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Influences of the increasing pressure of climate change and anthropogenic activities on wetlands ecosystems and agriculture are significant around the world. This paper assessed the spatiotemporal land use and land cover changes (LULCC), especially for conversion from marshland to other LULC types (e.g., croplands) over the Songnen and Sanjiang Plain (SNP and SJP), northeast China, during the past 35 years (1980–2015). The relative role of human activities and climatic changes in terms of their impacts on wetlands and agriculture dynamics were quantitatively distinguished and evaluated in different periods based on a seven-stage LULC dataset. Our results indicated that human activities, such as population expansion and socioeconomic development, and institutional policies related to wetlands and agriculture were the main driving forces for LULCC of the SJP and SNP during the past decades, while increasing contributions of climatic changes were also found. Furthermore, as few studies have identified which geographic regions are most at risk, how the future climate changes will spatially and temporally impact wetlands and agriculture, i.e., the suitability of wetlands and agriculture distributions under different future climate change scenarios, were predicted and analyzed using a habitat distribution model (Maxent) at the pixel-scale. The present findings can provide valuable references for policy makers on regional sustainability for food security, water resource rational management, agricultural planning and wetland protection as well as restoration of the region.

Arid and semi-arid regions account for about 40% of the world's land surface area, and are the most sensitive areas to climate change, leading to a dramatic expansion of arid regions in recent decades. Ephemeral plants are crucial herbs in this area and are very sensitive to climate change, but it is still unclear which factors can determine the distribution of ephemeral plants and how the distribution of ephemeral plants responds to future climate change across the globe. Understanding the impact of climate change on ephemeral plant distribution is crucial for sustainable biodiversity conservation. This study explored the potential distribution of three types of ephemeral plants in arid and semi-arid regions (cold desert, hot desert, and deciduous forest) on a global scale using the MaxEnt software. We used species global occurrence data and 30 environmental factors in scientific collections. Our results showed that (1) the average value of the area under the receiver operating curve (AUC) of each species was higher than 0.95, indicating that the MaxEnt model's simulation accuracy for each species was good; (2) distributions of cold desert and deciduous forest species were mainly determined by soil pH and annual mean temperature; the key factor that determines the distribution of hot desert species was precipitation of the driest month; and (3) the potential distribution of ephemeral plants in the cold desert was increased under one-third of climate scenarios; in the hot desert, the potential suitable distribution for was decreased in more than half of the climate scenarios, but was increased in more than half of the climate scenarios. In deciduous forests, the ephemeral plant decreased in nearly nine-tenths of climate scenarios, and was increased in 75% of climate scenarios. The potential suitable distributions of ephemeral plants in the different ecosystems were closely related to their specific adaptation strategies. These results contribute to a comprehensive understanding of the potential distribution pattern of some ephemeral plants in arid and semi-arid ecosystems.

Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% of the overall afforested area. Moreover, it plays a critical role in soil and water conservation; however, its suitability is subject to climate change. For this study, the appropriate distribution area of C. lanceolata was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed that (1) the minimum temperature of the coldest month (bio6), and the mean diurnal range (bio2) were the most important environmental variables that affected the distribution of C. lanceolata; (2) the currently suitable areas of C. lanceolata were primarily distributed along the southern coastal areas of China, of which 55% were moderately so, while only 18% were highly suitable; (3) the projected suitable area of C. lanceolata would likely expand based on the BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under different SSPs spanning 2041–2060. The increased area estimated for the future ranged from 0.18 to 0.29 million km2, where the total suitable area of C. lanceolata attained a maximum value of 2.50 million km2 under the SSP3-7.0 scenario, with a lowest value of 2.39 million km2 under the SSP5-8.5 scenario; (4) in combination with land use and farmland protection policies of China, it is estimated that more than 60% of suitable land area could be utilized for C. lanceolata planting from 2041–2060 under different SSP scenarios. Although climate change is having an increasing influence on species distribution, the deleterious impacts of anthropogenic activities cannot be ignored. In the future, further attention should be paid to the investigation of species distribution under the combined impacts of climate change and human activities.

