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Troides aeacus is the largest butterfly in China and is highly valued for its ornamental beauty. Due to T. aeacus being classified as a national second-class protected species in China, studying its spatial distribution is crucial for developing effective conservation measures. In this study, a total of 490 distribution points were obtained, and the potential distribution areas of the golden-sheathed T. aeacus were analyzed by using the maximum entropy model (MaxEnt) based on three different greenhouse gas emission scenarios, namely, SSP1-2.6, SSP2-4.5, and SSP5-8.5, in combination with nine important environmental variables. The results indicate that temperature and precipitation are the primary environmental factors influencing the suitable habitat of T. aeacus, with key variables including the minimum temperature of the coldest month (bio6), temperature annual range (bio7), mean temperature of the warmest quarter (bio10), annual precipitation (bio12), precipitation of the coldest quarter (bio19), and slope. The height distribution of T. aeacus in my country is in the area south of the Huaihe River in the Qinling Mountains, with a total area of 270.96 × 104 km², accounting for 28.23% of the total area of China. According to future climate change conditions, as climate warming progresses, both low- and high-suitability areas show an expansion trend in most scenarios, particularly under the SSP5-8.5 scenario, where highly suitable areas increase significantly while moderately suitable areas gradually shrink. To address future climate change, conservation strategies should focus on protecting highly suitable areas and strengthening the management of marginal habitats to enhance the adaptability and survival chances of T. aeacus. Full article

Global climate change exerts great effects on plant distributions. However, the response of Prunus mira Koehne, one of the most important species for ecological protection in the southeast of the Qinghai–Tibet Plateau, to climate change remains unclear. To explore the ecological factors affecting the distribution of P. mira in the context of global climate change, the MaxENT model is used to predict suitable habitats for P. mira. Our study indicated that the distribution of Prunus mira Koehn is primarily influenced by temperature rather than precipitation, and warming can facilitate the growth of P. mira. When the temperature seasonality (bio4) ranges from 134 to 576 and the mean temperature of the coldest quarter (bio11) ranges from −2.6 °C to 2.7 °C, it is most conducive to the growth of P. mira. Among the four climate scenarios, the optimal habitat for P. mira is predominantly concentrated in river valley areas and is expected to expand into higher altitude regions, particularly in the north and southeast. SSP245 and SSP370 climate pathways are conducive to the growth and spatial expansion of P. mira. Our findings highlight the significant impact of temperature not precipitation on the distribution of P. mira, and this insight is crucial for the stability and conservation of this ecologically significant plant species. Full article

Stormwater resource utilization is an important function of coupled grey–green infrastructure (CGGI) that has received little research focus, especially in multi-objective optimization studies. Given the complex water problems in areas with contamination-induced water shortages, it is important to incorporate more objectives into optimization systems. Therefore, this study integrated economic performance, hydrological recovery, water quality protection, and stormwater resource utilization into an optimization framework based on the non-dominant sorting genetic algorithm III (NSGA-III). A sponge city pilot area with contamination-induced water shortages in the Yangtze River Delta was considered, optimizing four objectives under different future multi-dimensional scenarios. The results showed a time series and scenarios composed of shared socioeconomic pathways and representative concentration pathways (SSP-RCP scenarios) which, together, affected future climate change and the benefits of a CGGI. In the near and middle periods, the SSP126 scenario had the greatest influence on stormwater management, whereas, in the far period, the SSP585 scenario had the greatest influence. The far period had the greatest influence under three SSP-RCP scenarios. Under the combined influence of SSP-RCP scenarios and a time series, the SSP585-F scenario had the greatest impact. Specific costs could be used to achieve different and no stormwater-resource utilization effects through different configurations of the CGGI. This provided various construction ideas regarding CGGIs for areas with contamination-induced water shortages. Full article

