Search Results (122)

The critically endangered African wild ass is found in low population densities and there may be as few as 600 individuals in the Danakil Desert of Ethiopia and Eritrea. An understanding of suitable habitats is important for prioritizing the conservation and management of the African wild ass. In this study, we recorded presence locations of the African wild ass and independently prepared environmental covariates to identify suitable habitats using the maximum entropy (Maxent) model. Model performances were high, with the area under the curve (AUC) values of 0.927 and 0.950 for wet and dry seasons, respectively. The predicted moderately suitable habitat area extent was greater during the wet season (15,223 km²) than during the dry season (6052 km²). Precipitation, temperature, and distance from water sources were vital variables for the wet season, while distance from water sources and distance from the settlements were important determinant covariates for the dry season. This information prioritizes where protected areas should be established for African wild ass conservation and also indicates potential new undocumented locations to guide surveys in the Danakil Desert of the Afar Region, Ethiopia. Full article

Climate change poses a serious threat to biodiversity, particularly for woody species with limited dispersal capacity such as Prunus avium L. (wild cherry). In this study, we assessed potential shifts in its suitable distribution range (SDR) across Türkiye by applying an ensemble modeling framework that combined Generalized Additive Models (GAM), Maximum Entropy (MaxEnt), and Random Forest (RF). We used updated occurrence data (including GBIF and EUFORGEN records) and 11 ecologically relevant bioclimatic variables under SSP2-4.5 and SSP5-8.5 scenarios. Model performance was validated using AUC (Area Under the ROC Curve) and TSS (True Skill Statistic) metrics. Results suggest that while 60–70% of current SDRs remain stable by 2100, approximately 10% may be lost, with 20–23% new expansions. Temperature seasonality (Bio4) and seasonal precipitation (Bio15) were consistently identified as dominant predictors across models. Notably, newly suitable habitats are expected to be spatially isolated, limiting natural colonization. Our findings highlight the necessity of proactive conservation planning, including assisted migration and drought-resistant genotype selection, to ensure long-term persistence of wild cherry under changing climates. These results offer actionable insights for adaptive forest management and biodiversity conservation in Mediterranean-type ecosystems. Full article

Species distribution models (SDMs) grapple with uncertainty. To address this, a parameter-optimized MaxEnt model was used to predict habitat suitability for Elsholtzia densa, a predominant agricultural weed on the Tibetan Plateau. Through multiparameter optimization with 149 occurrence points and three climate variable sets, we systematically evaluated how the three MaxEnt extrapolation approaches (Free Extrapolation, Extrapolation with Clamping, No Extrapolation) influenced model outputs. The results showed the following: (1) Model optimization using the Kuenm R package version (1.1.10) identified seven critical bioclimatic variables (Feature Combinations = LQTH, Regularization Multipliers = 2.5), with optimized models demonstrating high accuracy (Area Under Curve > 0.9). (2) Extrapolation approaches exhibited negligible effects on variable selection, though four bioclimatic variables “bio1 (annual mean temperature)”, “bio12 (annual precipitation)”, “bio2 (mean diurnal range)”, and “bio7 (temperature annual range)” predominantly drove model predictions. (3) Current high-suitability areas are clustered in the eastern and southern regions of the Tibetan Plateau, and with Free Extrapolation yielding the broadest current distribution. Climate change projections suggest habitat expansion, particularly under conditions of No Extrapolation. (4) Multivariate Environmental Similarity Surface (MESS) and Most Dissimilar Variable (MoD) are not affected by the extrapolation method, and extrapolation risk analyses indicate that future climate anomalies are mainly concentrated in the western and southern parts of the Tibetan Plateau and that future warming will further increase the unsuitability of these regions. (5) Variance analysis showed that the extrapolation methods did not significantly affect the 10-replicate results but influenced the parameter and emission scenarios, with No Extrapolation methods showing minimal variance changes. Our findings validate that multiparameter optimization improves species distribution model robustness, systematically characterizes extrapolation impacts on distribution projections, and provides a conceptual framework and early warning systems for agricultural weed management on the Tibetan Plateau. Full article

