Search Results (564)

Understanding the impacts of climate change on species’ geographic distributions is fundamental for biodiversity conservation and resource management. As a key plant group for ecological restoration and windbreak and sand fixation in arid and semi-arid ares in China’s Three Northern Regions (Northeast, North, and Northwest China), Caragana spp. exhibit distribution patterns whose regulatory mechanisms by environmental factors remain unclear, with a long-term lack of climatic explanations influencing their spatial distribution. This study integrated 2373 occurrence records of 44 Caragana species in China’s Three Northern Regions with four major environmental variable categories. Using the Biomod2 ensemble model, current and future climate scenario-based suitable habitats for Caragana spp. were predicted. This study innovatively combined quantitative analyses with Kira’s thermal indexes (warmth index, coldness index) and Wenduo Xu’s humidity index (HI) to elucidate species-specific relationships between distribution patterns and hydrothermal climatic constraints. The main results showed that (1) compared to other environmental factors, climate is the key factor affecting the distribution of Caragana spp. (2) The current distribution centroid of Caragana spp. is located in Alxa Left Banner, Inner Mongolia. In future scenarios, the majority of centroids will shift toward lower latitudes. (3) The suitable habitats for Caragana spp. will expand overall under future climate scenarios. High-stress scenarios exhibit greater spatial changes than low-stress scenarios. (4) Hydrothermal requirements varied significantly among species in China’s Three Northern Regions, and 44 Caragana species can be classified into five distinct types based on warmth index (WI) and humidity index (HI). The research findings will provide critical practical guidance for ecological initiatives such as the Three-North Shelterbelt Program and the restoration and management of degraded ecosystems in arid and semi-arid regions under global climate change. Full article

The Qinghai–Tibetan Plateau (QTP), often referred to as the “Third Pole” of the world, harbors alpine grassland ecosystems that play an essential role as global carbon sinks, helping to mitigate the pace of climate change. Nonetheless, alterations in natural environmental conditions coupled with escalating human activities have disrupted the seasonal growth cycles of grasslands, thereby intensifying degradation processes. To date, the key drivers and lifecycle dynamics of Grassland Depletion across the QTP remain contentious, limiting our comprehension of its ecological repercussions and regulatory mechanisms. This study comprehensively investigates grassland degradation on the Qinghai–Tibetan Plateau, analyzing its drivers and changes in ecological suitability during the growing season. By integrating natural factors (e.g., precipitation and temperature) and anthropogenic influences (e.g., population density and grazing intensity), it examines observational data from over 160 monitoring stations collected between the 1980s and 2020. The findings reveal three distinct phases of grassland degradation: an acute degradation phase in 1990 (GDI, Grassland Degradation Index = 2.53), a partial recovery phase from 1996 to 2005 (GDI < 2.0) during which the proportion of degraded grassland decreased from 71.85% in 1990 to 51.22% in 2005, and a renewed intensification of degradation after 2006 (GDI > 2.0), with degraded grassland areas reaching 56.39% by 2020. Among the influencing variables, precipitation emerged as the most significant driver, interacting closely with anthropogenic factors such as grazing practices and population distribution. Specifically, the combined impacts of precipitation with population density, grazing pressure, and elevation were particularly notable, yielding interaction q-values of 0.796, 0.767, and 0.752, respectively. Our findings reveal that while grasslands exhibit superior carbon sink potential relative to forests, their productivity and ecological functionality are undergoing considerable declines due to the compounded effects of multiple interacting factors. Consequently, the spatial distribution of ecologically suitable zones has contracted significantly, with the remaining high-suitability regions concentrating in the “twin-star” zones of Baingoin and Zanda grasslands, areas recognized as focal points for future ecosystem preservation. Furthermore, the effects of climate change and intensifying anthropogenic activity have driven the reduction in highly suitable grassland areas, shrinking from 41,232 km² in 1990 to 24,485 km² by 2020, with projections indicating a further decrease to only 2844 km² by 2060. This study sheds light on the intricate mechanisms behind Grassland Depletion, providing essential guidance for conservation efforts and ecological restoration on the QTP. Moreover, it offers theoretical underpinnings to support China’s carbon neutrality and peak carbon emission goals. Full article

