Search Results (241)

Wetland mapping plays a crucial role in monitoring wetland ecosystems, water resource management, and habitat suitability assessment. Wetland classification remains significantly challenging due to the diverse types, intricate spatial patterns, and highly dynamic nature. This study proposed a dynamic hybrid method that integrated feature selection and object-oriented ensemble model construction to improve wetland mapping using Sentinel-1 and Sentinel-2 data. The proposed feature selection approach integrates the ReliefF and recursive feature elimination (RFE) algorithms with a feature evaluation criterion based on Shapley additive explanations (SHAP) values, aiming to optimize the feature set composed of various variables. During the construction of ensemble models (i.e., RF, XGBoost, and LightGBM) with features selected by RFE, hyperparameter tuning is subsequently conducted using Bayesian optimization (BO), ensuring that the selected optimal features and hyperparameters significantly enhance the accuracy and performance of the classifiers. The accuracy assessment demonstrates that the BO-LightGBM model with ReliefF-RFE-SHAP-selected features achieves superior performance to the RF and XGBoost models, achieving the highest overall accuracy of 89.4% and a kappa coefficient of 0.875. The object-oriented classification maps accurately depict the spatial distribution patterns of different wetland types. Furthermore, SHAP values offer global and local interpretations of the model to better understand the contribution of various features to wetland classification. The proposed dynamic hybrid method offers an effective tool for wetland mapping and contributes to wetland environmental monitoring and management. Full article

Human activities have accelerated the extinction of species, driving biodiversity loss and ecosystem degradation. Establishing protected areas (PAs) that encompass habitats of endangered species is essential for achieving biodiversity conservation and ecosystem protection goals. This study aimed to identify and prioritize critical conservation areas for the endangered long-tailed goral (Naemorhedus caudatus) in five regions of Gangwon and Gyeongbuk Provinces, South Korea. The MaxEnt model was applied to predict the potential habitat of the species, considering key environmental factors such as topographic, distance-related, vegetation, and land cover variables. The InVEST Habitat Risk Assessment (HRA) model was used to quantitatively assess cumulative risks within the habitat from the impacts of forest development and anthropogenic pressures. Subsequently, the Zonation software was employed for spatial prioritization by integrating the outputs of the models, and core conservation areas (CCAs) with high ecological value were identified through overlap analysis with 1st-grade areas from the Ecological and Nature Map (ENM). Results indicated that suitable habitats for the long-tailed goral were mainly located in forested regions, and areas subjected to multiple stressors faced elevated habitat risk. High-priority areas (HPAs) were primarily forested zones with high habitat suitability. The overlap analysis emphasized the need to implement conservation measures targeting CCAs while also managing additional HPAs outside CCAs, which are not designated as ENM. This study provides a methodological framework and baseline data to support systematic conservation planning for the long-tailed goral, offering practical guidance for future research and policy development. Full article

Conserving the threatened West Himalayan endemic T. contorta (Taxaceae) is critical due to extinction risks from skewed male- or female-only populations. This study employs ChatGPT-driven artificial intelligence (AI) analysis for textual synthesis and preliminary hypothesis generation to identify favorable propagation sites for T. contorta within the Swat district of Pakistan. Over three years (2019–2021), eleven male- or female-only populations of T. contorta were surveyed. Environmental data from NASA POWER were analyzed using ChatGPT 3.5 to predict suitable propagation sites, which were then mapped in Google Earth Pro. PCA and hierarchical clustering were applied to identify key environmental variables. Out of 63 generated points, 58 were accurately located in Swat with 92% geographic accuracy, while species-specific general knowledge accuracy was 100%. All points fell within the pre-established T. contorta spatial range in Pakistan, with 21 unique sites meeting optimal conditions. Field surveys confirmed 16 new populations. These findings underscore the promising role of AI-driven analysis in conservation planning by identifying and supporting habitat restoration efforts. A bidirectional integration of AI and SDM, combined with remote sensing technologies, represents a novel approach for the effective conservation of endangered plant species. Full article