Solanum rostratum Dunal is a highly invasive species with strong environmental adaptability and reproductive capacity, posing serious threats to agroforestry ecosystems and human health. In this study, we compiled occurrence records of S. rostratum in China from online databases and sources in the literature. We employed the Biomod2 ensemble modeling framework to predict the potential distribution of the species under current climatic conditions and four future climate scenarios (SSP126, SSP245, SSP370, and SSP585), and to identify the key environmental variables influencing its distribution. The ensemble model based on the committee averaging (EMca) approach achieved the highest predictive accuracy, with a true skill statistic (TSS) of 0.932 and an area under the curve (AUC) of 0.990. Under present climatic conditions, S. rostratum is predominantly distributed across northern China, particularly in Xinjiang, Inner Mongolia, and the northeastern provinces, covering a total suitable area of 1,191,586.55 km², with highly suitable habitats accounting for 50.37% of this range. Under future climate scenarios, the species’ suitable range is projected to expand significantly, particularly under the high-emissions SSP585 scenario, with the distribution centroid expected to shift significantly toward high-altitude regions in Gansu Province. Precipitation and temperature emerged as the most influential environmental factors affecting habitat suitability. These findings indicate that ongoing global warming may facilitate the survival, reproduction, and rapid spread of S. rostratum across China in the coming decades. Full article

Sicyos angulatus L. is a rapidly spreading invasive alien vine that threatens natural and agricultural ecosystems globally. We collected occurrence data from 4886 sites and applied the maximum entropy (MaxEnt) model to assess current and future habitat suitability for S. angulatus. Future climate conditions were represented by low and high greenhouse gas concentrations under representative concentration pathways (i.e., RCP2.6 and RCP8.5, respectively). The MaxEnt model accurately predicted the distribution of S. angulatus, and the area under the receiver operating characteristic curve in the receiver operating characteristic test reached 0.921. Among the 19 climatic variables investigated, the best predictors for the distribution of S. angulatus were the precipitation in the driest month (with a contribution of 37.4%), annual precipitation (26.8%), average annual temperature (18.1%), and temperature seasonality (14.9%). Currently, the most suitable areas cover the central and eastern United States, parts of southern Europe, most Japanese islands, the majority of the Korean Peninsula, and eastern China, with a total area of 180.3 × 10⁴ km² (1.2% of the Earth’s land area). During the 2050s and 2090s under RCP2.6 and RCP8.5, the most suitable regions worldwide are projected to expand by factors of 1.0 and 2.2, respectively. In particular, suitable areas might expand to higher-latitude regions and encompass previously unsuitable areas, such as Liaoning Province in Northeast China. These findings may aid in the surveillance and management of S. angulatus’ invasion globally. Full article

As China’s first systematic assessment of high-risk Amaranthaceae invaders, this study addresses a critical knowledge gap identified in the National Invasive Species Inventory, in which four invasive Amaranthaceae species (Dysphania ambrosioides, Celosia argentea, Amaranthus palmeri, and Amaranthus spinosus) are prioritized due to CNY 2.6 billion annual ecosystem damages in China. By coupling multi-species comparative analysis with a parameter-optimized Maximum Entropy (MaxEnt) model integrating climate, soil, and topographical variables in China under Shared Socioeconomic Pathways (SSP) 126/245/585 scenarios, we reveal divergent expansion mechanisms (e.g., 247 km faster northward shift in A. palmeri than D. ambrosioides) that redefine invasion corridors in the North China Plain. Under current conditions, the suitable habitats of these species span from 92° E to 129° E and 18° N to 49° N, with high-risk zones concentrated in central and southern China, including the Yunnan–Guizhou–Sichuan region and the North China Plain. Temperature variables (Bio: Bioclimatic Variables; Bio6, Bio11) were the primary contributors based on permutation importance (e.g., Bio11 explained 56.4% for C. argentea), while altitude (e.g., 27.3% for A. palmeri) and UV-B (e.g., 16.2% for A. palmeri) exerted lower influence. Model validation confirmed high accuracy (mean area under the curve (AUC) > 0.86 and true skill statistic (TSS) > 0.6). By the 2090s, all species showed net habitat expansion overall, although D. ambrosioides exhibited net total contractions during mid-century under the SSP126/245 scenarios, C. argentea experienced reduced total suitability during the 2050s–2070s despite high-suitability growth, and A. palmeri and A. spinosus expanded significantly in both total and highly suitable habitat. All species shifted their distribution centroids northward, aligning with warming trends. Overall, these findings highlight the critical role of temperature in driving range dynamics and underscore the need for latitude-specific monitoring strategies to mitigate invasion risks, providing a scientific basis for adaptive management under global climate change. Full article

