Search Results (190)

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

As economically important sympatric species in the Northwest Pacific, the Japanese sardine (Sardinops melanostictus) and Chub mackerel (Scomber japonicus) exhibit significant biological interactions. Understanding the impact of interspecies competition on their habitat dynamics can provide crucial insights for the sustainable development and management of these interconnected species resources. This study utilizes fisheries data of S. melanostictus and S. japonicus from the Northwest Pacific, collected from June to November between 2017 and 2020. We integrated various environmental parameters, including temperature at different depths (0, 50, 100, 150, and 200 m), eddy kinetic energy (EKE), sea surface height (SSH), chlorophyll-a concentration (Chl-a), and the oceanic Niño index (ONI), to construct interspecific competition species distribution model (icSDM) for both species. We validated these models by overlaying the predicted habitats with fisheries data from 2021 and performing cross-validation to assess the models’ reliability. Furthermore, we conducted correlation analyses of the habitats of these two species to evaluate the impact of interspecies relationships on their habitat dynamics. The results indicate that, compared to single-species habitat models, the interspecific competition species distribution model (icSDM) for these two species exhibit a significantly higher explanatory power, with R² values increasing by up to 0.29; interspecific competition significantly influences the habitat dynamics of S. melanostictus and S. japonicus, strengthening the correlation between their habitat changes. This relationship exhibits a positive correlation at specific stages, with the highest correlations observed in June, July, and October, at 0.81, 0.80, and 0.88, respectively; interspecific competition also demonstrates stage-specific differences in its impact on the habitat dynamics of S. melanostictus and S. japonicus, with the most pronounced differences occurring in August and November. Compared to S. melanostictus, interspecific competition is more beneficial for the expansion of the optimal habitat (HIS ≥ 0.6) for S. japonicus and, to some extent, inhibits the habitat expansion of S. melanostictus. The variation in migratory routes and predatory interactions (with larger individuals of S. japonicus preying on smaller individuals of S. melanostictus) likely constitutes the primary factors contributing to these observed differences. Full article

Dutch elm disease is one of the most devastating plant diseases, primarily spread through bark beetles. Scolytus scolytus is a key vector of this disease. In this study, distribution data of S. scolytus were collected and filtered. Combined with environmental and climatic variables, an ensemble model was developed using the Biomod2 platform to predict its potential geographical distribution in China. The selection of climate variables was critical for accurate prediction. Eight bioclimatic factors with high importance were selected from 19 candidate variables. Among these, the three most important factors are the minimum temperature of the coldest month (bio6), precipitation seasonality (bio15), and precipitation in the driest quarter (bio17). Under current climate conditions, suitable habitats for S. scolytus are mainly located in the temperate regions between 30° and 60° N latitude. These include parts of Europe, East Asia, eastern and northwestern North America, and southern and northeastern South America. In China, the low-suitability area was estimated at 37,883.39 km², and the medium-suitability area at 251.14 km². No high-suitability regions were identified. However, low-suitability zones were widespread across multiple provinces. Under future climate scenarios, low-suitability areas are still projected across China. Medium-suitability areas are expected to increase under SSP370 and SSP585, particularly along the eastern coastal regions, peaking between 2041 and 2060. High-suitability zones may also emerge under these two scenarios, again concentrated in coastal areas. These findings provide a theoretical basis for entry quarantine measures and early warning systems aimed at controlling the spread of S. scolytus in China. Full article

