.1%, falling from 10.9 ± 0.3 individuals per berry in non-parasitized fruits to 7.0 ± 0.7 in parasitized berries, while parasitism by P. coffea peaked at 70%. CBB flight activity decreased markedly compared to historical averages. The shift from a chemical approach to sustainable pest management resulted in a 26% increase in net income per hectare and a Marginal Rate of Return (MRR) of 18.06. Overall, the results confirm that a sustainable pest management program effectively suppresses CBB populations, minimizes pesticide use, and enhances the economic and environmental sustainability of coffee production systems.