Search Results (282)

Understanding the geographic distribution of mammal species is essential for informed conservation planning, maintaining local ecosystem stability, and addressing research gaps, particularly in data-deficient regions. This study investigated the distribution and richness of 20 mammal species within Nyerere National Park (NNP), a large and understudied protected area in Southern Tanzania. We applied species distribution models (SDMs) using presence data collected through ground surveys between 2022 and 2024, combined with environmental variables derived from remote sensing, including land surface temperature, vegetation indices, soil moisture, elevation, and proximity to water sources and human infrastructure. Models were constructed using the Maximum Entropy (MaxEnt) algorithm, and performance was evaluated using the Area Under the Curve (AUC) metric, yielding high accuracy ranging from 0.81 to 0.97. Temperature (32.3%) and vegetation indices (23.4%) emerged as the most influential predictors of species distributions, followed by elevation (21.7%) and proximity to water (14.5%). Species richness, estimated using a stacked SDM approach, was highest in the northern and riparian zones of the park, identifying potential biodiversity hotspots. This study presents the first fine-scale SDMs for mammal species in Nyerere National Park, offering a valuable ecological baseline to support conservation planning and promote sustainable ecotourism development in Tanzania’s southern protected areas. Full article

This study aimed to predict the potential habitats of Luciola unmunsana using a species distribution model (SDM). Luciola unmunsana is an endemic species that lives only in South Korea, and because its females do not have genus wings and are less fluid, it is difficult to collect, so research related to its distribution and restoration is relatively understudied. Therefore, this study predicted the potential habitats of Luciola unmunsana across South Korea using the single model Maximum Entropy (MaxEnt) and a multi-model ensemble model to prepare basic data necessary for a conservation and habitat restoration plan for the species. A total of 39 points of occurrence were built based on public data and prior research from the Jeonbuk Green Environment Support Center (JGESC), the Global Biodiversity Information Facility (GBIF), and the National Institute of Biological Resources (NIBR). Among the input variables, climate variables were based on the shared socioeconomic pathway (SSP) scenario-based ecological climate index, while nonclimate variables were based on topography, land cover maps, and the Enhanced Vegetation Index (EVI). The main findings of this study are summarized below. First, in predicting Luciola unmunsana potential habitats, the EVI, water network analysis, land cover, and annual precipitation (Bio12) were identified as good predictors in both models. Accordingly, areas with high vegetation activity in their forests, adjacent to water resources, and stable humidity were predicted as potential habitats. Second, by overlaying the predicted potential habitats and highly significant variables, we found that areas with high vegetation vigor within their forests, proximity to water systems, and relatively high annual precipitation, which can maintain stable humidity, are potential habitats for Luciola unmunsana. Third, literature surveys used to predict potential habitat sites, including Geumsan-gun, Chungcheongnam-do, Yeongam-gun, Jeollabuk-do, Mudeungsan Mountain, Gwangju-si, Korea, and Gijang-gun, Busan-si, Korea, confirmed the occurrence of Luciola unmunsana. This study is significant in that it is the first to develop a regional SDM for Luciola unmunsana, whose population is declining due to urbanization. In addition, by applying various environmental variables that reflect ecological characteristics, it contributes to more accurate predictions of the potential habitats of this species. The predicted results can be used as basic data for the future conservation of Luciola unmunsana and the establishment of habitat restoration strategies. Full article

To ensure food security in Xinjiang, scientifically conducting land suitability evaluation is of significant importance. This paper takes an arid and ecologically fragile region of southern Xinjiang—Qiemu County—as an example. Based on the optimized Maximum Entropy (MaxEnt) model, 14 multi-source environmental variables including climate, soil, hydrology, and topography are integrated. The ENMeval package is used to optimize the model parameters, and Spearman’s rank correlation analysis is employed to screen key variables. The spatial distribution of land suitability and the dominant factors are systematically assessed. The results show that the model AUC values for the mountainous and plain areas are 0.987 and 0.940, respectively, indicating high accuracy. In the plain area, land suitability is primarily influenced by the soil sand content, while in the mountainous region, the annual accumulated temperature plays a leading role. The highly suitable areas are mainly distributed in the northern plains and parts of the southern mountains. This study clarifies the suitable areas for land development and environmental thresholds, providing a scientific basis for the development of land resources in arid regions and the implementation of the “store grain in the land” strategy. Full article