Wildfires are becoming an increasingly severe threat to European forests, driven by climate change, land use changes, and socio-economic factors. Integrated solutions for wildfire prevention, early detection, emergency management, and ecological restoration are urgently needed to enhance forest resilience. The Horizon 2020 SILVANUS project developed a comprehensive multi-sectoral platform combining technological innovation, stakeholder engagement, and sustainable forest management strategies. This report analyses the Slovak Republic’s participation in SILVANUS, applying a seven-criterion fit–gap framework (governance, legal, interoperability, staff capacity, ecological suitability, financial feasibility, and stakeholder acceptance) to evaluate the platform’s alignment with national conditions. Notable contributions include stakeholder-supported functional requirements for wildfire prevention, climate-sensitive forest models for long-term adaptation planning, IoT- and UAV-based early fire detection technologies, and decision support systems (DSS) for emergency response and forest-restoration activities. The Slovak pilot sites, particularly in the Podpoľanie region, served as important testbeds for the validation of these tools under real-world conditions. All SILVANUS modules scored ≥12/14 in the fit–gap assessment; early deployment reduced high-risk fuel polygons by 23%, increased stand-level structural diversity by 12%, and raised the national Sustainable Forest Management index by four points. Integrating SILVANUS outcomes into national forestry practices would enable better wildfire risk assessment, improved resilience planning, and more effective public engagement in wildfire management. Opportunities for adoption include capacity-building initiatives, technological deployments in fire-prone areas, and the incorporation of DSS outputs into strategic forest planning. Potential challenges, such as technological investment costs, inter-agency coordination, and public acceptance, are also discussed. Overall, the Slovak Republic’s engagement with SILVANUS demonstrates the value of participatory, technology-driven approaches to sustainable wildfire management and offers a replicable model for other European regions facing similar challenges. Full article

Andean grassland ecosystems in Peru are characterized by diverse plant species adapted to environmental factors including weather, soil type, elevation, slope orientation, and soil moisture. This study evaluated the floristic composition, alpha diversity, indicator species, and suitable species for Andean livestock in the Sillapata micro-watershed, Junín region, Peru, across rainy and dry seasons. Data collection involved 100 m linear transects, and analyses included floristic composition and dissimilarity, Shannon-Wiener (H′) and Simpson (D) diversity indices, and the identification of indicator and suitable species using QGIS vs 3.28.14 and R software vs 4.3.2. Results revealed a total of 130 species classified into 74 genera and 23 families, with Asteraceae and Poaceae as the dominant families, exhibiting variations in richness and dissimilarity between control points and seasonal periods. Alpha diversity (H′) ranged from 2.07 to 3.1867, while Simpson’s index (D) ranged from 0.7644 to 0.9234. Six indicator species were identified, along with 11 families containing suitable species, predominantly Poaceae (38–60%), Cyperaceae (11–15%), and Asteraceae (3–9%). The findings indicate that the studied ecosystem exhibits a heterogeneous floristic composition with medium to low and variable diversity, influenced by seasonal climatic changes and the current grassland management regime, which involves rotational grazing with cattle adapted to high-altitude conditions. Full article

Solanum aculeatissimum is a herbaceous to semi-woody perennial plant native to the Brazilian ecosystem. It has naturalized extensively in southwestern China, posing significant threats to local biodiversity. This study systematically screened and integrated 100 distribution records from authoritative databases, including the Chinese Virtual Plant Specimen Database, the Global Biodiversity Information Facility, and Chinese Natural Museums. Additionally, 23 environmental variables were incorporated, comprising 19 bioclimatic factors from the World Climate Dataset, 3 topographic indicators, and the Human Footprint Index. The objectives of this research are as follows: (1) to simulate the plant’s current and future distribution (2050s/2070s) under CMIP6 scenarios (SSP1-2.6, SSP2-4.5, and SSP5-8.5); (2) to quantify changes in the distribution range; and (3) to determine the migration trajectory using MaxEnt 3.4.4 software. The findings reveal that human pressure (contributing 79.7%) and isothermality (bioclimatic factor 3: 10.1%) are the primary driving forces shaping its distribution. The core suitable habitats are predominantly concentrated in the provinces of Yunnan, Guizhou, and Sichuan. By 2070, the distribution center shifts northeastward to Qujing City. Under the SSP5-8.5 scenario, the invasion front extends into southern Tibet, while retreat occurs in the lowlands of Honghe Prefecture. This study underscores the synergistic effects of socioeconomic development pathways and bioclimatic thresholds on invasive species’ biogeographical patterns, providing a robust predictive framework for adaptive management strategies. Full article

