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Ephedra przewalskii is a key species in arid regions recognized for its remarkable ecological, economic, and medicinal importance. However, climate change and anthropogenic activities have severely threatened their survival, reduced genetic diversity, and increased the risk of extinction. Nonclimatic variables lack systematic integration in current modeling frameworks, and intraspecific genetic variation is similarly poorly explored. This study assessed the vulnerability and adaptive capacity of E. przewalskii populations by applying ensemble species distribution models (ESDMs) and Climate‐Niche Factor Analysis (CNFA), incorporating climate scenarios and human activity data. Additionally, we calculated the haplotype genetic vulnerability and constructed a multidimensional scoring system that combined habitat and genetic vulnerabilities. High vulnerability was observed in regions such as Nilka County and Hotan, whereas regions such as the Ejina Banner‐Hami‐Heiying Mountain‐Jiuquan range area exhibited reduced vulnerability for E. przewalskii. The habitat vulnerability decreased over time under Shared Socioeconomic Pathways 1‐2.6 (SSP1‐2.6) but significantly increased by the 2090s under Shared Socioeconomic Pathways 5‐8.5 (SSP5‐8.5). Haplotypes G, E, and A were identified as being at high risk for genetic diversity loss under SSP5‐8.5. Application of the multidimensional scoring system successfully identified and prioritized key conservation hotspots—including populations in Wuheshalu, Qiemo, and Ruoqian (Tarim Basin) as well as Karamay, Wuchang, and Burqin (Junggar Basin)—based on their high haplotype diversity and vulnerability. The approach thereby offers an adaptable framework for vulnerability assessment and supporting broader conservation efforts. This study assesses the vulnerability and adaptive capacity of Ephedra przewalskii populations using ensemble species distribution models and Climate‐Niche Factor Analysis, integrating climate and human activity data. It reveals that habitat fragmentation and shrinkage are exacerbated by climate change and anthropogenic activities, with varying genetic vulnerability among haplotype populations. A newly constructed multidimensional scoring system helps prioritize conservation efforts, offering crucial insights for the species' conservation and a general framework for assessing other species' vulnerability.

The response of vegetation to climate extremes, including droughts and hot extremes, has been evaluated extensively in recent decades. However, quantitative assessments of individual and combined impacts of dry and hot conditions on vegetation are rather limited. In this study, we developed a multivariate approach for analyzing vegetation responses to dry, hot, and compound dry-hot conditions from a probabilistic perspective using precipitation, temperature, and the Normalized Difference Vegetation Index (NDVI) for the period from 1982 to 2015. The Standardized Precipitation Index (SPI) and Standardized Temperature Index (STI) were used to define individual and compound dry and hot conditions. Based on the diagnosis of the correlation between SPI/STI and NDVI during growing seasons, we investigated the conditional probability of vegetation decline under different climate conditions. The results showed that vegetation was affected by compound dry and hot conditions (defined as SPI ⩽ −1.3 and STI > 1.3) in arid and semi-arid regions. In these regions, the conditional probabilities of vegetation decline under compound dry and hot conditions increased by 7% and 28% compared with those under individual dry and hot conditions, respectively. The impact of compound dry and hot events on vegetation for different biomes was also assessed. Temperate grassland was found to be particularly vulnerable to compound dry and hot conditions. This study highlights the necessity of considering compound dry and hot extremes when assessing vegetation responses to climate extremes under global warming.

Digital agriculture involving different tools and management practices has advanced considerably in recent years, intending to overcome climate risk and reduce food insecurity. Climate change and its impacts on agricultural production and food security are significant sources of public concern worldwide. The objective of this study was to provide an overview of the potential impact of digital agriculture technologies and practices that can reduce greenhouse gas emissions and enhance productivity while ensuring food security. Based on a comprehensive survey of the previously published works, it was found that due to global warming, altered precipitation patterns, and an increase in the frequency of extreme events, climate change has negatively impacted food security by reducing agricultural yields, slowing animal growth rates, and decreasing livestock productivity. The reviewed works also suggest that using digital technology in agriculture is necessary to mitigate the effect of climate change and food insecurity. In addition, issues regarding creating sustainable agricultural food systems, minimizing environmental pollution, increasing yields, providing fair and equitable food distribution, and reducing malnutrition leading to food security were discussed in detail. It was shown that while digital agriculture has a crucial role in mitigating climate change and ensuring food security, it requires a concerted effort from policymakers, researchers, and farmers to ensure that the benefits of digitalization are realized in a sustainable and equitable manner.

