Search Results (3,522)

Monitoring species richness patterns across large spatial scales is essential for addressing the global biodiversity crisis. Dynamic Habitat Indices (DHIs), derived from satellite-based productivity data, have proven valuable for predicting species distributions. The original DHI framework comprises three complementary sub-indices, each corresponding to a key ecological hypothesis linking productivity and biodiversity: annual cumulative productivity (DHI Cum; available energy hypothesis), annual minimum productivity (DHI Min; environmental stress hypothesis), and the coefficient of variation in productivity (DHI CV; environmental stability hypothesis). However, current DHI formulations primarily focus on intra-annual vegetation productivity dynamics, thereby overlooking the ecological significance of inter-annual productivity variability. To address this limitation, we propose an extended DHI suite that integrates both seasonal (intra-annual) and long-term (inter-annual) productivity metrics. Using a random forest regression approach, we demonstrate that incorporating this extended DHI suite significantly improves predictions of global vertebrate species richness (cross-validated R² = 0.89, RMSE = 68.20) compared to using seasonal metrics alone (R² = 0.86). Notably, inter-annual productivity variation emerged as the most influential predictor, strongly supporting the environmental stability hypothesis. This was followed by importance in seasonal minimum productivity (environmental stress) and cumulative productivity (available energy). Our findings reveal the critical, complementary roles of seasonal and inter-annual productivity dynamics in shaping global faunal species richness patterns. This enhanced framework provides a robust scalable tool for assessing species richness distributions and informing conservation strategies amid accelerating climate shifts and anthropogenic pressures. Full article

Flood susceptibility in Pakistan is strongly influenced by hydro-climatic variability, land-surface conditions, topography, and recurrent floodplain exposure; however, national-scale studies often lack a comprehensive assessment that captures both spatial patterns and temporal flood-risk dynamics within a single framework. This study is one of Pakistan’s first national efforts to address the gap between flood risk assessment and prioritization through a unified geospatial assessment. This study assesses flood susceptibility across Pakistan for 2002, 2012, and 2022 using a GIS-based AHP approach by integrating climatic, environmental, topographic, hydrological, soil, LULC, and anthropogenic indicators. The study results were further analyzed through district-level assessments, risk change analysis, persistence mapping, LULC exposure assessments, and the Comprehensive Flood Risk Priority Index (FRPI). The results show that high and very high flood susceptibility zones are primarily concentrated along the Indus River corridor, lower floodplains, and coastal Sindh, accounting for more than 7% of the total land area of Pakistan. Persistent flood hotspots are identified in Rann of Kutch (66.6%), Jacobabad (65.0%), and Jafarabad (61.1%), indicating strong temporal stability of flood-prone conditions. LULC exposure analysis reveals that cropland is the dominant exposed class, with the highest district-level exposure observed in Badin (17.1%) and Larkana (10.1%). The FRPI further identifies priority flood-risk zones where susceptibility, persistence, risk change, and exposure converge, with the highest FRPI values observed in Jacobabad (0.742), Rann of Kutch (0.738), and Badin (0.711). Model validation demonstrates strong predictive performance, with susceptibility ROC-AUC values ranging from 0.85 to 0.87 and FRPI AUC reaching 0.85. The proposed framework provides a robust decision-support tool for targeted flood-risk management and climate-resilient land-use planning in Pakistan. Full article

