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.

Species distribution modeling (SDM) has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of these variables may affect the performance of SDM. This investigation elucidates a new bioclimate variable dataset (i.e., CMCC-BioClimInd) for its use in SDM. The predictive performance of SDM that includes WorldClim and CMCC-BioClimInd was evaluated by AUC and omission rate and the explanatory power of both datasets was assessed by the jackknife method. Furthermore, the ODMAP protocol was used to record CMCC-BioClimInd to ensure reproducibility. The results indicated that CMCC-BioClimInd effectively simulates invasive plant species’ distribution. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, it was inferred that the modified and simplified continentality and Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical, and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. This method has great potential to improve the efficiency of species distribution modeling, thereby providing a new perspective for risk assessment and management of global invasive plant species.

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.

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.

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.

Alien woody plants (AWPs) increasingly threaten biodiversity in China’s national nature reserves, with climate change expected to intensify these risks. We used species distribution modeling (MaxEnt) and spatial prioritization (Zonation) to assess invasion risk for 251 AWP species across 479 national nature reserves under current and future climate scenarios (SSP245 and SSP585). Spatial prioritization revealed current hotspots in southern tropical–subtropical national nature reserves (e.g., Hainan, Fujian, Yunnan provinces), with significant northward and westward expansion projected under warming. A total of 71 species—such as Quercus robur, Salix alba, and Robinia pseudoacacia—pose consistently high risks, while some others (e.g., Ficus benghalensis) may become emerging threats under future conditions. These range shifts are driven by thermal constraint relaxation and longer growing seasons. To mitigate future impacts, we recommend region-specific strategies: containment and seed-source control in southern national nature reserves, and early detection and monitoring in northern and western regions. Our findings provide a spatially explicit framework for climate-informed invasive species management in protected areas.