The impact of climate change on the distribution of Cinnamomum malabatrum (Laurales—Lauraceae), a culturally and ecologically important species in the Malabar region of Western Ghats, India, was studied using a MaxEnt machine learning algorithm. The findings are rooted in extensive field data and advanced modeling techniques. The predicted range shifts and contraction of suitable habitats for the species indicate significant challenges ahead, especially in the Malabar midlands and coastal plains—areas of high endemicity. The proposed conservation strategies provide a comprehensive framework that encompasses the protection of sacred groves, sustainable land-use policies, afforestation, and community conservation strategies within protected areas. This study serves as a clarion call for concerted action and collaboration among researchers, policymakers, local communities, and conservation practitioners to preserve the delicate balance of the ecosystem in the face of environmental change. Full article

A key challenge is how to effectively conserve habitats and biodiversity amid widespread habitat fragmentation and loss caused by global urbanization. Despite growing attention to this issue, knowledge of the seasonal dynamics of habitats remains limited, and conservation gaps are still inadequately identified. This study proposes a novel integrated framework, “Habitat Suitability–Risk–Quality”, to improve the assessment of the seasonal bird habitat quality and to identify priority conservation habitats in urban landscapes. The framework was implemented in Wuhan, China, a critical stopover site along the East Asian–Australasian Flyway. It combines the Maximum Entropy (MaxEnt) model to predict the seasonal habitat suitability, the Habitat Risk Assessment (HRA) model to quantify habitat sensitivity to multiple anthropogenic threats, and a refined Habitat Quality (HQ) model to evaluate the seasonal habitat quality. K-means clustering was then applied to group habitats based on seasonal quality dynamics, enabling the identification of priority areas and the development of differentiated conservation strategies. The results show significant seasonal variation in habitat suitability and quality. Wetlands provided the highest-quality habitats in autumn and winter, grasslands exhibited moderate seasonal quality, and forests showed the least seasonal fluctuation. The spatial analysis revealed that high-quality wetland habitats form an ecological belt along the urban–suburban fringe. Four habitat clusters with distinct seasonal characteristics were then identified. However, spatial mismatches were found between existing protected areas and habitats of high ecological value. Notably, Cluster 1 maintained high habitat quality year round, spanning 99.38 km², yet only 46.51% of its area is currently protected. The remaining 53.16 km², mostly situated in urban–suburban transitional zones, remain unprotected. This study provides valuable insights for identifying priority habitats and developing season-specific conservation strategies in rapidly urbanizing regions, thereby supporting the sustainable management of urban biodiversity and the development of resilient ecological systems. Full article

Ulmus glabra Huds. is a mesophilic, montane broadleaf tree with high ecological value, commonly found in temperate riparian and floodplain forests across Türkiye. Its populations in Türkiye have declined due to anthropogenic disturbances and climatic pressures that cause habitat fragmentation and threaten the species’ long-term survival. In this research, we used Maximum Entropy (MaxEnt) to build species distribution models (SDMs) and applied the Restoration Planner (RP) tool to identify and prioritize critical restoration sites under both current and projected climate scenarios (SSP245, SSP370, SSP585). The SDMs highlighted areas of high suitability, primarily along the Black Sea coast. Future projections show that habitat fragmentation and shifts in suitable areas are expected to worsen. To systematically compare restoration options across different future scenarios, we derived and applied four spatial network status indicators using the RP tool. Specifically, we calculated Restoration Pixels (REST_PIX), Average Distance of Restoration Pixels from the Network (AVDIST_RP), Change in Equivalent Connected Area (ΔECA), and Restoration Efficiency (EFFIC) using the RP tool. For the 1 <-> 2 restoration pathways, the highest efficiency (EFFIC = 38.17) was recorded under present climate conditions. However, the largest improvement in connectivity (ΔECA = 60,775.62) was found in the 4 <-> 5 pathway under the SSP585 scenario, though this required substantial restoration effort (REST_PIX = 385). Temporal analysis noted that the restoration action will have most effectiveness between 2040 and 2080, while between 2081 and 2100, increased habitat fragmentation can severely undermine ecological connectivity. The result indicates that incorporation of habitat suitability modeling into restoration planning can help to design cost-effective restoration actions for degraded land. Moreover, the approach used herein provides a reproducible framework for the enhancement of species sustainability and habitat connectivity under varying climate conditions. Full article

