Climate change is causing unprecedented alterations in the spatial pattern of global biodiversity, imposing severe challenges for biodiversity conservation. In particular, alpine biomes are sensitive to a variety of environmental changes. Therefore, understanding the distribution and conservation of alpine plant biodiversity is vital. In this study, we used species distribution modeling and 20,650 high-resolution occurrence coordinates of 1224 plant species to evaluate the potential distribution of plants in the northeastern Qinghai–Tibet Plateau (Qinghai Province, China) under different future climate scenarios, through an integrative analysis of species distribution probabilities, species richness, and priority conservation areas. Under current and future climate scenarios, the plant species are predicted to be mainly distributed in eastern and southern Qinghai Province, with the suitable conditions for plant species gradually extending from the southeast to the northwest of Qinghai Province under the effects of climate change. The priority conservation areas in Qinghai national nature reserves are predicted to expand, with this expansion being greater for herbaceous plants than woody plants, under future climate scenarios. However, the priority conservation areas outside nature reserves in Qinghai Province remain approximately three times larger than those inside nature reserves. Thus, there were great differences between the existing nature reserve area and the priority conservation areas, with nature reserves insufficiently covering priority conservation areas in Qinghai Province. Therefore, the original nature reserve areas should be expanded, according to the predicted plant habitat hotspots in Qinghai Province. Our research provides valuable information for biodiversity protection in the northeastern Qinghai–Tibet Plateau, reasonable strategies for addressing the future protection challenges associated with climate pressure, and new insights for improving nature reserves in the Qinghai–Tibet Plateau.

Invasive tree species threaten ecosystems, natural resources, and managed land worldwide. Land cover has been widely used as an environmental variable for predicting global invasive tree species distributions. Recent studies have shown that consensus land cover data can be an effective tool for species distribution modelling. In this paper, consensus land cover data were used as prediction variables to predict the distribution of the 11 most aggressive invasive tree species globally. We found that consensus land cover data could indeed contribute to modelling the distribution of invasive tree species. According to the contribution rate of land cover to the distribution of invasive tree species, we inferred that the cover classes of open water and evergreen broadleaf trees have strong explanatory power regarding the distribution of invasive tree species. Under consensus land cover changes, invasive tree species were mainly distributed near equatorial, tropical, and subtropical areas. In order to limit the damage caused by invasive tree species to global biodiversity, human life, safety, and the economy, strong measures must be implemented to prevent the further expansion of invasive tree species. We suggest the use of consensus land cover data to model global invasive tree species distributions, as this approach has strong potential to enhance the performance of species distribution modelling. Our study provides new insights into the risk assessment and management of invasive tree species globally.

Species distribution models (SDMs) based on fine-scale environmental data may reduce the uncertainty in predicting species distributions. However, many scientists have also projected the robust potential distributions of species using environmental data of different scales and found that the potential distributions modeled using SDMs are scale dependent. This may be due to the impact of the scale effect on species richness (as well as on multi-species distributions). To eliminate the impact of the scale effect, we aim to develop an improved method to integrate different scales into species distribution models. We use protected areas as the study regions and propose the hypothesis that there is a spatial element to the threat of invasive species for protected areas under climate change. We use Maxent to compute the current and future invasion ability and invasion inequality of invasive species for protected areas based on the potential distributions of species across different scales to evaluate the risk of invasive species. We find that an increase in the number of present records could reduce the accuracy of SDMs. There is a significant linear relationship between the fine-scale and coarse-scale risk of invasive species of alien plants in protected areas, and an appropriate scale should thus be selected to assess species risk based on this linear relationship of invasive risk. There is a significant relationship between the potential of IAPS to invade protected areas and the invasion inequality of IAPS in protected areas across all scales, and 5.0 arcminutes is the most appreciate scale to evaluate the risk of IAPS for protected areas under climate change based on principal component analysis. We provide new insights into the use of species distribution models coupled with different spatial scales to analyze the regional risks associated with species and to assess regional biodiversity.

The leaf nitrogen (N) and phosphorus (P) concentrations of terrestrial plants make large contributions to ecosystem function and dynamics. The relationship between aridity and leaf N and P has been established through experimental studies. However, few studies have focused on the large-scale effects of aridity on the leaf N and P of terrestrial plants. In this paper, we used linear regression models to test the effects of aridity on terrestrial plant leaf N and P and the N:P ratio based on global datasets. We found that aridity had significant effects on the leaf N and P and the N:P ratio of terrestrial plants. The strongest relationships were between fern leaf P, the fern N:P ratio, tree leaf P, the tree N:P ratio, vine leaf N, and the tree N:P ratio. Aridity could be used to predict the P and N:P ratio of terrestrial plants, particularly those of ferns and trees, on large scales in arid environments. Our study contributes to maintaining ecosystem functioning and services in arid environments under climate change.