A better understanding of the relative importance of different spatial scale determinants on fish communities will eventually increase the accuracy and precision of their bioassessments. Many studies have described the influence of environmental variables on fish communities on multiple spatial scales. However, there is very limited information available on this topic for the East Asian monsoon region, including Korea. In this study, we evaluated the relationship between fish communities and environmental variables at multiple spatial scales using self-organizing map (SOM), random forest, and theoretical path models. The SOM explored differences among fish communities, reflecting environmental gradients, such as a longitudinal gradient from upstream to downstream, and differences in land cover types and water quality. The random forest model for predicting fish community patterns that used all 14 environmental variables was more powerful than a model using any single variable or other combination of environmental variables, and the random forest model was effective at predicting the occurrence of species and evaluating the contribution of environmental variables to that prediction. The theoretical path model described the responses of different species to their environment at multiple spatial scales, showing the importance of altitude, forest, and water quality factors to fish assemblages.

Climate warming threatens biodiversity at global, regional and local levels by causing irreversible changes to species populations and biological communities. The Himalayan region is highly vulnerable to climate warming. This calls for efficient environmental management strategies because biodiversity monitoring is costly, particularly for the developing countries of the Himalaya. Species distribution modeling (SDM) represents a tool that can be used to identify vulnerable areas where biodiversity monitoring and conservation are required most urgently and can be prioritized. Here, we investigated the potential present-day community compositions of river invertebrates in the central and eastern Himalayas and predicted changes in community compositions in future decades using SDMs. We then quantified the climate-induced range shifts of benthic invertebrates along the elevational gradient and tested whether the predicted community shift is fast enough to fully compensate for the projected climate warming. Our model predicts future increases in benthic invertebrate taxonomic richness. Further, projected community shifts are characterized by the movement of warm-dwellers to higher elevations and losses in cold-dwellers. The predicted model shows that benthic invertebrate communities would not be able to compensate climate warming through uphill migration and thus would accumulate climatic debts. Our findings suggest that the ongoing warming effect would cause continued elevational range shifts of mountain river communities.

Central Asia is one of the most challenged places, prone to suffering from various natural hazards, where seismically triggered landslides have caused severe secondary losses. Research on this problem is especially important in the cross-border Mailuu-Suu catchment in Kyrgyzstan, since it is burdened by radioactive legacy sites and frequently affected by earthquakes and landslides. To identify the landslide-prone areas and to quantify the volume of landslide (VOL), Scoops3D was selected to evaluate the slope stability throughout a digital landscape in the Mailuu-Suu catchment. By performing the limit equilibrium analysis, both of landslide susceptibility index (LSI) and VOL were estimated under five earthquake scenarios. The results show that the upstream areas were more seismically vulnerable than the downstream areas. The susceptibility level rose significantly with the increase in earthquake strength, whereas the VOL was significantly higher under the extreme earthquake scenario than under the other four scenarios. After splitting the environmental variables into sub-classes, the spatial variations of LSI and VOL became more clear: the LSI reduced with the increase in elevation, slope, annual precipitation, and distances to faults, roads, and streams, whereas the highest VOL was observed in the areas with moderate elevations, high precipitation, grasslands, and mosaic vegetation. The relative importance analysis indicated that the explanatory power reduced with the increase in earthquake level and it was significant higher for LSI than for VOL. Among nine environmental variables, the distance to faults, annual precipitation, slope, and elevation were identified as important triggers of landslides. By a simultaneous assessment of both LSI and VOL and the identification of important triggers, the proposed modelling approaches can support local decision-makers and householders to identify landslide-prone areas, further design proper landslide hazard and risk management plans and, consequently, contribute to the resolution of transboundary pollution conflicts.