Climate change has a significant impact on the growth and distribution of vegetation worldwide. Hydrangea macrophylla is widely distributed and considered a model species for studying the distribution and responses of shrub plants under climate change. These results can inform decision‐making regarding shrub plant protection, management, and introduction of germplasm resources, and are of great importance for formulating ecological countermeasures to climate change in the future. We used the maximum entropy model to predict the change, scope expansion/reduction, centroid movement, and dominant climate factors that restrict the growth and distribution of H. macrophylla in China under current and future climate change scenarios. It was found that both precipitation and temperature affect the distribution of suitable habitat for H. macrophylla. Akaike information criterion (AICc) was used to select the feature combination (FC) and the regularization multiplier (RM). After the establishment of the optimal model (FC = QP, RM = 0.5), the complexity and over‐fitting degree of the model were low (delta AICc = 0, omission rate = 0.026, difference between training and testing area under the curve values = 0.0009), indicating that it had high accuracy in predicting the potential geographical distribution of H. macrophylla (area under the curve = 0.979). Overall, from the current period to future, the potential suitable habitat of this species in China expanded to the north. The greenhouse effect caused by an increase in CO2 emissions would not only increase the area of high‐suitability habitat in Central China, but also expand the area of total suitable habitat in the north. Under the maximum greenhouse gas emission scenario (RCP8.5), the migration distance of the centroid was the longest (e.g., By 2070s, the centroids of total and highly suitable areas have shifted 186.15 km and 89.84 km, respectively). In this manuscript, we used the maximum entropy model to predict the change, scope expansion/reduction, centroid movement, and dominant climate factors that restrict its growth and distribution in China under modern and future climate change scenarios.

Climate change has significantly influenced the growth and distribution of plant species, particularly those with a narrow ecological niche. Understanding climate change impacts on the distribution and spatial pattern of endangered species can improve conservation strategies. The MaxEnt model is widely applied to predict species distribution and environmental tolerance based on occurrence data. This study investigated the suitable habitats of the endangered Ormosia microphylla in China and evaluated the importance of bioclimatic factors in shaping its distribution. Occurrence data and environmental variables were gleaned to construct the MaxEnt model, and the resulting suitable habitat maps were evaluated for accuracy. The results showed that the MaxEnt model had an excellent simulation quality (AUC = 0.962). The major environmental factors predicting the current distribution of O. microphylla were the mean diurnal range (bio2) and precipitation of the driest month (bio14). The current core potential distribution areas were concentrated in Guangxi, Fujian, Guizhou, Guangdong, and Hunan provinces in south China, demonstrating significant differences in their distribution areas. Our findings contribute to developing effective conservation and management measures for O. microphylla , addressing the critical need for reliable prediction of unfavorable impacts on the potential suitable habitats of the endangered species.

IntroductionClimate change will either improve, reduce, or shift its appropriate climatic habitat of a particular species, which could result in shifts from its geographical range. Predicting the potential distribution through MaxEnt modeling has been developed as an appropriate tool for assessing habitat distribution and resource conservation to protect bamboo species.MethodsOur objective is to model the current and future distribution of Oxytenanthera abyssinica (A. Richard) based on three representative concentration pathways (RCP) (RCP2.6, RCP4.5, and RCP8.5) for 2050s and 2070s using a maximum entropy model (MaxEnt) in Northern Ethiopia. For modeling procedure, 77 occurrence records and 11 variables were retained to simulate the current and future distributions of Oxytenanthera abyssinica in Northern Ethiopia. To evaluate the performance of the model, the area under the receiver operating characteristic (ROC) curve (AUC) was used.ResultsAll of the AUCs (area under curves) were greater than 0.900, thereby placing these models in the “excellent” category. The jackknife test also showed that precipitation of the coldest quarter (Bio19) and precipitation of the warmest quarter (Bio18) contributed 66.8% and 54.7% to the model. From the area of current distribution, 1367.51 km2 (2.52%), 7226.28 km2 (13.29%), and 5377.26 km2 (9.89%) of the study area were recognized as high, good, and moderate potential habitats of Oxytenanthera abyssinica in Northern Ethiopia, and the high potential area was mainly concentrated in Tanqua Abergele (0.70%), Kola Temben (0.65%), Tselemti (0.60%), and Tsegede (0.31%). Kafta Humera was also the largest good potential area, which accounts for 2.75%. Compared to the current distribution, the total area of the high potential regions and good potential regions for Oxytenanthera abyssinica under the three RCPs (RCP2.6, RCP4.5, and RCP8.5) would increase in the 2050s and 2070s. However, the total area of the least potential regions under the three RCPs (RCP2.6, RCP4.5, and RCP8.5) in 2050s and 2070s would decrease.ConclusionThis study can provide vital information for the protection, management, and sustainable use of Oxytenanthera abyssinica, the resource to address the global climate challenges.