Extreme weather events, like flooding, are expected to become more severe due to climate change and increasingly impact populations across the US. Adding to this challenge, populations have concurrently settled in risky areas that were previously thought to have low, or no, exposure. Objective: This research seeks to understand the unique contribution of population growth and climate change as independent components of future risk levels in the US. To do so, future population level forecasts are coupled with future flood projections along all five Shared Socioeconomic Pathways (SSPs) at the block group level across the US. The results indicate that, across the five SSPs, the most increase in exposure will occur in SSP5 (+470,719), and the least will occur in SSP 3 (+57,189). By decomposing the contributions from flood and population growth, we find that the population growth-induced effect contributed to an increase in the population exposure for all of the SSPs except for SSP3. This research, and these results, provide a foundation for understanding future risks of flood exposure in an isolated framework and lay the groundwork for the development and integration of planning, adaptation, and mitigation efforts that may be used to address the growing risk of flooding in the context of the population forecasts provided here. Full article

Accurately predicting the wheat potential yield (PY) is crucial for enhancing agricultural management and improving resilience to climate change. However, most existing crop models for wheat PY rely on type-specific parameters that describe wheat traits, which often require calibration and, in turn, reduce prediction confidence when applied across different spatial or temporal scales. In this study, we integrated eco-evolutionary optimality (EEO) principles with a universal productivity model, the Pmodel, to propose a comprehensive full-chain method for predicting wheat PY. Using this approach, we forecasted wheat PY across China under typical shared socioeconomic pathways (SSPs). Our findings highlight the following: (1) Incorporating EEO theory improves PY prediction performance compared to current parameter-based crop models. (2) In the absence of phenological responses, rising atmospheric CO₂ concentrations universally benefit wheat growth and PY, while increasing temperatures have predominantly negative effects across most regions. (3) Warmer temperatures expand the window for selecting sowing dates, leading to a national trend toward earlier sowing. (4) By simultaneously considering climate impacts on wheat growth and sowing dates, we predict that PY in China’s main producing regions will significantly increase from 2020 to 2060 and remain stable under SSP126. However, under SSP370, while there is no significant trend in PY during 2020–2060, increases are expected thereafter. These results provide valuable insights for policymakers navigating the complexities of climate change and optimizing wheat production to ensure food security. Full article

Land suitability analyses are crucial for identifying sustainable areas for agricultural crops and developing appropriate land use strategies. Thus, the present study aims to analyze the current and future land suitability for wheat (Triticum aestivum L.) cultivation in Ethiopia. Twelve variables including soil properties, climate variables, and topographic characteristics were used in the evaluation of land suitability. Statistical methods such as Rotated Empirical Orthogonal Functions (REOF), Coefficient of Variation (CV), correlation, and parametric and non-parametric trend analyses were used to analyze the spatiotemporal variability in current and future climate data and identified significant patterns of variability. For future projections of land suitability and climate, this study employed climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) framework, downscaled using regional climate model version 4.7 (RegCM4.7) under two different Shared Socioeconomic Pathway (SSP) climate scenarios: SSP1 (a lower emission scenario) and SSP5 (a higher emission scenario). Under the current condition, during March, April, and May (MAM), 53.4% of the country was suitable for wheat cultivation while 44.4% was not suitable. In 2050, non-suitable areas for wheat cultivation are expected to increase by 1% and 6.9% during MAM under SSP1 and SSP5 climate scenarios, respectively. Our findings highlight that areas currently suitable for wheat may face challenges in the future due to altered temperature and precipitation patterns, potentially leading to shifts in suitable areas or reduced productivity. This study also found that the suitability of land for wheat cultivation was determined by rainfall amount, temperature, soil type, soil pH, soil organic carbon content, soil nitrogen content, and elevation. This research underscores the critical importance of integrating spatiotemporal climate variability with future projections to comprehensively assess wheat suitability. By elucidating the implications of climate change on wheat cultivation, this study lays the groundwork for developing effective adaptation strategies and actionable recommendations to enhance management practices. The findings support the county’s commitment to refining agricultural land use strategies, increasing wheat production through suitability predictions, and advancing self-sufficiency in wheat production. Additionally, these insights can empower Ethiopia’s agricultural extension services to guide farmers in cultivating wheat in areas identified as highly and moderately suitable, thereby bolstering production in a changing climate. Full article