Changing climatic conditions are significant determinants of agricultural productivity. Rio Grande do Sul is the southernmost state and the second-largest agricultural producer in Brazil. The suitability of its land for farming can be used as a proxy for agricultural and economic success, making it a pertinent case for exploring the consequences of climate change on major crop production. The latest available climate and environmental data was used to develop an agricultural Suitability Index (SI) that quantifies the suitability of land for rice, tobacco, soybean, and corn production in 2020 (present), 2050 (near-future), and 2100 (far-future) under moderate (SSP245) and extreme (SSP585) climate scenarios. SI scores for each municipality of Rio Grande do Sul consider inputs from a three-layer framework (climatic, non-climatic, and current production) to provide critical insight into potential shifts in agricultural productivity. While terrestrial suitability for crop growth varies both spatially and temporally, widespread decreases in suitability for all four crops are expected across the state under both scenarios. Soybean is expected to be the least affected crop, and rice is the most affected crop, tied to shifting patterns in precipitation, which significantly determines suitability. Local and state governments, agribusinesses, and family producers will have to adapt to environmental challenges to ensure the provision of food, labor, and economic security. Full article

Viticulture contributes significantly to Chile’s exports and GDP. However, the development and productivity of grapevines is threatened by climate change. Grapevines are grown in diverse regions; thus, adaptable tools for evaluating climate risk at the local level are required. In this study, a local climate risk index (LCRI) was developed to assess the vulnerability of Chilean viticulture (wine, table, and pisco grapes) in the current (2017–2024) and future (2046–2065) periods. Various components, including exposure, sensitivity, and adaptive and response capacities, were analyzed using different indicators based on municipal-level information. The results for the current period indicated that most municipalities were at medium risk, whereas future projections showed a marked increase in climate risk, principally due to changes in climate suitability. In the current period, the highest LCRI values were observed in semi-arid and mediterranean zones, particularly in the northern regions of Atacama and Coquimbo; in the future period, this situation intensified. In contrast, the lowest values in the current period occurred in the Maule region and further south, where the climate transitions from mediterranean to temperate conditions, and in the future period, valley and mountainous areas presented improvements in the index. Some municipalities showed improvement or stability with local adaptation efforts. The results highlight the urgent need for region-specific adaptation policies that prioritize water management, infrastructure, and increased capacities. Full article

The interaction between atmospheric moisture condensation (AMC) on leaf surfaces and vegetation health is an emerging area of research, particularly relevant for advancing our understanding of water–vegetation dynamics in the contexts of remote sensing and hydrology. AMC, particularly in the form of dew, plays a vital role in both hydrological and ecological processes. The presence of AMC on leaf surfaces serves as an indicator of leaf water potential and overall ecosystem health. However, the large-scale assessment of AMC on leaf surfaces remains limited. To address this gap, we propose a leaf area index (LAI)-derived condensation potential (LCP) index to estimate potential dew yield, thereby supporting more effective land management and resource allocation. Based on psychrometric principles, we apply the nocturnal condensation potential index (NCPI), using dew point depression (ΔT = T_a − T_d) and vapor pressure deficit derived from field meteorological data. Kriging interpolation is used to estimate the spatial and temporal variations in the AMC. For management applications, we develop a management suitability score (MSS) and prioritization (MSP) framework by integrating the NCPI and the LAI. The MSS values are classified into four MSP levels—High, Moderate–High, Moderate, and Low—using the Jenks natural breaks method, with thresholds of 0.15, 0.27, and 0.37. This classification reveals cases where favorable weather conditions coincide with low ecological potential (i.e., low MSS but high MSP), indicating areas that may require active management. Additionally, a pairwise correlation analysis shows that the MSS varies significantly across different LULC types but remains relatively stable across groundwater potential zones. This suggests that the MSS is more responsive to the vegetation and micrometeorological variability inherent in LULC, underscoring its unique value for informed land use management. Overall, this study demonstrates the added value of the LAI-derived AMC modeling for monitoring spatiotemporal micrometeorological and vegetation dynamics. The MSS and MSP framework provides a scalable, data-driven approach to adaptive land use prioritization, offering valuable insights into forest health improvement and ecological water management in the face of climate change. Full article