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.

Due to climate change, it is significant to explore the impact of rising temperatures on the distribution of Dendrolimus houi Lajonquiere (Lepidoptera) and its host plants, Pinus yunnanensis and Cryptomeria fortunei , and to simulate their suitable future distribution areas in order to provide a theoretical basis for the monitoring of, and early warning about, D. houi and the formulation of effective prevention and control policies. Based on the known distribution areas of, and relevant climate data for, D. houi , P. yunnanensis , and C. fortunei , their suitable habitat in China was predicted using the ENMeval data package in order to adjust the maximum entropy (MaxEnt) model parameters. The results showed that the regularization multiplier was 0.5 when the feature combination was LQHPT, with a MaxEnt model of lowest complexity and excellent prediction accuracy. The main climate variable affecting the geographical distribution of D. houi , P. yunnanensis , and C. fortunei is temperature, specifically including isothermality, temperature seasonality, maximum temperature of warmest month, minimum temperature of warmest month, average temperature of coldest quarter. The potential suitable distribution areas for P. yunnanensis and D. houi were similar under climate change, mainly distributed in southwest China, while C. fortunei was mainly distributed in southeast China. Under different future-climate scenarios, the areas suitable for the three species will increase, except for P. yunnanensis in the 2070s under Shared Socioeconomic Pathway 5–8.5. With climate change, all three species were found to have a tendency to migrate to higher latitudes and higher altitudes. The centroids of the areas suitable for P. yunnanensis and D. houi will migrate to the northwest and the centroids of the areas suitable for C. fortunei will migrate to the northeast.

Bletilla striata , an important traditional Chinese medicine resource, holds high medicinal and ornamental value. However, unscientific habitat selection for its cultivation has led to low yields and poor quality as medicinal materials in China. The optimized MaxEnt model is a powerful tool for analyzing the potential impacts of environmental factors on species distribution and predicting habitat changes under climate change. It offers great significance for the protection and development of B. striata in China. Based on 269 B. striata distribution records in China and 15 major environmental factors, this study simulated the distribution patterns of potentially suitable B. striata habitats under four different climate change scenarios (SSP1.26, SSP2.45, SSP3.70, and SSP5.85) and three time periods (the current period, 2050s, and 2070s). The analysis was conducted using the MaxEnt model which exhibited high predictive accuracy and minimal overfitting. Solar radiation, annual temperature range and mean diurnal range were revealed as the dominant factors affecting B. striata distribution, and their thresholds were ≤ 16,265.39 kJ/m 2 ·d −1 , ≤ 39.7 ℃ and ≤ 12.6 ℃, respectively. The results showed that the total potentially suitable B. striata habitats in China were 30.07 × 10 5 km 2 under current climate conditions, mainly distributed in 14 provinces or regions in southern China. Under future climate change conditions, the predicted potentially suitable B. striata habitats will decrease significantly over time, and the centroid of the predicted potentially suitable habitats at all levels will shift northward. The research results can guide future B. striata resource conservation, variety selection, and cultivation.

Scots pine (Pinus sylvestris L.) is an essential species for biodiversity and ecosystem services in Türkiye, yet it is becoming increasingly vulnerable to climate change, especially in climatically marginal areas such as Central Anatolia. This study used MaxEnt modeling along with CHELSA V2.1 climate projections to evaluate the current and future distribution of Scots pine under three Shared Socioeconomic Pathways (SSP1 2.6, SSP3 7.0, SSP5 8.5) projected for the year 2100. The key climatic factors influencing habitat suitability include precipitation seasonality (Bio15) and temperature seasonality (Bio7). The results show that while 34% of Central Anatolia is currently suitable for Scots pine, habitat suitability could decline by 91% under SSP5 8.5, leaving only 4% of the region viable for the species by 2100. This significant reduction highlights the uncertain future of Scots pine populations in the area. Unlike previous research, this study provides a high-resolution analysis that incorporates fine-scale environmental and topographical variables, emphasizing the importance of mid-altitude refugia as potential climate shelters. Aligning with Sustainable Development Goal 15 (SDG15), this study underscores the need to incorporate climate projections into forest management practices. The findings contribute to a broader understanding of climate-induced range shifts and inform adaptive conservation strategies for other vulnerable tree species in semiarid regions.