Climatic oscillations in the Quaternary are altering the performance of angiosperms, while the species’ distribution is regarded as a macroscopic view of these spatial and temporal changes. Oaks (Quercus L.) are important tree models for estimating the abiotic impacts on the distribution of forest tree species. In this study, we modeled the past, present, and future suitable habitat for two varieties of deciduous oaks (Quercus serrata and Quercus serrata var. brevipetiolata), which are widely distributed in China and play dominant roles in the local forest ecosystem. We evaluated the importance of environmental factors in shaping the species’ distribution and identified the “wealthy” habitats in harsh conditions for the two varieties. The ecological niche models showed that the suitable areas for these two varieties are mainly concentrated in mountain ranges in central China, while Q. serrata var. brevipetiolata is also widely distributed in the middle-east mountain range. The mean temperature of the coldest quarter was identified as the critical factor in shaping the habitat availability for these two varieties. From the last glacial maximum (LGM) to the present, the potential distribution range of these two sibling species has obviously shifted northward and expanded from the inferred refugia. Under the optimistic (RCP2.6), moderate (RCP 4.5)-, and higher (RCP 6.0)-concentration greenhouse gas emissions scenarios, our simulations suggested that the total area of suitable habitats in the 2050s and 2070s will be wider than it is now for these two varieties of deciduous oaks, as the distribution range is shifting to higher latitudes; thus, low latitudes are more likely to face the risk of habitat losses. This study provides a case study on the response of forest tree species to climate changes in the north temperate and subtropical zones of East Asia and offers a basis for tree species’ protection and management in China. Full article

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

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

The small yellow croaker (Larimichthys polyactis), a migratory estuarine-demersal fish critical to East Asian fisheries, has faced severe population declines because of anthropogenic pressures (e.g., overfishing and anthropogenic habitat modification) and shifting environmental conditions. This study investigates its spatio-temporal habitat dynamics in Hangzhou Bay (2017–2023) using fisheries surveys and species distribution models (SDMs), with insights applicable to Pacific Coast migratory fish conservation. We evaluated the performance of eleven modeling algorithms to identify the most accurate model for predicting small yellow croaker distributions. Our results showed that the random forest algorithm outperformed other models, with a high sensitivity (95.238) and specificity (99.49), demonstrating its ability to capture complex non-linear relationships between environmental factors and species distribution. Depth emerged as the most influential factor, accounting for 30% of the importance in the model, with small yellow croakers preferring deeper waters around 60 m. Salinity was the second most important factor, with higher occurrence probabilities in areas where salinity exceeded 25 PSU. Other environmental factors, such as temperature and dissolved oxygen, had relatively smaller impacts on distribution. Spatially, small yellow croakers were predominantly distributed in offshore regions east of 122.5° E, where deeper waters and higher salinity levels provided suitable habitat conditions. This study underscores the need for targeted management measures, such as habitat restoration, to ensure the sustainable management of small-bodied yellow croaker populations. Full article

Accurate information on species distributions and population sizes is essential for effective biodiversity conservation, yet such data are often lacking at national scales. This study addresses this gap by assessing the distribution and abundance of 111 bird species across Croatia, including breeding, wintering, and migratory flyway populations. We combined Species Distribution Models (SDMs) with expert-based population estimates to generate spatially explicit predictions. The modeling framework incorporated high-resolution Earth observation (EO) data and advanced spatial analysis techniques. Environmental variables, such as land cover, were derived from satellite datasets, while climate variables were interpolated from ground measurements and refined using EO-based co-variates. Model calibration and validation were based on species occurrence records and EO-derived predictors. This integrative approach enabled both national-scale population estimates and fine-scale habitat assessments. The results identified critical habitats, population hotspots, and areas likely to experience distribution shifts under changing environmental conditions. By integrating EO data with expert knowledge, this study enhances the robustness of population estimates, particularly where species monitoring data are incomplete. The findings support conservation prioritization, inform land use and resource management, and contribute to long-term biodiversity monitoring. The methodology is scalable and transferable, offering a practical framework for ecological assessments in diverse regions. We integrated expert-based population estimates with species distribution models (SDMs) by applying expert-derived density values to areas of suitable habitat predicted by SDMs. This approach enables spatially explicit population estimates by combining ecological modeling with expert knowledge, which is particularly useful in systems with limited data. Experts provided species-specific density estimates stratified by habitat type, seasonality, behavior, and detectability, aligned with habitat suitability classes derived from SDM outputs. Full article

