High‐mountain areas such as the Tibeto‐Himalayan region (THR) host cold‐adapted biota expected to be sensitive to anthropogenic climate change. Meconopsis is a representative endangered genus confined to alpine meadow or subnival habitats in the THR. We used climate‐niche factor analysis to study the vulnerability of ten Meconopsis species to climate change, comparing current climate (representative of 1960–1990) to future climate scenarios (2070: average 2061–2080). For these ten Meconopsis species, we then identified potential future climate refugia and determined optimal routes for each species to disperse to the proposed refugia. Our results indicate that for the ten Meconopsis species, the regions with low vulnerability to climate change in the THR are the central Qinghai‐Tibet Plateau, the Hengduan Mountains (HDM), the eastern Himalayas, and the West Qinling Mountain (WQL), and can be considered potential future climate refugia. Under future climate change, we found for the ten Meconopsis species potential dispersal routes to three of the four identified refugia: the HDM, the eastern Himalayas, and the WQL. Our results suggest that past refugia on the THR will also be the future climate refugia for the ten Meconopsis species, and these species may potentially persist in multiple future climate refugia, likely reducing risks from climate change. Furthermore, climate change may affect the threat ranking of Red Listed Species for Meconopsis species, as Least Concern species were estimated to become more vulnerable to climate change than the only Near Threatened species.

Climate change increases the extinction risk of species, and studying the impact of climate change on endangered species is of great significance to biodiversity conservation. In this study, the endangered plant Meconopsis punicea Maxim (M. punicea) was selected as the research object. Four species distribution models (SDMs): the generalized linear model, the generalized boosted regression tree model, random forest and flexible discriminant analysis were applied to predict the potential distribution of M. punicea under current and future climates scenarios. Among them, two emission scenarios of sharing socio-economic pathways (SSPs; i.e., SSP2-4.5 and SSP5-8.5) and two global circulation models (GCMs) were considered for future climate conditions. Our results showed that temperature seasonality, mean temperature of coldest quarter, precipitation seasonality and precipitation of warmest quarter were the most important factors shaping the potential distribution of M. punicea. The prediction of the four SDMs consistently indicated that the current potential distribution area of M. punicea is concentrated between 29.02° N–39.06° N and 91.40° E–105.89° E. Under future climate change, the potential distribution of M. punicea will expand from the southeast to the northwest, and the expansion area under SSP5-8.5 would be wider than that under SSP2-4.5. In addition, there were significant differences in the potential distribution of M. punicea predicted by different SDMs, with slight differences caused by GCMs and emission scenarios. Our study suggests using agreement results from different SDMs as the basis for developing conservation strategies to improve reliability.

The effects of historical geology and climatic events on the evolution of plants around the Qinghai-Tibetan Plateau region have been at the center of debate for years. To identify the influence of the uplift of the Tianshan Mountains and/or climatic oscillations on the evolution of plants in arid northwest China, we investigated the phylogeography of the Euphrates poplar (Populus euphratica) using chloroplast DNA (cpDNA) sequences and nuclear microsatellites, and estimated its historical distribution using Ecological Niche Modeling (ENM). We found that the Euphrates poplar differed from another desert poplar, P. pruinosa, in both nuclear and chloroplast DNA. The low clonal diversity in both populations reflected the low regeneration rate by seed/seedlings in many locations. Both cpDNA and nuclear markers demonstrated a clear divergence between the Euphrates poplar populations from northern and southern Xinjiang regions. The divergence time was estimated to be early Pleistocene based on cpDNA, and late Pleistocene using an Approximate Bayesian Computation analysis based on microsatellites. Estimated gene flow was low between these two regions, and the limited gene flow occurred mainly via dispersal from eastern regions. ENM analysis supported a wider distribution of the Euphrates poplar at 3 Ma, but a more constricted distribution during both the glacial period and the interglacial period. These results indicate that the deformation of the Tianshan Mountains has impeded gene flow of the Euphrates poplar populations from northern and southern Xinjiang, and the distribution constriction due to climatic oscillations further accelerated the divergence of populations from these regions. To protect the desert poplars, more effort is needed to encourage seed germination and seedling establishment, and to conserve endemic gene resources in the northern Xinjiang region.

How to effectively obtain species‐related low‐dimensional data from massive environmental variables has become an urgent problem for species distribution models (SDMs). In this study, we will explore whether dimensionality reduction on environmental variables can improve the predictive performance of SDMs. We first used two linear (i.e., principal component analysis (PCA) and independent components analysis) and two nonlinear (i.e., kernel principal component analysis (KPCA) and uniform manifold approximation and projection) dimensionality reduction techniques (DRTs) to reduce the dimensionality of high‐dimensional environmental data. Then, we established five SDMs based on the environmental variables of dimensionality reduction for 23 real plant species and nine virtual species, and compared the predictive performance of those with the SDMs based on the selected environmental variables through Pearson's correlation coefficient (PCC). In addition, we studied the effects of DRTs, model complexity, and sample size on the predictive performance of SDMs. The predictive performance of SDMs under DRTs other than KPCA is better than using PCC. And the predictive performance of SDMs using linear DRTs is better than using nonlinear DRTs. In addition, using DRTs to deal with environmental variables has no less impact on the predictive performance of SDMs than model complexity and sample size. When the model complexity is at the complex level, PCA can improve the predictive performance of SDMs the most by 2.55% compared with PCC. At the middle level of sample size, the PCA improved the predictive performance of SDMs by 2.68% compared with the PCC. Our study demonstrates that DRTs have a significant effect on the predictive performance of SDMs. Specifically, linear DRTs, especially PCA, are more effective at improving model predictive performance under relatively complex model complexity or large sample sizes.

The threat posed by poisonous weeds to grassland ecosystems may be exacerbated by climate change mainly driven by carbon dioxide (CO₂) emissions. Achnatherum inebrians is a common and poisonous grassland weed that is seriously endangering the sustainable development of prairie animal husbandry in Western China. Understanding the influence of future climate change under different CO₂ emission scenarios on the potential distributions of A. inebrians is critical for planning agricultural strategies to manage the continued invasion. An ecological niche model (ENM) was developed using Maxent to predict the potential distribution of A. inebrians under three different CO₂ emission scenarios. Occurrence records of A. inebrians were selected utilizing the nearest neighbor method. Six environmental variables, which were identified through principal component analysis, correlation analysis and their contribution rates, were used to perform the ENM. At the same time, considering the uncertainties of predicting future climates, four global circulation models were used for the Maxent projections with average results calculated. Our results demonstrate differential influences of various CO₂ emission scenarios on the potential distributions of A. inebrians. Before 2050, high CO₂ emission scenarios resulted in a wider potential distribution of A. inebrians, when compared to low CO₂ emission scenarios. However, after 2050, the low CO₂ emission scenarios were more conducive to an expanded potential distribution. In addition, after 2050, high CO₂ emission scenarios maintain the geographical distribution centroids of A. inebrians in lower latitudes, while low CO₂ emission scenarios result in distribution centroids rising to higher latitudes. Further, low CO₂ emission scenarios resulted in the average potential distribution elevation dropping lower than in high CO₂ emission scenarios.