Chengiodendron marginatum, an evergreen tree or shrub belonging to the Oleaceae family, represents a critical germplasm resource with considerable potential for novel cultivar breeding. To elucidate the adaptive responses of C. marginatum to climate change and provide strategic guidance for its conservation, this study investigates the changing patterns in its potential suitable habitats under various climate scenarios. We employed an integrated approach combining maximum entropy (Maxent) modeling with GIS spatial analysis, utilizing current occurrence records and paleoclimatic data spanning from the mid-Holocene to future projections (2041–2060 [2050s] and 2061–2080 [2070s]). Climate scenarios SSP126 and SSP585 were selected to represent contrasting emission pathways. The model demonstrated excellent predictive accuracy with an AUC value of 0.942, identifying precipitation-related variables (particularly the precipitation of driest month and annual precipitation) as the primary environmental factors shaping the geographical distribution of C. marginatum. Current suitable habitats encompass approximately 98.38 × 10⁴ km², primarily located in East, Central, and South China, with high-suitability habitats restricted to southern Hainan, Taiwan, and northeastern Guangxi. Since the mid-Holocene, an expansion of suitable habitats occurred despite localized contractions in Southwest China. Future projections revealed moderate habitat reduction under both scenarios, and high-suitability areas decreased substantially. Importantly, under both scenarios, persistent high-suitability habitats were maintained in southern Hainan, Taiwan, and northeastern Guangxi, which are identified as essential climate refugia for the species. These findings provide a basis for understanding the response of the species to climate change and offer valuable guidance for its conservation. Full article

Tamarix boveana Bunge demonstrates strong drought and salinity tolerance, exhibiting significant economic potential and ecological functions. With global warming profoundly altering plant distribution patterns, this study aims to identify key factors influencing its distribution and predict shifts in habitat suitability under future climate scenarios. This study employed the maximum entropy (MaxEnt) model with 186 presences and 36 environmental variables. Results reveal that the current suitable habitat of Tamarix boveana is primarily concentrated along the southern Mediterranean coast and partial western coastal areas, with highly suitable zones comprising 14% of the total suitable range. Dominant environmental factors governing its distribution include isothermality (bio3), annual mean temperature (bio1), soil pH (t_pH_h₂o), and precipitation of the warmest quarter (bio18). Projections under varying carbon emission scenarios indicate a contraction in suitable habitat area, accompanied by pronounced poleward range shifts and habitat fragmentation, particularly under high-emission pathways. This study provides a scientific foundation for the conservation and management of Tamarix boveana, while contributing to climate change impact assessments and biodiversity preservation. 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

The increasing global demand for food, combined with rising climate extremes, is driving agricultural expansion—often without sufficient consideration for sustainability. Greenhouse agriculture presents a promising solution to address the dual challenges of food security and climate change mitigation. This study models potential scenarios for the expansion of greenhouse agriculture in Imbabura Province, Ecuador, while adhering to sustainability criteria. Two widely used methods were compared: the Analytical Hierarchy Process (AHP) integrated with Geographic Information Systems (GIS) and the Maximum Entropy (MaxEnt) model. The GIS-AHP method relies on expert-defined weights, whereas the MaxEnt model utilizes the probabilistic distribution of presence-only data, enabling a complementary evaluation of both subjective and data-driven approaches. Both models incorporated various factors, including topographic, climatic, hydrological, ecological, infrastructural, agricultural, and soil-related variables. The results classified the territory into five levels of suitability for greenhouse expansion. The GIS-AHP model identified 20,761.64 hectares as highly suitable, while the MaxEnt model identified only 5618.15 hectares. This discrepancy highlights the differing influences of various factors: In the GIS-AHP, land cover/use, irrigation availability, and proximity to existing greenhouses were the most influential. In contrast, in the MaxEnt model, proximity to greenhouses was the dominant factor. These findings not only provide a spatially explicit foundation for sustainable territorial planning but also contribute methodologically by integrating both data-driven and expert-driven approaches. This supports evidence-based policy-making in fragile Andean ecosystems. 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

The common leopard (Panthera pardus) in Azad Jammu and Kashmir (AJ and K), Pakistan, is increasingly threatened by habitat fragmentation and climate change. This study employs a dual-model approach, integrating Maximum Entropy (MaxEnt) and Random Forest algorithms with multi-source remote sensing data to evaluate leopard habitat suitability. Our analysis identifies land cover (LC), fractional vegetation cover (FVC), elevation, temperature seasonality (bio4), and distance to roads (Dist_road) as the most influential habitat predictors. Leopards exhibit a strong preference for mixed forests at elevations between 1000 and 3000 m, with a suitability index of 0.83. The study identifies several unsuitable conditions including: road proximity (<0.08 km), low elevation zones (<1000 m), areas with high temperature seasonality (bio4 > 8 °C), and non-forested land cover types. MaxEnt demonstrated superior habitat prediction accuracy over Random Forest (AUC = 0.912 vs. 0.827). The results highlight a distinct north-to-south suitability gradient, with optimal habitats concentrated in the northern districts (Muzaffarabad, Hattian, Neelum, Bagh, Haveli, Poonch, Sudhnutti) and declining suitability in human-dominated southern areas. Based on these findings, this study underscores the urgency of targeted conservation efforts in the northern districts of AJ and K, where optimal leopard habitats are identified. The findings emphasize the need for habitat connectivity and protection measures to mitigate the impacts of habitat fragmentation and climate change. Future conservation strategies should prioritize the preservation of mixed forests and the establishment of buffer zones around roads to ensure the long-term survival of the common leopard in this region. Full article