Climate change poses a significant threat to the persistence of rear-edge populations, which are located at the margins of a species’ distribution range and are particularly vulnerable to environmental shifts. This study focuses on Yew (Taxus baccata L.) in the Iberian Peninsula, representing the southernmost extent of its range, where warming temperatures and decreasing moisture may compromise its survival. Our research aims to assess the climate sensitivity and habitat variability of Yew, addressing the hypothesis that future climate scenarios will significantly reduce the species’ climatic suitability, particularly in southern and low-altitude regions, and that this reduction will negatively impact individual growth performance. We used species distribution models (SDMs) based on ecological niche modeling (ENM) to project the current and future distribution of suitable habitats for Yew under two climate scenarios (SSP126 and SSP585). The models were calibrated using bioclimatic variables, and the resulting suitability maps were integrated with field data on individual growth performance, measured as basal area increment over the last five years (BAI5). The ensemble model showed high predictive performance, highlighting precipitation seasonality and annual mean temperature as the most influential variables explaining the climatic suitability distribution in the Iberian Peninsula. Our results indicate a substantial reduction in suitable habitats for Yew, especially under the high-emission scenario (SSP585), with southern populations experiencing the greatest losses. Furthermore, individual growth was positively correlated with climatic suitability, confirming that populations in favorable habitats exhibit better performance. These findings highlight the vulnerability of rear-edge populations of Yew to climate change and underscore the need for targeted conservation strategies, including the identification of climatic refugia and the potential use of assisted migration. Full article

Sterculia foetida L., commonly known as the Java olive, is a tropical tree species native to regions of East Africa, tropical Asia, and northern Australia. This study employs species distribution modeling (SDM) to predict the potential geographic distribution of S. foetida under current and future climate scenarios. Using 1425 occurrence data and 19 environmental variables, we applied an ensemble modelling approach of three algorithms: Boosting Regression Trees (BRT), Generalized Linear Model (GLM), and Random Forests (RF), to generate distribution maps. Our models showed high accuracy (mean AUC = 0.98) to indicate that S. foetida has a broad ecological niche, with high suitability in tropical and subtropical regions of north Australia (New Guinea and Papua), Southeast Asia (India, Thailand, Myanmar, Taiwan, Philippines, Malaysia, Sri Lanka), Oman and Yemen in the southwest of Asia, Central Africa (Guinea, Ghana, Nigeria, Congo, Kenya and Tanzania), the Greater and Lesser Antilles, Mesoamerica, and the north of South America (Colombia, Panama, Venezuela, Ecuador and Brazil). Indeed, the probability of occurrence of S. foetida positively correlates with the Maximum temperature of warmest month (bio5), Mean temperature of wettest quarter (bio8) and Precipitation of wettest month (bio13). The model results showed a suitability area of 4,744,653 km², representing 37.86% of the total study area, classified into Low (14.12%), Moderate (8.71%), and High suitability (15.02%). Furthermore, the study found that habitat suitability for S. foetida showed similar trends under both near future climate scenarios (SSP1-2.6 and SSP5-8.5 for 2041–2060), with a slight loss in potential distribution (0.24% and 0.25%, respectively) and moderate gains (1.98% and 2.12%). In the far future (2061–2080), the low scenario (SSP1-2.6) indicated a 0.29% loss and a 2.52% gain, while the high scenario (SSP5-8.5) showed a more dramatic increase in both loss (0.6%) and gain areas (3.79%). These findings are crucial for conservation planning and management, particularly in regions where S. foetida is considered invasive and could become problematic. The study underscores the importance of incorporating climate change projections in SDM to better understand species invasiveness dynamics and inform biodiversity conservation strategies. Full article

The import of ornamental plants has become a major source of alien invasive plants in China, posing threats to local ecosystems. However, research on their invasive potential and management strategies remains limited. This study evaluated the invasion risks of nine representative introduced ornamental plants (including naturalized and invasive species) in China (IOPCs). Using ecospat to perform climatic niche comparisons, we found significant unfilling and expansion (>50%) in most introduced ornamental plants, indicating strong invasiveness. Species distribution models (SDMs) were applied to predict the current and future distributions of these IOPCs under four shared socioeconomic pathways (SSPs: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) across four time periods (2021–2040, 2041–2060, 2061–2080, and 2081–2100). The SDM results showed that the current high-risk areas are concentrated in southern China. Under future climate change, moderate- and high-risk zones are projected to shift northward, with the total areas increasing significantly, namely moderate-risk areas by 106.10% and high-risk areas by 64.35%, particularly in the border regions of Jiangxi, Fujian, and Zhejiang. We recommend establishing restricted introduction lists for non-native ornamental plants, enhancing monitoring and management in high-risk regions, and implementing early eradication measures. This study quantified the invasion risks and potential distributions of representative invasive ornamental plants, providing a scientific basis for effective control strategies. Full article