Bumblebees are among the most important wild pollinators; however, their populations are declining worldwide due to factors such as climate change, habitat loss, and pesticide use. For their conservation, it is important to understand the community structure at the local scale and the drivers responsible for their assemblages. However, little is known about bumblebee community assemblages and their drivers in Yunnan Province, China. In this study, we mapped bumblebee community assemblages across 125 counties in Yunnan Province using field-collected and published data. We also quantified the climatic and land use/land cover (LULC) drivers shaping these assemblages. The climatic habitat suitability for 21 bumblebee species was assessed at the county level across Yunnan using species distribution modeling. The biogeographic zones (groups of counties) were identified using Ward’s agglomerative cluster analysis, and the impacts of 12 bioclimatic and LULC drivers on the zonation pattern were assessed using Canonical Correspondence Analysis (CCA). Results indicated that more than 70% of bumblebee species showed their highest environmental suitability in the northern region of Yunnan. Among climatic factors, temperature-related bioclimatic variables were identified as dominant drivers influencing the spatial distribution of 15 out of 21 bumblebee species within the counties of Yunnan. In contrast, five species, B. grahami, B. impetuosus, B. lepidus, B. picipies, and B. securus, showed the highest contribution from precipitation-related factors. Six biogeographic zones (I, II, III, IV, V, and VI) were identified using Ward’s agglomerative cluster analysis. All 12 drivers were found to play critical roles in shaping the community assemblages of bumblebee species. This study provides essential insights for devising targeted conservation strategies at a local scale to maintain bumblebee populations in Yunnan. Full article

Cattle fever ticks, Rhipicephalus microplus and Rhipicephalus annulatus, transmit Babesia pathogens, the causative agents of cattle fever worldwide. Although eradicated from the United States, increasing incursions of cattle fever ticks in Texas have put considerable strain on the Cattle Fever Tick Eradication Program (CFTEP). The movement of ticks between wildlife and cattle along the Texas–Mexico border complicates control efforts. Here, we used habitat suitability models, the literature, and quantitative survey data to project the distributions of native and introduced ungulates in Texas. Specifically, we used habitat suitability models and downscaling to estimate potential overlap between cattle and free-ranging white-tailed deer (Odocoileus virginianus) and nilgai (Boselaphus tragocamelus) that may carry cattle fever ticks and generate maps of estimated tick exposure risk. Our findings suggest that the introduction and spread of exotic ungulates, such as the nilgai antelope, may facilitate the expansion of cattle fever ticks within and beyond the historical quarantine zone established in 1943. The increasing range of nilgai populations could enhance landscape connectivity for cattle fever ticks in sensitive areas along the Texas–Mexico border. By combining these models with cattle inventory data, we provide tools to help the CFTEP better allocate resources, monitor tick populations, prevent incursions, and implement early interventions. Full article

Hippophae neurocarpa S. W. Liu & T. N. Ho exhibits established medicinal characteristics, valuable dietary attributes, and remarkable adaptability, displaying strong resistance to cold, drought, and to acidic and alkaline soils. These traits and others make it a valuable species for soil erosion control and a distinctive economic forest tree in western China. However, research on its geographic distribution remains limited. To address this gap, we employed the MaxEnt model to map its current distribution and to predict the future geographic distribution of suitable habitats for this species under SSP1-2.6, SSP2-4.5, and SSP5-8.5 climate scenarios. Collectively, these data suggest that the species’ current and future suitable habitats are predominantly concentrated at the junction of the northeastern Qinghai-Tibet Plateau and the Loess Plateau. Under present climatic conditions, highly suitable habitats are primarily located in the northeastern Qinghai-Tibet Plateau, with smaller patches in the Hengduan and Himalaya mountains. The AUC value of this model reached 0.954; projections under three future emission scenarios indicate an overall expansion trend in suitable habitat area. Notably, by the 2070s under the SSP2-4.5 scenario, the total suitable habitat area is projected to increase by 11.64%—the highest among all scenarios. Additionally, climate change is expected to drive a slight northward shift in the species’ distribution center toward higher latitudes. Key environmental factors influencing its projected distribution include elevation (elev), temperature seasonality (bio04), mean temperature of the coldest quarter (bio11), and precipitation of the warmest quarter (bio18). These insights are critical for conserving H. neurocarpa’s genetic resources and guiding future biodiversity conservation strategies. Full article