Since the 20th century, global climate has been recognized as the most important environmental factor affecting the distribution of plants. Angelica dahurica ( A. dahurica ) has been in great demand as a medicinal herb and flavoring, but the lack of seed sources has hindered its development. In this study, we utilized the Maxent model combined with Geographic Information System (GIS) to predict the potential habitat of A. dahurica in China based on its geographical distribution and 22 environmental factors. This prediction will serve as a valuable reference for the utilization and conservation of A. dahurica resources.The results indicated that: (1) the Maxent model exhibited high accuracy in predicting the potential habitat area of A. dahurica , with a mean value of the area under the ROC curve (AUC) at 0.879 and a TSS value above 0.6; (2) The five environmental variables with significant effects were bio6 (Min temperature of the coldest month), bio12 (Annual Precipitation), bio17 (Precipitation of Driest Quarter), elevation, and slope, contributing to a cumulative total of 89.6%. Suitable habitats for A. dahurica were identified in provinces such as Yunnan, Guizhou, Guangxi, Sichuan, Hunan, and others. The total area of suitable habitat was projected to increase, with expansion primarily in middle and high latitudes, while areas of decrease were concentrated in lower latitudes. Under future climate change scenarios, the centers of mass of suitable areas for A. dahurica were predicted to shift towards higher latitudes in the 2050s and 2090s, particularly towards the North China Plain and Northeast Plain. Overall, it holds great significance to utilize the Maxent model to predict the development and utilization of A. dahurica germplasm resources in the context of climate change.

Identifying and predicting the impacts of climate change are crucial for various purposes, such as maintaining biodiversity, agricultural production, ecological security, and environmental conservation in different regions. In this paper, we used the surface pressure (SP), surface temperature (ST), 2-m air temperature (AT), 2-m dewpoint temperature (DT), 10-m wind speed (WS), precipitation (PRE), relative humidity (RH), actual evapotranspiration (ET a ), potential evapotranspiration (ET P ), total solar radiation (TRs), net solar radiation (NRs), UV intensity (UVI), sunshine duration (SD), convective available potential energy (CAPE) as factors in our climate modeling. The spatiotemporal distribution characteristics of the climate factors were analyzed and identified based on historical data for China from 1950 to 2020 using factor analysis and a grey model (GM (1,1)), and their future change characteristics were predicted. The results show that there is a strong correlation between climate factors. ST, AT, DT, PRE, RH, and ET a are the main factors that have the potential to cause heavy rain, thunderstorms, and other severe weather. Meanwhile, PRE, RH, TR s , NR s , UVI, and SD are among the major factors linked to climate change. Specifically, SP, ST, AT, and WS are among the minor factors in most areas. The top ten provinces in terms of combined factor scores are Heilongjiang, Neimenggu, Qinghai, Beijing, Shandong, Xizang, Shanxi, Tianjin, Guangdong, and Henan. The trend of climate factors in China is expected to remain relatively stable over the next 30 years, with a noteworthy decrease observed in CAPE compared to the past 71 years. Our findings can help to better mitigate the risks associated with climate change and enhance resilience; they also provide a scientific basis for environmental, ecological, and agricultural systems to cope with climate change.

• Despite widespread anthropogenic nutrient enrichment, it remains unclear how nutrient enrichment influences plant–arbuscular mycorrhizal fungi (AMF) symbiosis and ecosystem multifunctionality at the global scale. • Here, we conducted a meta-analysis to examine the worldwide effects of nutrient enrichment on AMF and plant diversity and ecosystem multifunctionality using data of field experiments from 136 papers. • Our analyses showed that nutrient addition simultaneously decreased AMF diversity and abundance belowground and plant diversity aboveground at the global scale. The decreases in AMF diversity and abundance associated with nutrient addition were more pronounced with increasing experimental duration, mean annual temperature (MAT) and mean annual precipitation (MAP). Nutrient addition-induced changes in soil pH and available phosphorus (P) predominantly regulated the responses of AMF diversity and abundance. Furthermore, AMF diversity correlated with ecosystem multifunctionality under nutrient addition worldwide. • Our findings identify the negative effects of nutrient enrichment on AMF and plant diversity and suggest that AMF diversity is closely linked with ecosystem function. This study offers an important advancement in our understanding of plant–AMF interactions and their likely responses to ongoing global change.