Understanding phenotypic variation in traits associated with drought and heat tolerance is essential for developing climate-resilient spring wheat cultivars under increasingly variable environmental conditions. To evaluate phenotypic and physiological variation in water-use efficiency (WUE), carbon isotope discrimination (δ¹³C), and heat tolerance, 198 Canadian spring wheat cultivars representing diverse breeding backgrounds were assessed under controlled drought and high-temperature conditions. Traits measured included whole-plant water-use efficiency (WUE_WP), carbon isotope composition (δ¹³C), biomass accumulation, water use per plant, and chlorophyll fluorescence across six developmental stages. Whole-plant WUE ranged from 3.07 to 7.81 g L⁻¹, while δ¹³C values ranged from −24.06‰ to −29.33‰. Biomass accumulation and water use were strongly positively correlated (r = 0.94, p < 0.001), indicating that greater biomass production was associated with increased water consumption. In contrast, the relationship between WUE_WP and δ¹³C was weak (r = −0.09), suggesting that δ¹³C alone may not be a reliable proxy for WUE_WP under combined drought and heat stress conditions. Phenotypic diversity across the cultivar panel was relatively low to moderate (Shannon diversity index, H^′ = 1.88–2.62), indicating limited adaptive capacity within the evaluated germplasm. Principal component analysis explained 76.6% of the total variation and effectively differentiated cultivar responses to stress. Chlorophyll fluorescence, particularly the maximum quantum efficiency of PSII photochemistry (F_V/F_M), was highly sensitive to stress-induced reductions in photosynthetic performance. Measurements obtained during reproductive drought and heat stress stages showed stronger associations with biomass, water use, WUE_WP, and δ¹³C than measurements collected during non-stress periods, indicating that F_V/F_M can be a reliable physiological indicator for screening drought and heat tolerance. Overall, the results revealed detectable phenotypic variation but relatively modest diversity and generally weak to moderate trait associations, highlighting the potential value of incorporating diverse germplasm and integrated phenotyping approaches to improve climate resilience in Canadian spring wheat. Full article

Accurate monitoring of hydrological dynamics in complex perennial landscapes is a cornerstone for tropical agricultural sustainability. Traditional energy balance models based on orbital optical data often face methodological bottlenecks due to cloud cover and the “greening myth,” where optical indices fail to capture immediate water stress due to the non-linear decoupling between stomatal closure and pigment loss. This study developed a cloud-integrated multisensor framework to estimate actual evapotranspiration (ET_a) at a refined 100 m resolution in mountain coffee systems, utilizing active microwave proxies from Sentinel-1. We fused polarimetric metrics—Degree of Polarization (DoP) and Shannon Entropy (SE)—with land surface temperature and soil moisture data. Multiple Linear Regression (MLR) was compared against non-linear algorithms (Random Forest and SVR) to prioritize model parsimony and physical interpretability. The results show that MLR emerged as the most parsimonious and suitable model within this localized dataset scope (R² = 0.872; RMSE = 2.916 mm/8-day), outperforming complex “black-box” architectures. Soil moisture emerged as the dominant environmental driver of ET_a variability, while SAR-based metrics served as sensitive mechanical proxies for canopy geometric heterogeneity and macro-structural variations. Cross-correlation analysis revealed a 16-day lag, empirically indicating that biophysical water shifts temporally precede geometric canopy alterations. Operationally, this framework ensures temporal continuity under persistent cloud cover and provides high-fidelity spatial detailing for precision water management. This approach offers an auditable and scalable tool for watershed planning and climate resilience in tropical agriculture. Full article

Beekeeping is highly affected by climate change, which alters environmental conditions and challenges colony stability. In this context, digital monitoring technologies can enhance apiary resilience. This study presents the development and field validation of a low-cost hive monitoring system based on a customizable Raspberry Pi architecture, integrating temperature and weight sensors with robust data continuity features. The system was evaluated over one year in Southern Europe (Serbia) against a commercial reference. Results show that correlation between systems depends on both the monitored parameter and the biological state of the colony. For weight, strong agreement was observed only during winter, when reduced biological activity allows reliable comparison, whereas correlations were weak in more active periods. Conversely, temperature monitoring exhibited the highest correlation over long-term datasets, indicating that extended time scales are required for reliable sensor validation. These findings highlight the importance of a context-aware validation approach in apiculture. The proposed system provides a cost-effective and reliable solution for continuous hive monitoring, supporting data-driven management and improved resilience under climate variability. Full article

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R² values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes. Full article