The Chinese sturgeon (Acipenser sinensis), a critically endangered anadromous fish species, spends over 90% of its life cycle in marine habitats, yet research on its marine ecology and habitat requirements is limited due to sparse data. To address this, we integrated satellite remote sensing with ecological modeling to assess spatiotemporal dynamics in marine habitat suitability across China’s continental shelf (2003–2020). Nine key habitat factors were derived from multi-source remote sensing data and inverted transparency algorithms. Species occurrence data were coupled with the Maximum Entropy (MaxEnt) model to evaluate habitat preferences and seasonal shifts. Results revealed distinct environmental preferences: shallow depths (≤20 m), sea surface and bottom temperature (10–30 °C and 10–25 °C), salinity (10–35‰), transparency (0.40–3.00 m), eastward and northward seawater velocity (−0.20–0.15 m/s and −0.20–0.20 m/s), moderate productivity (1000–3000 mg/m²), and zooplankton carbon (0.20–6.00 g/m²). Habitat factor importance varied seasonally—salinity, depth, and net primary productivity dominated in spring; bottom temperature and productivity in summer/autumn; salinity and transparency in winter. Spatially, high-suitability areas peaked in autumn (70% total suitable habitat), concentrating near the Yangtze Estuary, northern Jiangsu coast, and Zhoushan Archipelago. This study emphasizes the need to prioritize these areas for protection and inform proliferation and release schemes for Chinese sturgeon. It also demonstrates the efficacy of remote sensing for mapping essential habitats of migratory megafauna in complex coastal ecosystems and provides actionable insights for targeted conservation strategies. Full article

Diospyros kaki Thunb. ‘Taishuu’ is novel fruit cultivar known for its excellent mouthfeel properties and high economic value. This study aimed to identify the ecological adaptability and potential suitable cultivating regions of this persimmon in China. In addition, key ecological factors influencing fruit mouthfeel were also investigated. Differences between key metabolites and mouthfeel properties of 35 persimmon samples from 13 provinces were compared. Subsequently, ecological factors were evaluated to explore interactions among dominant ecological factors, habitat suitability, and fruit quality. An adaptive segmentation map was ultimately created to highlight variations in mouthfeel properties of the persimmon. The findings were summarized as follows: The core ecological suitability zones encompass most warm, temperate and typically subtropical regions of China, spanning 116,200 square kilometers. Habitat suitability influences fruit size but does not affect mouthfeel properties. Key factors affecting mouthfeel properties of D. kaki ‘Taishuu’ include precipitation during the growing period, high temperature during the fruit ripening stage, and low temperatures during dormancy. Persimmons from coastal areas and Yunnan province were characterized by a lusciously sweeter and richer taste, a satisfying crisp texture, and an overall distinctly superior mouthfeel. In contrast, samples from central cultivation areas exhibited higher density, greater firmness, reduced crispness, and inferior flavor quality Based on zoning results, extensive regions show significant potential for high-quality production, making them highly promising for D. kaki ‘Taishuu’ cultivation. For marginally suitable habitats, appropriate cultivation measures should be implemented to mitigate limiting factors such as temperature and soil moisture. Full article

Habitat fragmentation restricts the movement of large mammals across broad landscapes, leading to isolation of individuals or groups, reduced interaction with other species, and limited access to vital resources in surrounding habitats. In this study, we aimed to determine the wildlife ecological corridors for five large mammals (Ursus arctos, Cervus elaphus, Capreolus capreolus, Sus scrofa, and Canis lupus) between Kastamonu Ilgaz Mountain Wildlife Refuge and Gavurdağı Wildlife Refuge. In the field studies, we used the transect, indirect observation, and camera-trap methods to collect presence data. Maximum Entropy (MaxEnt) (v. 3.4.1) software was used to create habitat suitability models of the target species, which are based on the presence-only data approach. The results indicated that AUC values varied between 0.808 and 0.835, with water sources, stand type, and slope contributing most significantly to model performance. In order to determine wildlife ecological corridors, resistance surface maps were created using the species distribution models (SDMs), and bottleneck areas were determined. The Circuit Theory approach was used to model the connections between ecological corridors. As a result of this study, we developed connectivity models for five large mammals based on Circuit Theory, identified priority wildlife ecological corridors, and evaluated critical connection points between two protected areas, Ilgaz Mountain Wildlife Refuge and Gavurdağı Wildlife Refuge. These findings highlight the essential role of ecological corridors in sustaining landscape-level connectivity and supporting the long-term conservation of wide-ranging species. Full article