Sect. Tuberculata, as one of the endemic plant groups in China, belongs to the genus Camellia of the Theaceae family and possesses significant economic and ecological value. Nevertheless, the characteristics of habitat distribution and the major eco-environmental variables affecting its suitability are poorly understood. In this study, using 65 occurrence records, along with 60 environmental factors, historical, present and future suitable habitats were estimated using MaxEnt modeling, and the important environmental variables affecting the geographical distribution of sect. Tuberculata were analyzed. The results indicate that the size of the its potential habitat area in the current climate was 1.05 × 10⁵ km², and the highly suitable habitats were located in Guizhou, central-southern Sichuan, the Wuling Mountains in Chongqing, the Panjiang Basin, and southwestern Hunan. The highest probability of presence for it occurs at mean diurnal range (bio2) ≤ 7.83 °C, basic saturation (s_bs) ≤ 53.36%, temperature annual range (bio7) ≤ 27.49 °C, −7.75 °C < mean temperature of driest quarter (bio9) < 7.75 °C, annual UV-B seasonality (uvb2) ≤ 1.31 × 10⁵ W/m², and mean UV-B of highest month (uvb3) ≤ 5089.61 W/m². In particular, bio2 is its most important environmental factor. During the historical period, the potential habitat area for sect. Tuberculata was severely fragmented; in contrast, the current period has a more concentrated habitat area. In the three future periods, the potential habitat area will change by varying degrees, depending on the aggressiveness of emissions reductions, and the increase in the potential habitat area was the largest in the SSP2.6 (Low-concentration greenhouse gas emissions) scenario. Although the SSP8.5 (High-concentration greenhouse gas emissions) scenario indicated an expansion in its habitat in the short term, its growth and development would be adversely affected in the long term. In the centroid analysis, the centroid of its potential habitat will shift from lower to higher latitudes in the northwest direction. The findings of our study will aid efforts to uncover its originsand geographic differentiation, conservation of unique germplasms, and forestry development and utilization. Full article

Compound extreme events can cause serious impacts on both the natural environment and human beings. This work aimed to explore the changes in compound drought–heatwave and heatwave–extreme precipitation events (i.e., CDHEs and CHPEs) across China using daily-scale gauge-based meteorological observations, and to examine their future projections and potential risks using the Coupled Model Intercomparison Project (CMIP6) under the shared socioeconomic pathway (SSP) scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5). The results show the following: (1) The frequencies of CDHEs and CHPEs across China showed a significant increasing trend from 1961 to 2020, with contrasting trends between the first half and second half of the period (i.e., a decrease from 1961 to 1990 and an increase from 1991 to 2020). Similar trends were observed for four intensity levels (i.e., mild, moderate, severe, and extreme) of CDHEs and CHPEs. (2) All the frequencies under three SSP scenarios will show increasing trends, especially under higher emission scenarios. Moreover, the projected intensities of CDHEs and CHPEs will gradually increase, especially for higher levels. (3) The exposure of the population (POP) and Gross Domestic Product (GDP) will be concentrated mainly in China’s coastal areas. The GDP exposures to the CDHEs and CHPEs will reach their highest values for SSP5-8.5, while the POP exposure will peak for SSP2-4.5 and SSP5-8.5, respectively. Our findings can offer scientific and technological support to actively mitigate future climate change risks. Full article