The Al-Ahsa Oasis region is an important agricultural area; however, continuous spatial–temporal monitoring is essential to assess and mitigate the impacts of climate change and land use change. The current study examines environmental and land cover changes in the Al-Ahsa Oasis region from 1990 to 2025 by utilizing spectral indices derived from multiple satellites. Multi-temporal Landsat imagery (Landsat 5, 8, and 9) was processed in Google Earth Engine (GEE) to derive key biophysical indicators, including the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and bare soil index (BSI). Supervised classification techniques were employed to generate LULC maps for each time step, enabling the assessment of spatiotemporal land cover dynamics. In addition, a random forest (RF) machine learning algorithm was applied to accurately quantify and map the distribution of palm trees across the study area. The results showed that NDVI values fluctuated between −0.19 and 0.75 during the period from 1990 to 2025. Higher vegetation density was observed in central and eastern areas, with maximum values of −0.44–0.75 in 2025. The higher LST was observed in 2025, with a range of 34.7 to 54.6 °C, and the lower LST was observed in 1990 with a range 28.7 to 48.34 °C. BSI values decreased from −0.40 to 0.46 between 1990 and 2025 to a more variable range of −0.27 to 0.36, indicating reduced soil exposure. The classification of LULC numerical data shows a rapid rise in urban development of 67.19% and a 25% decrease in vegetation area. Furthermore, the results of the RF model indicate that palm tree area increased by 16.23% from 1990 to 2025, with overall accuracy of 98.15, and kappa coefficient of 0.962. This research highlights that urban expansion impacts environmental indicators such as LST, while the increasing trend of NDVI could support the palm trees expansion. This study finds valuable information for policymakers and land use planners to develop sustainable urban growth strategies, protect agricultural lands, and enhance oasis ecosystem resilience. Combined remote-sensing-based monitoring into regional planning frameworks can inform decision making for balancing urban development, environmental protection, and long-term agricultural sustainability in the Al-Ahsa Oasis. Full article

Climate change is increasingly reshaping species habitat suitability worldwide. Primula L., the largest genus in Primulaceae, comprises 404 species in China (including 296 endemic species) and is characterized by high endemism and numerous rare and endangered taxa. However, global warming has intensified habitat fragmentation and loss, while its distribution patterns and key environmental drivers remain insufficiently understood. We compiled 7647 occurrence records of 404 wild Primula species in China and integrated 60 environmental variables (climatic, topographic, and soil factors). Using the MaxEnt model combined with ArcGIS spatial analysis, we assessed current and future habitat suitability, identified dominant environmental drivers, and quantified conservation gaps under multiple climate scenarios. Species richness is highly concentrated in Sichuan (186 species), Yunnan (177 species), and Xizang (165 species), with the Hengduan Mountains and eastern Himalayas representing the core distribution area and showing clear peripheral differentiation. The optimized MaxEnt model performed well (AUC = 0.858), identifying temperature seasonality (bio4, 39.8%) and elevation (27.1%) as the main limiting factors. The total suitable habitat area is 268.52 × 10⁴ km², with high-suitability areas mainly distributed in the Hengduan Mountains, southeastern Qinghai–Xizang Plateau, and the Central Mountain Range of Taiwan. Under three shared socioeconomic pathway (SSP) scenarios (SSP126, SSP245, and SSP585), suitable habitat shows a persistent decline, most pronounced under SSP585 in the 2090s (−20.73%), accompanied by a 25.86% reduction in low-suitability areas. Localized expansion of high-suitability habitats suggests that the Hengduan Mountains and southeastern Qinghai–Xizang Plateau may act as potential climatic refugia. Habitat loss consistently exceeds habitat gain, while the distribution centroid shifts westward and northwestward, with migration distances increasing under higher-emission scenarios. Conservation gap analysis indicates that 90.01% of high-suitability habitats lie outside the current protected area network, revealing a strong mismatch between biodiversity hotspots and conservation coverage. These findings highlight the urgent need to expand protected areas and establish micro-reserves in key gap regions (southwestern Sichuan, northwestern Yunnan, southeastern Xizang, and southern Gansu), and to integrate climate-driven migration corridors into conservation planning to support long-term alpine plant persistence under climate change. Full article

