Tomicus yunnanensis Kirkendall (Coleoptera: Scolytinae) is a stem-boring pest that endangers Pinus yunnanensis Franch (Pinales:Pinoideae), which seriously affects the ecological environment safety in southwest China. In order to understand the potential distribution pattern and change in the potential distribution of P. yunnanensis and T. yunnanensis, this study used the maximum entropy model to predict the distribution of potentially suitable areas for P. yunnanensis and T. yunnanensis and explored the relationships between their different spatiotemporal distributions based on change analysis. The experimental results show that altitude is the main factor restricting the current distribution of P. yunnanensis. The current suitable areas of P. yunnanensis are mainly distributed in Yunnan, Sichuan and Guizhou. The minimum temperature of the coldest month is the main factor affecting the current distribution of T. yunnanensis. The current suitable areas of T. yunnanensis are mainly distributed in Yunnan, Sichuan and Tibet. Under future climate scenarios, the total suitable areas of P. yunnanensis and T. yunnanensis are expected to increase. The suitable areas tend to move to higher altitudes in the west and higher latitudes in the north. At the same time, this study finds that there is an obvious bottleneck of expansion to northeastern Sichuan near the Daba Mountains. The results of intersection analysis showed that, with future climate change, P. yunnanensis and T. yunnanensis mainly showed lowly suitable (or unsuitable)—lowly suitable (or unsuitable) to moderately (or highly) suitable—and moderate (or high) variation patterns of suitable areas under the SSP1-2.6 climate scenario. These results will provide an important basis for the breeding of P. yunnanensis and controlling T. yunnanensis.

Pine nuts are not only the important agent of pine reproduction and afforestation, but also the commonly consumed nut with high nutritive values. However, it is difficult to distinguish among pine nuts due to the morphological similarity among species. Therefore, it is important to improve the quality of pine nuts and solve the adulteration problem quickly and non-destructively. In this study, seven pine nuts (Pinus bungeana, Pinus yunnanensis, Pinus thunbergii, Pinus armandii, Pinus massoniana, Pinus elliottii and Pinus taiwanensis) were used as study species. 210 near-infrared (NIR) spectra were collected from the seven species of pine nuts, five machine learning methods (Decision Tree (DT), Random Forest (RF), Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Naive Bayes (NB)) were used to identify species of pine nuts. 303 images were used to collect morphological data to construct a classification model based on five convolutional neural network (CNN) models (VGG16, VGG19, Xception, InceptionV3 and ResNet50). The experimental results of NIR spectroscopy show the best classification model is MLP and the accuracy is closed to 0.99. Another experimental result of images shows the best classification model is InceptionV3 and the accuracy is closed to 0.964. Four important range of wavebands, 951–957 nm, 1,147–1,154 nm, 1,907–1,927 nm, 2,227–2,254 nm, were found to be highly related to the classification of pine nuts. This study shows that machine learning is effective for the classification of pine nuts, providing solutions and scientific methods for rapid, non-destructive and accurate classification of different species of pine nuts.

Macroevolution of most organisms is generally the result of synergistic action of multiple key genes in evolutionary biology. Unfortunately, the weights of these key genes in macroevolution are difficult to assess. In this study, we designed various word embedding libraries of natural language processing (NLP) considering the multiple mechanisms of evolutionary genomics. A novel method (IKGM) based on three types of attention mechanisms (domain attention, kmer attention and fused attention) were proposed to calculate the weights of different genes in macroevolution. Taking 34 species of diurnal butterflies and nocturnal moths in Lepidoptera as an example, we identified a few of key genes with high weights, which annotated to the functions of circadian rhythms, sensory organs, as well as behavioral habits etc. This study not only provides a novel method to identify the key genes of macroevolution at the genomic level, but also helps us to understand the microevolution mechanisms of diurnal butterflies and nocturnal moths in Lepidoptera.

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