The article presents an innovative methodology supporting sustainable urban development through the strategic expansion of green infrastructure in Warsaw, based on the 3-30-300 concept. The proposed approach integrates a multi-criteria Fuzzy Analytic Hierarchy Process (F-AHP) with Geographic Information System (GIS) tools, enabling objective and precise identification of suitable locations for new parks of at least 1 hectare in size. The analysis considers five key factors: distance from populated areas, land cover and use, surface temperature, proximity to nuisance facilities, and an NDVI index value. The study results demonstrated a significant increase in green space accessibility across the city. In all districts of Warsaw, the number of residential buildings meeting the criterion of a maximum 300 m distance to a park or forest increased—from 2% in Rembertów to 32% in Wilanów. The districts of Ursynów and Wilanów exceeded the 30% green space coverage threshold, while Białołęka reached 29%. These results indicate the real potential to achieve the goals of the 3-30-300 concept, contributing simultaneously to sustainable urban development, improved quality of life, mitigation of the urban heat island effect, increased biodiversity, and enhanced climate change adaptation. Spatial limitations related to high-density development were also identified—many districts lack available space for large parks. A viable solution supporting balanced development may lie in implementing smaller green forms, such as green squares or micro-parks, particularly in areas of planned development. The proposed methodology serves as a practical tool to support land-use management and sustainable spatial planning, addressing contemporary environmental, social, and urban challenges. Full article

Studies show that Nature-Based Solutions can mitigate Urban Heat Island (UHI) effects by implementing green spaces. Green roofs (GRs) may minimize land surface temperature (LST) by modifying albedo. This research predicts, assesses, and measures the impact of reducing the LST by applying green roofs in buildings by using a Random Forest algorithm and different remote sensing methods. To this aim, the city of Granada, Spain, was used as a case study. The city is classified into different Local Climate Zones (LCZs) to determine the area available for retrofitting GRs in built-up areas. A total of 14 Surface Temperature Collection 2 Level-2 images were acquired through Landsat 8–9, while 14 images for spectral indices such as the Normalized Difference Vegetation Index (NDVI), the Normalized Difference Building Index (NDBI), and Proportion Vegetation (PV) were calculated from Sentinel-2 in dates coinciding or close to LST images. Additional factors were considered including the sky view factor (SVF) and water distance (WD). The results suggest that Granada has limited suitable areas for retrofitting GRs, and available areas can reduce LST with a moderate impact, at an average of 1.45 °C; however, vegetation plays an important role in decreasing LST. This study provides a methodological example to identify the benefits of implementing GRs in reducing LST in semi-arid cities and recommends a combination of strategies for LST mitigation. 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

Despite growing importance, agricultural landscapes face threats, like fragmentation, shrinkage, and degradation, due to climate change. Although remote sensing and GIS are widely used in monitoring croplands, integrating machine learning, remote sensing, GIS, and landscape metrics for the holistic management of this landscape remains underexplored. Thus, this study monitored crop patterns using random forest (94% accuracy), the role of geographical factors (such as elevation, aspect, slope, maximum and minimum temperature, rainfall, cation exchange capacity, NPK, soil pH, soil organic carbon, soil type, soil water content, proximity to drainage, proximity to market, proximity to road, population density, and profit per hectare production), diversity, combinations, and fragmentation using landscape metrics and a fragmentation index. Findings revealed that slope, rainfall, temperature, and profit per hectare production emerged as significant drivers in shaping crop patterns. However, anthropogenic drivers became deciding factors during spatial overlaps between crop suitability zones. Rice belts were the least fragmented and highly productive with a risk of monoculture. Croplands with a combination of soybean, black grams, and maize were highly fragmented, despite having high diversity with comparatively less production per field. These diverse fields were providing higher profits and low risks of crop failure due to the crop combinations. Equally, intercropping balanced the nutrient uptakes, making the practice sustainable. Thus, it can be suggested that productivity and diversity should be prioritized equally to achieve sustainable land use. The development of the PCA-weighted fragmentation index offers an efficient tool to measure fragmentation across similar agricultural regions, and the integrated approach provides a scalable framework for holistic management, sustainable land use planning, and precision agriculture. Full article

