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Here, we present data collected from the Qinghai–Tibet Plateau that describes the variation of leaf functional traits across 32 plant species and could be used to investigate plant community functioning and predict the impact of climate change on biogeochemical cycles. The sampling area is located in Huangshui River Valley, in the southeast of Qinghai Province, China (36° 19′ to 36° 53′ N, 100° 59′ to 102° 48′ E). The area contains an alpine meadow typical of the Qinghai–Tibet Plateau.This dataset includes field survey data on the functional properties of compound leaves from herbaceous species in the Huangshui River Basin of Qinghai Province, China, at altitudes from 1800 m to 4000 m in the summer of 2021. Data were collected from 326 plots, including 646 data points of compound leaf plants, spanning 32 compound leaf plant species belonging to 14 genera and four families. The study species were chosen from 47 families, 165 genera and 336 species present in the plots and all compound leaf plants were chosen within each plot. We picked the parts containing leaves, petioles and rachis from the study plants and separated the leaves from the plants. The cut compound leaf part was a leaflet, while the petiole and rachis were linear elements. The dataset includes information about the leaflet trait variation (i.e. leaflet area, leaflet dry mass, specific leaflet area and leaflet nitrogen content per unit dry mass) and linear elements' biomass and nitrogen content per unit dry mass (i.e. both petiole and rachis) of 646 compound leaves. This dataset can be used to analyse the evolution of leaf traits and the basic functioning of ecosystems. Moreover, the dataset provides an important basis for studying the species distribution and protection of biodiversity of the Qinghai–Tibet Plateau and evaluating ecosystem services. These data also support the high-quality development of the Yellow River Basin and have empirical and practical value for alpine biodiversity protection and ecosystem management.

Insect pests pose a significant threat to alpine ecosystems, especially under rapid environmental change conditions. Therefore, it is necessary to explore the effects of environmental factors on insect pest risks and provide methods for pest management in alpine regions. Habitat heterogeneity and topographic variation are the indicators of insect pest risks. However, few studies have explored the effects of habitat heterogeneity and topographic variation on insect pest risks in alpine regions. We used species distribution modeling (i.e., maxent modeling) to project the distributions of insect pests in this alpine region based on occurrence records. Then, we delineated the high-risk areas for insect pests based on the species distributions under a conceptual risk framework using Zonation software for different ecoregional types. We determined the alpine conifer and mixed forests of the Nujiang Langcang Gorge, the conifer forests of the Qilian Mountains, and the shrublands and meadows of Southeast Tibet as the key areas requiring monitoring for insect pests in Qinghai province based on the scoring of insect pest risk rank with >0.7. Habitat heterogeneity and topographic variation could be developed as indicators of risk exposure to insect pests in alpine regions. Our study suggests that the prevention and control of insect pests should be conducted in areas with high habitat heterogeneity and topographic roughness in alpine regions. We provided new insights into the application of species distribution modeling based on habitat heterogeneity and topographic variation. The results of our study indicate that habitat heterogeneity and topographic variation should be considered for improving pest management effectiveness in alpine regions.