Plant functional traits often show strong latitudinal trends. To explain these trends, studies have often focused on environmental variables, correlations with other traits that themselves show latitudinal trends, and phylogenetic conservatism. However, few studies have systematically disentangled the relative contributions of these factors. Using a dataset consisting of 9,370 plant species from Southwest China, we investigated factors affecting fruit type (fleshy vs. dry): plant growth form, environmental constraints (summarized by climate region), and phylogenetic conservatism. Growth form and climate region are often cited in the literature as important explanations for the higher proportion of fleshy fruited species in the tropics. Nonetheless, in our analyses using partial R², growth form and climate region explained only 1.7% and 0.3%, respectively, of the variance in fruit type in a model including phylogeny, while phylogenetic conservatism explained 79.5%. Furthermore, phylogenetic conservatism was evenly distributed along the phylogeny, implying that fruit type reflects both ancient and recent phylogenetic relationships. Our findings illustrate the value of parsing out the contributions of explanatory variables and phylogeny to the variance in species’ traits. Methods using phylogenies that calculate partial R² give a more informative tool than traditional methods to explore the phylogenetic patterns of functional traits.

Deserts are important landscapes on the earth and their variations have impacts on global climate through feedback processes. However, there is a limited understanding of the climatic controls on the spatial and temporal variations of global deserts. Here, we use climate reanalysis datasets, global land use/land cover (LULC) products and the CMIP6 (Coupled Model Intercomparison Project) model outputs to calculate the changing of global deserts during 1982–2020, and estimate future spatial trends of global deserts. Our results show that mean annual global desert area over this period is 17.64×10 6 km 2 , accounting for 12% of the terrestrial land. Desert areas decreased rapidly from the end of the 1980s to the 1990s in North Africa and Australia, followed by a slow expansion in the early 21st century globally. Spatio-temporal variations of areas of arid climate are characterized by interdecadal fluctuations, and there are clear regional differences in dynamics of the aridity index (AI, used here as a proxy for the area of drylands) and desert areas. Statistical analyses reveal that increased vegetation cover is directly related to the reduction of desert area, while potential evaporation, surface temperature and humidity are also significantly correlated with the desert area. The relationship between wind speed and desert dynamics varies regionally. The results of the CMIP6 simulations suggest that global deserts will expand in the 21st century, albeit at different rates under the ssp245 and ssp585 scenarios. Desert expansions are modelled to be greatest in Asia, Africa and Australia, while those of southern North Africa may reduce as their southern borders migrate northwards.

We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on a provincial scale. Potential driving factors were divided into two groups according to scale of influence: ‘climate factors’, which operate on a regional scale, and ‘local factors’, which includes infrastructure, vegetation, topographic and socioeconomic data. The groups of factors were analysed separately and then significant factors from both groups were analysed together. Both models identified significant driving factors, which were ranked in terms of relative importance. Results show that climate factors are the main drivers of fire occurrence in the forests of Fujian, China. Particularly, sunshine hours, relative humidity (fire seasonal and daily), precipitation (fire season) and temperature (fire seasonal and daily) were seen to play a crucial role in fire ignition. Of the local factors, elevation, distance to railway and per capita GDP were found to be most significant. Random Forest demonstrated a higher predictive ability than logistic regression across all groups of factors (climate, local, and climate and local combined). Maps of the likelihood of fire occurrence in Fujian illustrate that the high fire-risk zones are distributed across administrative divisions; consequently, fire management strategies should be devised based on fire-risk zones, rather than on separate administrative divisions.

The large-scale vegetation restoration project on the Loess Plateau increased the ecosystem carbon (C) stocks and affected C budget in arid and semi-arid ecosystems. The specific details affecting the C stocks, their distribution, and dependence on land use and climate were never presented and generalized. We assessed the effects of climate factors and soil properties on ecosystem C stocks through field investigation across the Loess Plateau. The total C stocks in the four ecosystems: forestlands [0.36], shrublands [0.24], grasslands [1.18], and farmlands [1.05] was 2.84 Pg (1 Pg = 10 15  g), among which 30% were stored in topsoil (0–20 cm), 53% in above-ground biomass, and 17% in roots. The total ecosystem C density decreased according to the climate from the southeast (warm dry) to the northwest (cold moist) of the Loess Plateau. The ecosystem C density decreased with increasing temperature (from 5 to 15 °C), but increased with precipitation (from 200 to 700 mm). Variation partitioning analysis and structural equation models indicated that ecosystem C density was more explained by climate compared with soil properties. This supports the theory and empirical findings that large scale pattern of ecosystem C density is predominantly regulated by climate on the Loess Plateau. Our results highlight that grasslands are more predestined to store C compared with the other ecosystems, and the C stored in roots is substantial and should be considered when assessing C stocks and strongly contributes to soil organic matter formation. We suggest that investing in roots can be an effective strategy for meeting part of Loess Plateau C reduction goals to mitigate climate change, which is necessary for validating and parameterizing C models worldwide.