This study examines the distribution dynamics of the tribe Erythroneurini, a group of economically significant leafhoppers in China that pose threats to crops through sap feeding and virus transmission, while also serving as valuable ecological indicators due to their sensitivity to environmental changes. Through a systematic evaluation of 12 species distribution models (SDMs), we ultimately selected the Maximum Entropy (MaxEnt) model for predicting species distributions. The R-optimized MaxEnt model incorporated 11 environmental variables and 218 occurrence records to assess habitat suitability under historical, current, and future climate scenarios (SSP1-2.6 and SSP5-8.5). The model was configured with LQP features and a default regularization multiplier value of 1. Results reveal that temperature (BIO6, BIO2, BIO4) and precipitation (BIO12) are the primary drivers of habitat suitability, with tropical and subtropical regions identified as the most favorable. Future projections indicate a complex pattern of habitat contraction and expansion, with a notable northward shift toward higher latitudes under climate change. These findings highlight the profound impact of climate change on Erythroneurini distribution, underscoring the need for proactive management. Implementing long-term monitoring and targeted control in vulnerable regions mitigates ecological and agricultural risks, supporting sustainable pest management and fostering the integration of ecological conservation with agricultural development. Full article

The endemic and critically endangered gharial, Gavialis gangeticus, experienced a severe population decline in its range. However, conservation efforts, notably through the implementation of “Project Crocodile” in India, have led to a significant recovery of its population. The present study employs an ensemble Species Distribution Model (SDM) to delineate suitable habitats for G. gangeticus under current and future climatic scenarios to understand the impact of climate change. The model estimates that 46.85% of the area of occupancy is suitable under the present scenario, with this suitable area projected to increase by 145.16% in future climatic conditions. States such as Madhya Pradesh, Uttar Pradesh, and Assam are projected to experience an increase in habitat suitability, whereas Odisha and Rajasthan are anticipated to face declines. The study recommends conducting ground-truthing ecological assessments using advanced technologies and genetic analyses to validate the viability of newly identified habitats in the Lower Ganges, Mahanadi, and Brahmaputra River systems. These areas should be prioritized within the Protected Area network for potential translocation sites allocation. Collaborative efforts between the IUCN-SSC Crocodile Specialist Group and stakeholders are vital for prioritizing conservation and implementing site-specific interventions to protect the highly threatened gharial population in the wild. Full article

Climate change has presented considerable challenges in the management of urban forests and trees. Varieties of studies have predicted the potential changes in species distribution by employing single-algorithm species distribution models (SDMs) to investigate the impacts of climate change on plant species. However, there is still limited quantitative research on the impacts of climate change on the suitable distribution ranges of commonly used urban tree species. Therefore, our study aims to optimize traditional SDMs by integrating multiple machine learning algorithms and to propose a framework for identifying suitable distribution ranges of urban trees under climate change. We took Michelia chapensis, a tree species of particular significance in southern China, as a pilot tree species to investigate the evolution of its suitable distribution range in the context of two future climate scenarios (SSP126 and SSP585) across four periods (2030s, 2050s, 2070s, and 2090s). The findings indicated that the ensemble SDM showed strong predictive capacity, with an area under the curve (AUC) value of 0.95. The suitable area for Michelia chapensis is estimated at 15.9 × 10⁵ km² currently and it will expand in most areas under future climate scenarios according to the projection. However, it will contract in southeastern Yunnan, central Guangdong, the Sichuan Basin, northern Hubei, and Jiangxi, etc. The central location of the current suitable distribution area is located in Hengyang, Hunan (27.36° N, 112.34° E), and is projected to shift westward with climate change in the future. The migration magnitude is positively correlated with the intensity of climate change. These findings provide a scientific basis for the future landscape planning and management of Michelia chapensis. Furthermore, the proposed framework can be seen as a valuable tool for predicting the suitable distribution ranges of urban trees in response to climate change, providing insights for proactive adaptation to climate change in urban planning and landscape management. Full article