Forest age is a key factor in determining the carbon sequestration capacity and trends of forests. Based on the Google Earth Engine platform and using the topographically complex and climatically diverse Southeastern Tibet as the study area, we propose a new method for forest age estimation that integrates multi-source remote-sensing data with machine learning. The study employs the Continuous Degradation Detection (CODED) algorithm combined with spectral unmixing models and Normalized Difference Fraction Index (NDFI) time series analysis to update forest disturbance information and provide annual forest distribution, mapping young forest distribution. For undisturbed forests, we compared 12 machine-learning models and selected the Random Forest model for age prediction. The input variables include multiscale satellite spectral bands (Sentinel-2 MSI, Landsat series, PROBA-V, MOD09A1), vegetation parameter products (canopy height, productivity), data from the Global Ecosystem Dynamics Investigation (GEDI), multi-band SAR data (C/L), vegetation indices (e.g., NDVI, LAI, FPAR), and environmental factors (climate seasonality, topography). The results indicate that the forests in Southeastern Tibet are predominantly overmature (>120 years), accounting for 87% of the total forest cover, while mature (80–120 years), sub-mature (60–80 years), intermediate-aged (40–60 years), and young forests (< 40 years) represent relatively lower proportions at 9%, 1%, 2%, and 1%, respectively. Forest age exhibits a moderate positive correlation with stem biomass (r = 0.54) and leaf-area index (r = 0.53), but weakly negatively correlated with L-band radar backscatter (HV polarization, r = −0.18). Significant differences in reflectance among different age groups are observed in the 500–1000 nm spectral band, with 100 m resolution PROBA-V data being the most suitable for age prediction. The Random Forest model achieved an overall accuracy of 62% on the independent validation set, with canopy height, L-band radar data, and temperature seasonality being the most important predictors. Compared with 11 other machine-learning models, the Random Forest model demonstrated higher accuracy and stability in estimating forest age under complex terrain and cloudy conditions. This study provides an expandable technical framework for forest age estimation in complex terrain areas, which is of significant scientific and practical value for sustainable forest resource management and global forest resource monitoring. Full article

Viticulture represents an important agricultural sector in Oltenia, which is one of the Romanian regions most affected by temperature increases. The main purpose of the present study was to analyze the changes in climate suitability for grapevine and wine production against this climate context in the region. Two specific bioclimatic indices were applied, namely the bioclimatic index and the oenoclimate aptitude index, both reflecting the cumulated influence of temperature, actual sunshine duration, and precipitation amounts on the grapevine during the growing season (1 April–30 September). The indices were calculated as average values for the period 1961–2020. In order to emphasize potential shifts in suitability, the mean, maximum, and minimum values were calculated for two distinct periods, 1961–1990 and 1991–2020. The results of the analysis underlined three distinct suitability changes: the area suitable for quality red wines shifting northwards (on average, about 30′ of latitude or 55.5 km), including the eastern part of the Getic Subcarpathians, which is not currently part of any winegrowing region; the emerging new areas suitable for quality white wine (the western part of the Subcarpathians); and a potentially overly hot climate developing in Southern Oltenia where grapevine varieties are currently grown. Thus, the development of adequate adaptation strategies for viticulture to climate change in the region should be considered in the near future. Full article

Climate change imposes significant challenges on viticulture, especially in subtropical regions, where thermal and water stresses impact vine physiology and yield. This study evaluated the effects of two rootstocks (‘IAC 766 Campinas’ and ‘106-8 Mgt’) and two training systems (low and high trellis) on the photosynthesis, biochemical parameters, and productivity of Vitis labrusca (‘Bordô’ and ‘Isabel’). The interaction between rootstock and training system significantly influenced gas exchange, chlorophyll fluorescence, antioxidant enzyme activity, and yield components. In ‘Bordô’, grapevines trained on high trellis and grafted onto ‘IAC 766 Campinas’ showed a 45.1% higher electron transport rate and 39.8% greater total chlorophyll content at flowering compared to the low trellis and ‘106-8 Mgt’ combination. Productivity increased by 49% under this combination. In ‘Isabel’, low trellis combined with ‘IAC 766 Campinas’ enhanced water use efficiency by 50% and SPAD index by 11%. These results highlight that selecting suitable rootstock and training system combinations can optimize physiological efficiency and yield, representing an effective adaptation strategy for viticulture under subtropical conditions. Full article