This study assesses habitat suitability and identifies conservation priority areas for the endangered Marco Polo sheep throughout Khunjerab National Park (Pakistan) and Tashkurgan Natural Reserve (China). We analyzed species occurrence records against environmental variables (elevation, slope, climate, land cover) using MaxEnt modeling. Model performance was validated through AUC-ROC analysis and response curves, generating spatial predictions of suitable habitats to inform conservation strategies. Spatial predictions were generated to map potential distribution zones, aiding conservation planning for this endangered species. The model’s predictive performance was evaluated using the Area Under the Curve (AUC) of the Receiver Operating Characteristic curve, yielding an AUC of 0.919, indicating strong discriminatory capability. Elevation (43.9%), slope (25.9%), and September precipitation (15.9%) emerged as the most influential environmental predictors, collectively contributing 85.7% to the model. The total percentage contribution and permutation significance values were 98.6% and 77.8%, respectively. Jackknife analysis identified elevation (bio-1), slope (bio-7), hillshade (bio-2), and the maximum July temperature (bio-9) as the most significant factors influencing the distribution of Marco Polo sheep, Conversely, variables such as viewshade (bio-14), land cover (bio-3), and precipitation in August (bio-4) contributed a minimal gain, suggesting that they had little impact on accurately predicting species distribution. The habitat suitability map reveals varying conditions across the study area, with the highest suitability (yellow zones) found in the northern and western regions, particularly along the Wakhan Corridor ridgelines. The southern regions, including Khunjerab Pass, show predominantly low suitability, marked by purple zones, suggesting poor habitat conditions. The eastern region displays moderate to low suitability, with fragmented patches of green and yellow, indicating seasonal habitats. The survival of transboundary Marco Polo sheep remains at risk due to poaching activities and habitat destruction and border fence barriers. This study recommends scientific approaches to habitat restoration together with improved China–Pakistan cooperation in order to establish sustainable migratory patterns for this iconic species. Full article

The selection of environmental variables with different spatial resolutions is a critical factor affecting the accuracy of machine learning-based fishery forecasting. In this study, spring-season survey data of Decapterus macarellus in the South China Sea from 2016 to 2024 were used to construct six machine learning models—decision tree (DT), extra trees (ETs), K-Nearest Neighbors (KNN), light gradient boosting machine (LGBM), random forest (RF), and extreme gradient boosting (XGB)—based on seven environmental variables (e.g., sea surface temperature (SST), chlorophyll-a concentration (CHL)) at four spatial resolutions (0.083°, 0.25°, 0.5°, and 1°), filtered using Pearson correlation analysis. Optimal models were selected under each resolution through performance comparison. SHapley Additive exPlanations (SHAP) values were employed to interpret the contribution of environmental predictors, and the maximum entropy (MaxEnt) model was used to perform habitat suitability mapping. Results showed that the XGB model at 0.083° resolution achieved the best performance, with the area under the receiver operating characteristic curve (ROC_AUC) = 0.836, accuracy = 0.793, and negative predictive value = 0.862, outperforming models at coarser resolutions. CHL was identified as the most influential variable, showing high importance in both the SHAP distribution and the cumulative area under the curve contribution. Predicted suitable habitats were mainly located in the northern and central-southern South China Sea, with the latter covering a broader area. This study is the first to systematically evaluate the impact of spatial resolution on environmental variable selection in machine learning models, integrating SHAP-based interpretability with MaxEnt modeling to achieve reliable habitat suitability prediction, offering valuable insights for fishery forecasting in the South China Sea. Full article