Agriophyllum squarrosum (L.) Moq. (sand rice) is a drought-tolerant psammophytic species with high potential as a climate-resilient food crop due to its nutritional value and adaptation to arid environments. This study evaluates morphometric and biochemical variation in seeds from five natural populations across the deserts of Kazakhstan to assess their breeding potential. Seed morphometric traits showed moderate variability (CVs of 4.71–17.98%), with strong positive correlations among seed length, width, and thousand-seed weight, indicating coordinated development. In contrast, biochemical traits, particularly amino acid composition, exhibited substantially higher variability (CV up to 174.9%), reflecting metabolic flexibility under different environmental conditions. Among the amino acids reliably quantified in this study, histidine was the most abundant, while cysteine, tyrosine, and alanine showed high variability. Total protein content remained relatively stable, reaching up to 34.96% in superior accessions. Multivariate analyses revealed significant population differentiation: Akt1 was the most distinct, whereas Alm1 exhibited superior seed size and mass. Weak correlations between morphometric and biochemical traits suggest their partial independence. Integrated multivariate evaluation identified Akt2 and Alm1 as the most promising populations for breeding. Overall, the observed variation highlights strong potential to select genotypes that combine improved seed size with favorable biochemical characteristics, based on the five amino acids quantified above the LOQ, thereby supporting breeding and domestication efforts. Full article

Although pathogenic Leptospira DNA has been detected in water and soil from different climatic regions, information from Mediterranean-climate agricultural systems remains limited. This study characterized the environmental detection of pathogenic Leptospira DNA in water and soil samples from irrigated agroecosystems of central Chile, evaluating spatial and seasonal variation and associations with selected physicochemical variables. A total of 605 samples were collected from eight agricultural sites during spring 2019, summer 2020, and winter 2021. Samples were analyzed by real-time PCR targeting lipL32. Overall, 29.1% of samples were PCR-positive, and pathogenic Leptospira DNA was detected in all sites and seasons. Soil samples showed higher positivity than water samples (34.5% vs. 21.4%), and positivity was higher in summer (41.7%) than in spring (22.7%) or winter (19.3%). Water temperature and turbidity were the only physicochemical variables that differed between positive and negative samples, whereas the binomial generalized linear mixed model (GLMM) showed that season and sample type were associated with PCR positivity after accounting for site-level clustering. These results show that pathogenic Leptospira DNA can be widely detected in irrigated agricultural systems from a Mediterranean-climate region, suggesting that soil, seasonality, irrigation practices, and other site-level characteristics should be considered in future studies on the environmental ecology of pathogenic Leptospira. Full article

Introduction: The circum-Mediterranean region is a global biodiversity hotspot, hosting a highly distinctive freshwater fauna with a high degree of endemism and conservation concern. However, these ecosystems are increasingly threatened by biological invasions, particularly by non-native fish species, which represent a major driver of biodiversity loss. Objective: This study aims to compile a comprehensive and updated inventory of non-native inland fish species across the circum-Mediterranean region and to identify the main taxonomic, biogeographical, and socio-environmental drivers shaping their distribution. Methodology: We conducted an extensive review of the scientific literature, online databases (including EASIN, GISD, and CABI), and technical reports to compile records of non-native fish species across inland and transitional waters of Mediterranean-climate basins. Analyses focused on species composition, taxonomic representativeness, introduction pathways, native regions, and the relationship between species richness and selected environmental and socio-economic variables. Results: A total of 151 non-native fish species were recorded across the study area. Italy, Spain, Bosnia and Herzegovina, France, and Croatia exhibited the highest numbers of established species. Taxonomic representation was uneven, with Salmoniformes and Esociformes overrepresented among established non-native species, while Siluriformes and Characiformes were underrepresented. Most introductions originated from Europe, Asia, and North America, primarily through intentional releases and escape events. Non-native species richness was positively correlated with gross domestic product, precipitation, and the number of dams, highlighting the role of economic development and habitat modification in facilitating invasions. Conclusions: Biological invasions by non-native fishes are widespread across the Mediterranean basin and are strongly driven by human activities and environmental conditions. The high invasion levels observed in this biodiversity hotspot pose a significant threat to endemic freshwater faunas. These findings underscore the need for coordinated transnational management strategies, stricter regulation of introduction pathways, and prioritization of high-risk species to mitigate further impacts. Full article