Climate change is becoming an important driver of biodiversity loss by altering the habitat, distribution and interspecific relationships of species. Japanese yew (Taxus cuspidata) is a first class protected plant in China, which has important ecological significance and occupies a certain position in the feeding habit of wapiti (Cervus elaphus) and Siberian roe deer (Capreolus pygargus). Due to human and animal damage, the number of Japanese yew has gradually decreased. Therefore, understanding the potential distribution of Japanese yew and the suitable areas for deer to browse on it under climate change will help to further protect these three species in Northeast China, especially migrate to more suitable areas in different scenarios in the future. From July 2021 to July 2024, we collected the information of species distribution and the variables associated with the species’ ecological limits in Muling National Nature Reserve to cross-reflect the current and future distribution and feeding area of the two species to assess each other’s impacts with Maximum entropy model (MaxEnt). The results showed that under the SSP2-4.5 and SSP5-8.5 scenarios, feeding pressure, driest quarter precipitation (BIO17) and seasonal temperature variation coefficient (BIO4) were the main variables affecting the distribution of Japanese yew, and the driest quarter precipitation (BIO17) and annual precipitation (BIO12) were the main variables affecting wapiti and Siberian roe deer foraging them. Under SSP2-4.5 and SSP5-8.5 scenarios, the suitable area of Japanese yew and the feeding area of the two species of deer gradually decreased from 2041 to 2100. Compared with wapiti, Siberian roe deer has a greater impact on the distribution range of Japanese yew, and the suitable feeding area is wider. It is expected that the potential centroid of Japanese yew, wapiti and Siberian roe deer will migrate to higher latitudes in the future. These findings provide a scientific basis for the reserve to develop relevant measures and plans and effectively protect the three species. Full article

The accurate prediction of global forest soil respiration (Rs) is critical for climate change research. Rs consists of autotrophic (Ra) and heterotrophic (Rh) respiration, which respond differently to environmental factors. Predicting Rs as a single flux can be biased; therefore, Ra and Rh should be predicted separately to improve prediction accuracy. In this study, we used the SRDB_V5 database and the random forest model to analyze the uncertainty in predicting Rs using a single global model (SGM) and Ra/Rh using a specific categorical model (SCM) and predicted the spatial dynamics of the distribution pattern of forest Ra, Rh, and Rs in the future under the two different climate patterns. The results show that Rs is higher under tropical and inland climatic conditions, while Rh fluctuates less than Ra and Rs. In addition, the SCM predictions better capture key environmental factors and are more consistent with actual data. In the SSP585 (high emissions) scenario, Rs is projected to increase by 19.59 percent, while in the SSP126 (low emissions) scenario, Rs increases by only 3.76 percent over 80 years, which underlines the need for SCM in future projections. Full article

This study focuses on the impacts of climate change on hydrological processes in watersheds and proposes an integrated approach combining a weather generator with a multi-site conditional generative adversarial network (McGAN) model. The weather generator incorporates ensemble GCM predictions to generate regional average synthetic weather series, while McGAN transforms these regional averages into spatially consistent multi-site data. By addressing the spatial consistency problem in generating multi-site synthetic weather series, this approach tackles a key challenge in site-scale climate change impact assessment. Applied to the Jinghe River Basin in west-central China, the approach generated synthetic daily temperature and precipitation data for four stations under different shared socioeconomic pathways (SSP1-26, SSP2-45, SSP3-70, SSP5-85) up to 2100. These data were then used with a long short-term memory (LSTM) network, trained on historical data, to simulate daily river flow from 2021 to 2100. The results show that (1) the approach effectively addresses the spatial correlation problem in multi-site weather data generation; (2) future climate change is likely to increase river flow, particularly under high-emission scenarios; and (3) while the frequency of extreme events may increase, proactive climate policies can mitigate flood and drought risks. This approach offers a new tool for hydrologic–climatic impact assessment in climate change studies. Full article

Sea beet (Beta vulgaris L. subsp. maritima (L.) Arcang.) is a wild member of the Amaranthaceae family and a progenitor for all the cultivated beets (Beta vulgaris subsp. vulgaris). It is a source of stress-resistant genes, contributing 21 valuable traits to sugar beet through multiple breeding approaches. Despite its importance, the core morphological diversity of sea beet within the Egyptian Mediterranean coastal region has not yet been thoroughly explored. The field observations indicated notable morphological diversity among sea beet populations. This study investigated the morphological diversity of six sea beet populations along with their associated soil and climatic conditions in their primary habitats. Our morphometric investigations identified two varieties: Beta vulgaris subsp. maritima var. glabra, characterized by glabrous, erect, larger basal leaves, and Beta vulgaris subsp. maritima var. pilosa, distinguished by its hairy, prostrate form with smaller basal leaves. These varieties exhibited differences in their spatial distribution, showing high variations at the inter- and intra-population levels as well as the variety level. Soil parameters significantly influenced population morphological variability, which demonstrated a strong positive correlation with soil organic carbon. Our results highlight the need for precise survey and molecular characterization to secure these potential genetic resources from alteration and loss, especially in coastal habitats that are particularly sensitive to future climate change. Full article