Desert plants in arid regions are facing escalating challenges from global warming, underscoring the urgent need to predict shifts in the distribution and habitats of dominant species under future climate scenarios. This study employed the Maximum Entropy (MaxEnt) model to project changes in the potential suitable habitats of three keystone desert species in Xinjiang—Halostachys capsica (M. Bieb.) C. A. Mey (Caryophyllales: Amaranthaceae), Haloxylon ammodendron (C. A. Mey.) Bunge (Caryophyllales: Amaranthaceae), and Karelinia caspia (Pall.) Less (Asterales: Asteraceae)—under varying climatic conditions. The area under the Receiver Operating Characteristic curve (AUC) exceeded 0.9 for all three species training datasets, indicating high predictive accuracy. Currently, Halos. caspica predominantly occupies mid-to-low elevation alluvial plains along the Tarim Basin and Tianshan Mountains, with a suitable area of 145.88 × 10⁴ km², while Halox. ammodendrum is primarily distributed across the Junggar Basin, Tarim Basin, and mid-elevation alluvial plains and aeolian landforms at the convergence zones of the Altai, Tianshan, and Kunlun Mountains, covering 109.55 × 10⁴ km². K. caspia thrives in mid-to-low elevation alluvial plains and low-elevation alluvial fans in the Tarim Basin, western Taklamakan Desert, and Junggar–Tianshan transition regions, with a suitable area of 95.75 × 10⁴ km². Among the key bioclimatic drivers, annual mean temperature was the most critical factor for Halos. caspica, precipitation of the coldest quarter for Halox. ammodendrum, and precipitation of the wettest month for K. caspia. Future projections revealed that under climate warming and increased humidity, suitable habitats for Halos. caspica would expand in all of the 2050s scenarios but decline by the 2070s, whereas Halox. ammodendrum habitats would decrease consistently across all scenarios over the next 40 years. In contrast, the suitable habitat area of K. caspia would remain nearly stable. These projections provide critical insights for formulating climate adaptation strategies to enhance soil–water conservation and sustainable desertification control in Xinjiang. Full article

Beetles of the family Mordellidae, important global pollinators, include Glipa, the third largest genus, which retains plesiomorphic traits related to pollination and is mainly found between 38° S–38° N. Existing studies on Glipa focus largely on taxonomy and systematics. The ecological response of Glipa to climate change remains poorly understood. Our objective was to investigate how the distribution of Glipa may respond to climate change using a species-level MaxEnt based model with 297 geographic distribution data points and seven bioclimatic environmental variables. The study showed that the MaxEnt model had a high predictive accuracy, with an Area Under the Curve (AUC) value of 0.963. The maximum temperature of the warmest month, mean annual precipitation, and mean precipitation of the driest quarter were the three most important factors affecting the distribution of Glipa. Currently, the suitable distribution areas of Glipa are mainly located in East Asia, Southeast Asia, eastern North America, South America, and central and western Africa. Under future climate scenarios, the area of suitable habitat is expected to increase gradually as global temperatures rise. Under the SSP585 scenario in the 2070s, the suitable habitat area is projected to expand by 53.89% compared to the present. Additionally, the centroid of suitable habitat is expected to shift northward. This study not only deepens the understanding of the distribution patterns of Glipa and their response to climate change but also provides important scientific evidence for the conservation of pollinator diversity. Full article

