Inferential models are usually used to evaluate the effect of winter warming on range expansion of insects. Generally, correlative approaches used to predict changes in the distributions of organisms are based on the assumption that climatic boundaries are fixed. Spodoptera exigua Hübner (Lepidoptera: Noctuidae) overwinters as larvae or pupae in China regions. To understand the climate change impacts on overwintering of this species in regions of China, CLIMEX and Arc-GIS models were used to predict possible changes of distribution based on temperature. The climate change projection clearly indicated that the northern boundary of overwintering for S. exigua will shift northward from current distribution. Thus, the ongoing winter warming is likely to increase the frequency of S. exigua outbreaks.

In this paper, we use the permutation entropy algorithm to derive the static and dynamic permutation entropy of commodity futures, and to evaluate the effectiveness of main products in China’s commodity futures market. The intraday data of six varieties belonging to six categories in China’s commodity futures market are taken as samples. We find the following: (1) The return distribution of the main varieties shows high peaks, fat tails and asymmetry, and follows the biased random walk distribution characteristics; (2) The permutation entropy of all varieties decreases significantly in the same time window, during which the price volatility of major commodity markets rises. And the time window coincides with the impact time of COVID-19 epidemic; (3) By comparing the distribution of permutation entropy of main varieties in different stages of event shock, we found that the mean value of permutation entropy decreases significantly during the process of event shock, and the price fluctuates greatly. Therefore, the significant decrease of permutation entropy is a valuable warning signal for regulators and investors.