Phosphate has been proven to be effective in remediating soils contaminated with potentially toxic elements (PTEs); however, the potential release of phosphorus (P) through runoff and the impact on PTEs’ transport in this process have never been assessed. A rainfall simulation study was conducted to investigate P runoff loss and its impact on the stability of arsenic (As) and cadmium (Cd) after applying potassium dihydrogen phosphate (PDP), superphosphate (SSP), and ground phosphate rock (GPR) in soil trays packed with As–Cd-contaminated soil. The phosphorus loss through runoff and sedimentary phases followed the order of SSP > PDP > GPR > control. Phosphate fertilizers’ application reduced the mobility of As and Cd. In the first rainfall, the enrichment ratios (ERs) of As and Cd in the sedimentary phase after PDP, SSP, and GPR treatment were 0.12, 0.04, and 0.08 and 0.24, 0.16, and 0.07 units lower than the control, respectively. The <53 μm fraction in the sedimentary phase accounted for 53.06–75.95%, and phosphate fertilizers significantly enhanced the As and Cd stability in this fraction. The XPS analysis showed that the conversion of As(III) to As(V) and the generation of Cd–phosphate compounds were important reasons for enhancing As and Cd stability. This study demonstrated that PDP might be capable of the remediation of As–Cd contamination with the least release of P to watersheds. Full article

Climate warming exacerbates the deterioration of soil and degradation of vegetation caused by coastal flooding, impairing ecosystem climate-regulating functions. This will elevate the risk of carbon storage (CS) loss, further intensifying climate change. To delve deeper into this aspect, we aimed to integrate future land use/land cover changes and global mean sea-level rise to assess the impact of coastal floods on terrestrial CS under the effects of climate change. We compared the 10-year (RP10) and 100-year (RP100) return-period floods in 2020 with projected scenarios for 2050 under SSP1-26, SSP2-45, SSP3-70, and SSP5-85. The study findings indicate that CS loss caused by coastal flooding in China’s coastal zones was 198.71 Tg (RP10) and 263.46 Tg (RP100) in 2020. In 2050, under the SSP1-26, SSP2-45, and SSP3-70 scenarios, the CS loss is projected to increase sequentially, underscoring the importance of implementing globally coordinated strategies for mitigating climate change to effectively manage coastal flooding. The value of CS loss is expected to increase in 2050, with an anticipated rise of 97–525% (RP10) and 91–498% (RP100). This highlights the essential need to include coastal flood-induced CS changes in carbon emission management and coastal climate risk assessments. Full article

This study investigated the effects of fermenting plum pulp with Bifidobacterium animalis ssp. lactis (BAL) on its physicochemical and bioactive properties, as well as the optimization of the freeze-drying (FD) process to develop a fruit-based probiotic delivery system. Fermentation significantly reduced the pH and total acidity of the pulp, achieving a cell viability of 11 log CFU/mL. The FD process was optimized using a factorial design, with maltodextrin concentration (3, 5, and 7%) and freezing temperature (−150, −100, and −50 °C) as variables. The P2 experiment, which used 7% maltodextrin and freezing at −150 °C, showed the best results in terms of yield (25.67%), cell viability (8 log CFU/g), and probiotic survival rate (97.66%). Samples P5, P6, and P7, prepared with 5% maltodextrin and freezing at −100 °C, exhibited the highest levels of bioactive compounds and antioxidant activity (p < 0.05). During 28 days of storage, all samples maintained cell viability without significant logarithmic reduction. In summary, probiotic plum powders offer an excellent plant-based alternative for probiotic consumption, providing safe levels of beneficial bacteria and bioactive compounds with antioxidant action, meeting health and nutrition needs. Full article