The Grapholita dimorpha is one of the significant borer pests that primarily damage plum, pear, and apple trees, often resulting in substantial economic losses in fruit production. However, the potential distribution range of this economically important pest remains poorly understood. In this study, we simulated an optimized maximum entropy (MaxEnt) model to predict the spatiotemporal distribution pattern of G. dimorpha and identified its underlying driving factors. The results indicate that suitable habitats, under current bioclimatic conditions, are mainly distributed in eastern China, northeastern China, Korea, and Japan, covering a total of 273.5 × 10⁴ km². The highly suitable habitats are primarily located in Korea and parts of central Japan, with a total area of 19.8 × 10⁴ km². In future projections, the suitable area is expected to increase by 17.74% to 62.10%, and the suitable habitats are predicted to shift northward overall. In particular, there are more highly suitable habitats for G. dimorpha in China and Japan compared to their predominance in Korea under current climatic conditions. The bio9 and bio18 contribute 51.9% and 20.7% to the modeling, respectively, indicating that the distribution of G. dimorpha may be shaped mainly by the mean temperature of the driest quarter and precipitation of the warmest quarter. In summary, the distribution range predicted, particularly for regions with highly suitable habitats, poses a high risk of G. dimorpha outbreaks, emphasizing the priority of pest monitoring and management. Furthermore, the key bioclimatic variables identified could also provide crucial reference for pest monitoring. Full article

As a vital bridge linking human well-being with ecological processes, cultural ecosystem services (CESs) play a pivotal role in understanding the equilibrium of social–ecological systems. However, the spatial supply–demand relationships of CESs remain underexplored in rapidly urbanizing regions. This study establishes an integrated framework by synthesizing multi-source geospatial data, socioeconomic indicators, and the Maximum Entropy (MaxEnt) model to investigate the spatial dynamics of CESs in Taiyuan City. Key findings include the following: (1) A pronounced spatial heterogeneity in CES supply distribution, exhibiting a core-to-periphery diminishing gradient, with inverse correlations observed among different CES categories. (2) Accessibility, topographic features, and fractional vegetation cover emerged as primary drivers of spatial supply differentiation, while climatic factors and elevation exerted non-negligible influences on this Loess Plateau urban system. (3) Four spatial mismatch patterns were identified through the supply–demand analysis: high supply–high demand (38.1%), low supply–low demand (37.2%), low supply–high demand (13.6%), and high supply–low demand (10.9%). The coupling coordination degree of CESs in Taiyuan City indicated moderate coordination, with severe imbalances observed in urban–rural transitional zones. This study reveals nonlinear interactions between natural landscapes and anthropogenic factors in shaping CES spatial distributions, particularly the trade-offs between esthetic value and transportation constraints. By integrating big data and spatial modeling, this research advances CES quantification methodologies and provides actionable insights for optimizing green infrastructure, prioritizing ecological restoration, and balancing urban–rural CES provision. These outcomes address methodological gaps in coupled social–ecological system research while informing practical spatial governance strategies. Full article

Cupressus funebris forests grow relatively fast and have a strong natural regeneration ability, showing great potential in carbon sequestration. Global warming has already had a significant impact on its distribution pattern. This study used the Maximum Entropy Model (MaxEnt) and the distribution data of Cupressus funebris communities to explore the contraction and expansion of the adaptive distribution of Cupressus funebris. The research results are as follows: The contemporary adaptive distribution area of Cupressus funebris is mainly located in the southern region of China, and the area of the adaptive distribution accounts for approximately 7.15% of the total land area. The main driving variables affecting the distribution of Cupressus funebris are annual precipitation, the minimum temperature of the coldest month, isothermality, temperature seasonality, carbonate content, and altitude. Among them, climate plays a dominant role in the distribution of this community. Under different carbon emission scenarios in the future, the adaptive distribution areas show an expansion trend, but most of the highly adaptive areas are shrinking and the changes are relatively significant. In the high emission pathway, the distribution area continues to expand in the north while gradually contracting in the southern regions. The community distribution shows a trend of migrating to higher latitudes and altitudes in northern regions, and there are significant non-linear characteristics in altitude migration under the scenario of intensified carbon emissions. This study provides theoretical guidance for the protection and management of Cupressus funebris forests and helps to improve the carbon sequestration capacity of the communities in the context of carbon neutrality. Full article