Global change includes a substantial increase in the frequency and intensity of extreme high temperatures (EHTs), which influence insects at almost all levels. The number of studies showing the ecological importance of EHTs has risen in recent years, but the knowledge is rather dispersed in the contemporary literature. In this article, we review the biological and ecological effects of EHTs actually experienced in the field, i.e., when coupled to fluctuating thermal regimes. First, we characterize EHTs in the field. Then, we summarize the impacts of EHTs on insects at various levels and the processes allowing insects to buffer EHTs. Finally, we argue that the mechanisms leading to positive or negative impacts of EHTs on insects can only be resolved from integrative approaches considering natural thermal regimes. Thermal extremes, perhaps more than the gradual increase in mean temperature, drive insect responses to climate change, with crucial impacts on pest management and biodiversity conservation.

Organisms often experience adverse high temperatures during the daytime, but they may also recover or repair themselves during the night-time when temperatures are more moderate. Thermal effects of daily fluctuating temperatures may thus be divided into two opposite processes (i.e. negative effects of daytime heat stress and positive effects of night-time recovery). Despite recent progress on the consequences of increased daily temperature variability, the independent and combined effects of daytime and night-time temperatures on organism performance remain unclear. By independently manipulating daily maximum and minimum temperatures, we tested how changes in daytime heat stress and night-time recovery affect development, survival and heat tolerance of the lady beetle species Propylea japonica. Thermal effects on development and survival differed between daytime and night-time. Daytime high temperatures had negative effects whereas night-time mild temperatures had positive effects. The extent of daytime heat stress and night-time recovery also affected development and critical thermal maximum, which indicates that there were both independent and combined effects of daytime and night-time temperatures on thermal performances. Our findings provide insight into the thermal effect of day-to-night temperature variability and have important implications for predicting the impacts of diel asymmetric warming under climate change.

Here we proposed a grape disease identification model based on improved YOLOXS (GFCD-YOLOXS) to achieve real-time detection of grape diseases in field conditions. We build a dataset of 11,056 grape disease images in 15 categories, based on 2566 original grape disease images provided by the State Key Laboratory of Plant Pest Biology data center after pre-processing. To improve the YOLOXS algorithm, first, the FOCUS module was added to the backbone network to reduce the lack of information related to grape diseases in the convolution process so that the different depth features in the backbone network are fused. Then, the CBAM (Convolutional Block Attention Module) was introduced at the prediction end to make the model focus on the key features of grape diseases and mitigate the influence of the natural environment. Finally, the double residual edge was introduced at the prediction end to prevent degradation in the deep network and to make full use of the non-key features. Compared with the experimental results of relevant authoritative literature, GFCD-YOLOXS had the highest identification accuracy of 99.10%, indicating the superiority of the algorithm in this paper.