This study, based on the vessel position data of pump-suction beam trawlers and the integrated species distribution model (ISDM), deeply analyzes the spatio-temporal distribution characteristics of the habitat of Antarctic krill and the contributions of key environmental factors. The Convolutional Neural Network–attention model (CNN–attention model) was used to identify the fishing status of the vessel position data of Norwegian pump-suction beam trawlers for Antarctic krill during the fishing seasons from 2021 to 2023. Variables of marine environment, including sea surface temperature (SST), sea surface height (SSH), chlorophyll concentration (CHL), sea ice concentration (SIC), sea surface salinity (SSS), and spatial factor Geographical Offshore Linear Distance (GLD) were combined and input into the ISDM for simulating and predicting the spatial distribution of the habitat. The model results show that the Area Under the Curve (AUC) and True Skill Statistic (TSS) indices for all months exceed 0.9, with an average AUC of 0.997 and a TSS of 0.973, indicating extremely high accuracy of the model in habitat prediction. Further analysis of environmental factors reveals that Geographical Offshore Linear Distance (GLD) and chlorophyll concentration (CHL) are the main factors affecting habitat suitability, contributing 34.9% and 25.2%, respectively, and their combined contribution exceeds 60%. In addition, factors such as sea surface height (SSH), sea surface temperature (SST), sea ice concentration (SIC), and sea surface salinity (SSS) have impacts on the habitat distribution to varying degrees, and each factor exhibits different suitability response characteristics in different seasons and sub-regions. There is no significant correlation between the habitat area of Antarctic krill and catch (p > 0.05), while there is a significant positive correlation between the fishing duration and the catch (p < 0.001), indicating that a longer fishing duration can effectively increase the Antarctic krill catch. Full article

Anoplophora horsfieldii Hope, a potential pest of the Cerambycidae family, is widely distributed throughout China, where it can cause damage to various living tree species. It has emerged as a critical invasive organism threatening China’s agricultural and forestry production as well as ecological security. This study comprehensively analyzed the key environmental factors influencing the geographical distribution of A. horsfieldii and its spatiotemporal dynamics by integrating multi-source environmental data and employing ecological niche modeling. Model validation demonstrated high reliability and accuracy of our predictions, with an area under the receiver operating characteristic curve (AUC) value of 0.933, Kappa coefficient of 0.704, and true skill statistic (TSS) reaching 0.960. Our analysis identified four dominant environmental factors governing the distribution of A. horsfieldii: mean diurnal range (Bio2), temperature annual range (Bio7), precipitation of driest quarter (Bio17), and precipitation of coldest quarter (Bio19). Under current climatic conditions, the total potential suitable distribution area for A. horsfieldii was estimated at 212.394 × 10⁴ km², primarily located in central, southern, eastern, southwestern, and northwestern China. Future projections under three climate scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5) suggest significant reductions in highly and moderately suitable habitats, while low-suitability areas may expand into central, eastern, and southwestern regions, with Chongqing, Henan, and Anhui potentially becoming new suitable habitats. Concurrently, the centroid coordinates of suitable habitats exhibited a directional shift toward Guangdong Province, with the overall distribution pattern demonstrating a spatial transition characterized by movement from inland to coastal areas and from higher to lower latitudes. This study provides scientific theoretical support for forestry authorities in controlling the spread of A. horsfieldii, while establishing a solid foundation for future ecological conservation and biosecurity strategies. The findings offer both theoretical insights and practical guidance for pest management and ecosystem protection. Full article