Abies nephrolepis and Picea jezoensis are native Pinaceae trees distributed in high mountainous regions of Northeast Asia (typically above ~1000 m a.s.l. on the Korean peninsula, northeastern China, Sakhalin, and the Russian Far East) and southern boreal forests, vulnerable to climate change and human disturbances, necessitating accurate habitat identification for effective conservation. While protected areas (PAs) are essential, merely expanding existing ones often fail to protect populations under human pressure and climate change. Using species distribution models with current and projected climate data, we mapped potential habitats across Northeast Asia. Spatial clustering analyses integrated with PA and land cover data helped identify optimal sites and priorities for new conservation areas. Ensemble species distribution models indicated extensive suitable habitats, especially in southern Sikhote-Alin, influenced by maritime-continental climates. Specific climate variables strongly affected habitat suitability for both species. The Kamchatka peninsula consistently emerged as an optimal habitat under future climate scenarios. Our study highlights essential environmental characteristics shaping the habitats of these species, reinforcing the importance of strategically enhancing existing PAs, and establishing new ones. These insights inform proactive conservation strategies for current and future challenges, by focusing on climate refugia and future habitat stability. Full article

(1) Background: As a prominent zoonotic parasitic disease, fascioliasis threatens the sustainable development of animal husbandry and public health. Current research focuses mainly on individual species (parasite or intermediate host), neglecting systematic evaluation of the transmission chain and exposure risks to animal husbandry. Thus, comprehensive studies are urgently needed, especially in the ecologically fragile alpine region of the Qinghai-Tibet Plateau; (2) Methods: Distribution data of Radix spp. and Fasciola hepatica in the Qinghai-Tibet Plateau and adjacent areas were gathered to establish a potential distribution model, which was overlaid on a map of livestock farming in the region; (3) Results: The key environmental factors influencing Radix spp. distribution were temperature seasonality (21.4%), elevation (16.4%), and mean temperature of the driest quarter (14.7%). For F. hepatica, the main factors were elevation (41.3%), human footprint index (30.5%), and Precipitation of the driest month (12.1%), with all AUC values exceeding 0.9. Both species exhibited extensive suitable habitats in Qinghai and Tibet, with higher F. hepatica transmission risk in Qinghai than Tibet; (4) Conclusions: The significant transmission risk and its impacts on the livestock industry in the Qinghai-Tibet Plateau highlight the need for proactive prevention and control measures. This study provides a scientific foundation for targeted alpine diseases control, establishes an interdisciplinary risk assessment framework, fills gaps in high-altitude eco-epidemiology, and offers insights for ecological conservation of the plateau. 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 elongated tortoise (Indotestudo elongata), a Critically Endangered (CR) species, faces numerous threats across its range. Yet, the ecological and anthropogenic factors affecting its conservation in fragmented habitats remain poorly understood. This study integrated field surveys and community questionnaires to assess the distribution drivers and local perceptions, such as attitudes, knowledge, conservation practices, and perceived threats, in the Jalthal Forest, one of the last remnants of suitable habitat for the elongated tortoise in eastern Nepal. Using ArcMap, we established 138 randomly selected grids (500 m × 500 m) to evaluate the environmental covariates of tortoise occurrence and anthropogenic pressures. Generalized linear models revealed that tortoise occurrence was negatively associated with dense ground cover (β = −3.50, p = 0.017) and human disturbance (β = −8.11, p = 0.019). Surveys of local residents from community forest user groups (n = 236 respondents) indicated strong local support for tortoise conservation (69% willing to protect the species). Despite this, the respondents identified persistent threats, including hunting for bushmeat and traditional medicine (74%), habitat degradation (65%), and forest fires. While 60% of the respondents recognized the threatened species status, significant knowledge gaps regarding that status and ongoing illegal exploitation persisted. These findings underscore the need for targeted habitat management, reduced anthropogenic pressures, and community-led initiatives to align local attitudes with conservation actions. This study provides critical baseline data for conserving the elongated tortoise in human-modified landscapes and emphasizes the necessity of integrated ecological and socio-cultural strategies for its long-term survival. 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

Lethocerus patruelis (Stål, 1854) is a large aquatic hemipteran and the only European representative of the family Belostomatidae. Commonly known as the giant water bug, this species was historically restricted to the Balkans, Anatolia, and parts of the Middle East, but has exhibited a marked westward and northward range expansion in recent decades. In this study, we report the first confirmed occurrence of L. patruelis on Lesvos Island, in the northeastern Aegean Sea, based on a direct observation made within a wastewater treatment facility. The individual was identified in situ using diagnostic morphological traits and photographed without disturbance. This finding extends the known insular distribution of the species and underscores its capacity to exploit anthropogenically modified aquatic systems. Given the island’s rich mosaic of natural and artificial wetland habitats—including over 200 mapped sites—Lesvos may offer suitable conditions for the establishment of local populations. This record highlights the need for targeted surveys and long-term monitoring across under-sampled insular landscapes. Full article