The bio-geographical composition and spatial distribution patterns of dytiscid assemblages in Mongolia are relatively unexplored. In this study, we compiled a list of 99 dytiscid species belonging to 20 genera and five subfamilies recorded in Mongolia and investigated species richness, spatial distribution and bio-geographical composition of the Mongolian dytiscid fauna. This study encompasses the information of currently recorded species and their geographic localities in Mongolia based on our own data and literature sources. We examined how dytiscid species richness was related to sub-basins of surface water network, as well as to geographical elevations within Mongolia. The majority of the Mongolian dytiscid fauna was associated with the sub-basins belonging to Arctic Ocean (80 species, 80.8%) and Central Asian Inland (60 species, 60.6%) basins. Only a few species of dytiscids belonged to the remaining river basins. Species richness of dytiscids and total area of sub-basins were not correlated, but species composition of dytiscids differed significantly among the sub-basins. We observed that most of the species (77 species or 77.8% of total fauna) were recorded in a wide range of elevations and mid-altitudes (1000–2000 m a.s.l.) and showed the greatest diversity of dytiscids. Regarding the bio-geographical composition, species with wide geographical distributions (27.3% of dytiscids), were Palearctic species, while species of Arctic origin (21.2%) together with Boreal elements (16.2%) comprised a large proportion of the dytiscid fauna in Mongolia.

While unmanned aerial vehicle (UAV) remote sensing technology has been successfully used in crop vegetation pest monitoring, a new approach to forest pest monitoring that can be replicated still needs to be explored. The aim of this study was to develop a model for identifying the degree of damage to forest trees caused by Erannis jacobsoni Djak. (EJD). By calculating UAV multispectral vegetation indices (VIs) and texture features (TF), the features sensitive to the degree of tree damage were extracted using the successive projections algorithm (SPA) and analysis of variance (ANOVA), and a one-dimensional convolutional neural network (1D-CNN), random forest (RF), and support vector machine (SVM) were used to construct damage degree recognition models. The overall accuracy (OA), Kappa, Macro-Recall (Rmacro), and Macro-F1 score (F1macro) of all models exceeded 0.8, and the best results were obtained for the 1D-CNN based on the vegetation index sensitive feature set (OA: 0.8950, Kappa: 0.8666, Rmacro: 0.8859, F1macro: 0.8839), while the SVM results based on both vegetation indices and texture features exhibited the poorest performance (OA: 0.8450, Kappa: 0.8082, Rmacro: 0.8415, F1macro: 0.8335). The results for the stand damage level identified by the models were generally consistent with the field survey results, but the results of SVMVIs+TF were poor. Overall, the 1D-CNN showed the best recognition performance, followed by the RF and SVM. Therefore, the results of this study can serve as an important and practical reference for the accurate and efficient identification of the damage level of forest trees attacked by EJD and for the scientific management of forest pests.

Detection of forest pest outbreaks can help in controlling outbreaks and provide accurate information for forest management decision-making. Although some needle injuries occur at the beginning of the attack, the appearance of the trees does not change significantly from the condition before the attack. These subtle changes cannot be observed with the naked eye, but usually manifest as small changes in leaf reflectance. Therefore, hyperspectral remote sensing can be used to detect the different stages of pest infection as it offers high-resolution reflectance. Accordingly, this study investigated the response of a larch forest to Jas’s Larch Inchworm (Erannis jacobsoni Djak) and performed the different infection stages detection and identification using ground hyperspectral data and data on the forest biochemical components (chlorophyll content, fresh weight moisture content and dry weight moisture content). A total of 80 sample trees were selected from the test area, covering the following three stages: before attack, early-stage infection and middle- to late-stage infection. Combined with the Findpeaks-SPA function, the response relationship between biochemical components and spectral continuous wavelet coefficients was analyzed. The support vector machine classification algorithm was used for detection infection. The results showed that there was no significant difference in the biochemical composition between healthy and early-stage samples, but the spectral continuous wavelet coefficients could reflect these subtle changes with varying degrees of sensitivity. The continuous wavelet coefficients corresponding to these stresses may have high potential for infection detection. Meanwhile, the highest overall accuracy of the model based on chlorophyll content, fresh weight moisture content and dry weight moisture content were 90.48%, 85.71% and 90.48% respectively, and the Kappa coefficients were 0.85, 0.79 and 0.86 respectively.

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Risk Analysis and Crisis Response under the Background of the Belt and Road (RAC-18