Rapid climate change has significantly impacted species distribution patterns, necessitating a comprehensive understanding of dominant tree dynamics for effective forest resource management and utilization. The Camellia subgenus Camellia, a widely distributed taxon in subtropical China, represents an ecologically and economically important group of woody plants valued for both oil production and ornamental purposes. In this study, we employed the BIOMOD2 ensemble modeling framework to investigate the spatial distribution patterns and range dynamics of the subgenus Camellia under projected climate change scenarios. Our analysis incorporated 1455 georeferenced occurrence records from 15 species, following the filtering of duplicate points, along with seven bioclimatic variables selected after highly correlated factors were eliminated. The ensemble model, which integrates six single species distribution models, demonstrated robust predictive performance, with mean true skil l statistic (TSS) and area under curve (AUC) values exceeding 0.8. Our results identified precipitation of the coldest quarter (Bio19) and temperature seasonality (Bio4) as the primary determinants influencing species distribution patterns. The center of species richness for the subgenus Camellia was located in the Nanling Mountains and eastern Guangxi Zhuang Autonomous Region. The projections indicate an overall expansion of suitable habitats for the subgenus under future climate conditions, with notable scenario-dependent variations: distribution hotspots are predicted to increase by 8.86% under the SSP126 scenario but experience a 2.53% reduction under the SSP585 scenario. Furthermore, a westward shift in the distribution centroid is anticipated. To ensure long-term conservation of Camellia genetic resources, we recommend establishing a germplasm conservation center in the Nanling Mountains region, which represents a critical biodiversity hotspot for this taxon. Full article

Prunus microcarpa is an endemic species prevalent throughout the highlands of the Kurdistan Region of Iraq. Conservation, introduction, and restoration efforts require an in-depth understanding of the species’ current and future habitat distributions under different climate change scenarios. This study utilized field observations, species distribution modeling, geospatial techniques, and environmental predictors to analyze the distribution and forecast potential habitats for P. microcarpa in the highlands of Iraq. Findings indicate that, according to the global climate models (i.e., BCC-CSM2-MR and MRI-ESM2.0), the reduction in habitat for the species is projected to be more than the potential expansion. Specifically, the area of habitat is expected to reduce by 2351.908 km² (4.6%) and 2216.957 km² (4.3%), while it could increase by 1306.384 km² (2.5%) and 1015.612 km² (2.0%) for the respective climate models. Topographic features such as elevation and slope, climatic conditions, precipitation seasonality, and annual mean temperature relatively shape the distribution of P. microcarpa. The modeling demonstrated good predictive capability (area under the curve (AUC) score = 0.933). The total study area is approximately 51,558.327 km², with around 20.5% (10,602 km²) identified as suitable habitat for P. microcarpa. These findings offer essential baseline information for conservation strategies and provide new insights into where the species currently resides and where it could be found in the future. This underscores how combining distribution modeling with geospatial techniques can be effective, particularly in data-deficient regions like Iraq. Full article

As climate change continues to alter species distributions, Pinus bungeana, an endangered conifer of significant ecological and ornamental value, faces heightened vulnerability, underscoring the critical need to understand and predict its future habitat shifts. Here, we used 83 effective geographic distribution records, along with climate, topography, soil, and drought indices, to simulate the potential distribution of suitable ecological niches for P. bungeana under current conditions and across three future time periods (2040–2060, 2060–2080, and 2080–2100) under two shared socioeconomic pathways: SSP126 (low emissions) and SSP585 (high emissions), using the maximum entropy (MaxEnt) model. The results show that the area under the receiver operating characteristic curve (AUC) for all simulations exceeded 0.973, indicating high predictive accuracy. Soil moisture, the minimum temperature of the coldest month, temperature seasonality, isothermality, the precipitation of the wettest quarter, and altitude were identified as key environmental factors limiting the distribution of P. bungeana, with soil moisture and the minimum temperature of the coldest month being the most important factors. Under the current climatic conditions, the potentially suitable ecological niches for P. bungeana were primarily located in Shaanxi Province, southern Shanxi Province, southeastern Gansu Province, northeastern Sichuan Province, Henan Province, and northwestern Hubei Province, covering approximately 75.59 × 10⁴ km². However, under the future climate scenarios, highly suitable areas were projected to contract, with the rate of decline varying significantly between scenarios. Despite this, the total area of potentially suitable ecological niches was predicted to expand in the future periods. Additionally, a pronounced eastward shift in P. bungeana’s distribution was projected, especially under the high-emission SSP585 scenario. These findings provide insights into the potential impacts of climate change on the distribution of P. bungeana, and they offer valuable guidance for its conservation strategies and habitat management in the context of climate change. Full article