Insect populations in temperate climates can show seasonal differences in demographic rates. Extreme high‐temperature events (EHTs) are increasing in frequency across all seasons. These may, in turn, disrupt insects' season‐specific demographic strategies. However, whether insect populations respond to EHTs in a season‐specific fashion is not known, but may be critical for forecasting their responses to climate change. We conducted a series of common garden experiments measuring the inherent vital rates of spring, summer, and autumn cohorts of a global nonmigratory pest, the oriental fruit moth (Grapholita molesta), under constant mild temperatures, EHT exposures at different life stages, and short‐term heat hardening followed by EHT exposure at different life stages. We simultaneously conducted a 4‐year survey of G. molesta in orchards to link our experimental results to observed population dynamics in the field. We encountered intrinsic variation in vital rates and reproductive traits among seasonal cohorts, such that summer cohorts had higher intrinsic population growth rates but smaller eggs than spring and autumn cohorts. Moreover, these responses varied among the life stages to which the EHT was applied. Pre‐acclimation via heat hardening did not strongly affect these results but did slightly increase growth rates when applied to late‐stage larvae. These results were qualitatively recovered in natural population time series, with moderate EHTs enhancing population growth in the spring and reducing it in autumn. Our results underscore the importance of climatic seasonality in shaping season‐specific thermal responses to EHTs among ectotherms. Season‐specific EHT responses can potentially enhance growth of weak spring cohorts and depress the growth of autumn cohorts failing in dormancy, thereby contributing to insects' annual population persistence. Our study helps clarify how a deeper understanding of season and life‐stage‐specific heat responses can enable a more informed forecasting and management of multivoltine insect populations under climate change. Read the free Plain Language Summary for this article on the Journal blog.

Extreme heat events are expected to increase under global climate change, which may depress the abundance of most insect species, including agricultural pests. However, here our experimental observations show that the behavioural plasticity within fine-scale microhabitats, which is often neglected in pest damage and crop loss predictions, can buffer the exposure to simulated extreme high temperatures in a small pest aphid species. As expected, this buffering effect promotes aphid population growth in our lab experiments. Our model predictions further suggest that this buffering effect can aggravate pest damage and wheat yield losses globally during 1977-2017 (8.7 million tons/year, accounting for ~1.2% of global production), assuming a moderate initial pest density if no pesticides are applied. This estimated yield-loss aggravation may even worsen faster ( + 2.6% per year) than the reported increase of global wheat production ( + 2.0% per year) under warming. More than 4/5 countries may undergo increasingly aggravated losses, including most of the countries with the largest wheat production, particularly serious for less developed countries. Global food security will be greatly challenged, considering many other pest insects may also cause aggravated crop loss via buffering climate impacts through behavioural plasticity.

Global trade facilitates the introduction of invasive species that can cause irreversible damage to agriculture and the environment, as well as stored food products. The raisin moth (Cadra figulilella) is an invasive pest that poses a significant threat to fruits and dried foods. Climate change may exacerbate this threat by expanding moth’s distribution to new areas. In this study, we used CLIMEX and MaxEnt niche modeling tools to assess the potential global distribution of the raisin moth under current and future climate change scenarios. Our models projected that the area of suitable distribution for the raisin moth could increase by up to 36.37% by the end of this century under high emission scenario. We also found that excessive precipitation decreased the probability of raisin moth establishment and that the optimum temperature range for the species during the wettest quarter of the year was 0–18 °C. These findings highlight the need for future research to utilize a combined modeling approach to predict the distribution of the raisin moth under current and future climate conditions more accurately. Our results could be used for environmental risk assessments, as well as to inform international trade decisions and negotiations on phytosanitary measures with regards to this invasive species.

Grape leafhoppers (Erythroneura spp.) are major pests in grape cultivation, leading to significant economic losses. Accurate and efficient damage level assessment is crucial for effective pest management and reducing financial impact. In this study, we categorized damage into uninfested leaves and five damage levels (I–V) and constructed a grape leafhopper damage dataset. Based on this dataset, we developed a lightweight detection model for grape leafhopper damage levels, incorporating improvements to the YOLOv8n architecture. The model employs FasterNet as the backbone network to enhance computational efficiency and reduce model complexity. It substitutes for the nearest-neighbor upsampling with CARAFE to improve small target detection capabilities. Additionally, the SE attention mechanism is integrated to optimize leaf feature extraction, thereby enhancing recognition accuracy in complex vineyard environments. The experimental results demonstrate that CSF-YOLO achieves a mAP of 90.15%, which is 1.82% higher than the baseline model, YOLOv8n. Additionally, the model’s inference results can be accessed via mobile devices, demonstrating the feasibility of real-time vineyard pest monitoring. This study provides a solid technical foundation for advancing intelligent pest monitoring systems in vineyards and the development of smart agriculture.