Rootstock selection is a key component of sustainable vineyard planning, as it strongly influences vine adaptation to soil and environmental conditions. Despite its importance, this decision is often based on empirical knowledge rather than on structured, site-specific approaches. This study presents SR-Vitis, a decision-support module developed within the Vitis system, designed to support rootstock selection through a rule-based framework integrating pedological, climatic, and agronomic variables. The model translates site-specific characteristics into suitability criteria for a set of widely used European rootstocks. The system was applied to four vineyards located in two contrasting Italian winegrowing regions (Chianti Classico and Alta Murgia) to assess the coherence of the model outputs under different pedoclimatic conditions. The comparison with existing tools and current grower choices showed a general agreement in most cases, while also identifying situations where alternative rootstocks may better match site constraints. These results suggest that SR-Vitis can effectively support a more structured and transparent decision-making process. Although not intended as a predictive validation study, this work provides a first operational assessment of the model and highlights its potential as a practical tool for vineyard planning. By integrating expert knowledge and soil-based criteria into an accessible digital framework, SR-Vitis contributes to bridging the gap between empirical practices and data-supported approaches, supporting viticultural adaptation under increasing environmental variability. Full article

Environmental risk management in urban areas has become increasingly important in recent decades, mainly due to climate change and the anticipated rise in the frequency and severity of extreme rainfall events. In highly urbanized environments, these conditions can intensify hydraulic stress on drainage systems, leading to flooding and surcharge within combined sewer networks. Continuous simulations (2008–2018) were performed using coupled hydrological–hydraulic modeling. Discharge outputs and rainfall series were aggregated at hourly resolution and segmented into independent events. Results show marked seasonality: ~86 events/year and ~118 events/year were identified, with higher occurrence in autumn and winter and fewer events in summer. Event duration tends to be longer from late autumn to spring, whereas summer events are generally shorter. Conversely, peak rainfall and peak discharge exhibit higher median values and variability during summer and early autumn, consistent with intense convective Mediterranean storms. Full article

Introduction: Climate change is reducing freshwater availability worldwide, making it essential to understand how freshwater inflow influences estuarine ecosystem functioning and marine fisheries productivity. In the Gulf of Cádiz (SW Spain), one of the most important fishing areas in Spain, the Guadalquivir Estuary serves as a key nursery habitat for commercially important fish and crustacean species. Objective: The aim of this study is to evaluate the effects of droughts and floods on estuarine functioning and coastal fisheries. Methodology: We analyzed 25 years of monthly data (1997–2022) from the Guadalquivir Long-Term Ecological Research Program (GUADALQUIVIR-LTER), using time-series analyses and dynamic structural equation modelling. Environmental variables, zooplankton and mysid biomass, and juvenile biomass of anchovy, sardine, and meagre were examined to assess trophic relationships and recruitment dynamics. Results: Our findings show that positive North Atlantic Oscillation (NAO) phases, associated with drought conditions in southern Europe, reduced freshwater inflow from the Alcalá del Río Dam into the estuary. Freshwater input increased organic matter and turbidity, which positively affected the mysid Rhopalophthalmus tartessicus, an important prey species for anchovy recruits. The mysid Mesopodopsis slabberi showed the strongest positive effect on anchovy recruitment (0.39). Although turbidity initially had a negative effect on M. slabberi, a significant positive effect appeared after monthly lag = 4. Conclusions: These findings demonstrate that spring freshwater inflow is essential for maintaining estuarine productivity, enhancing mysid abundance, and supporting anchovy recruitment, ultimately benefiting adult fish stocks after juveniles migrate from the estuary to coastal waters. Full article

Wildfires are a major ecological disturbance in Mediterranean ecosystems, affecting vegetation dynamics and landscape resilience. However, the relative importance of environmental factors controlling post-fire vegetation recovery remains insufficiently quantified at regional scales. This study investigates the drivers of vegetation regeneration following 45 large wildfires (>1000 ha) that occurred across Greece between 2017 and 2023. Vegetation recovery was assessed using Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series, while environmental predictors included burn severity metrics, soil moisture at four depth layers derived from the European Centre for Medium-Range Weather Forecasts Reanalysis 5-Land (ERA5-Land) climate reanalysis dataset, terrain characteristics (slope and aspect), land cover, and time since fire. All variables were harmonized at the fire-perimeter scale and analyzed using two complementary modeling approaches: multiple linear regression and artificial neural network (ANN) modeling. The linear regression model explained approximately 38% of the variability in vegetation recovery (R² = 0.38), while the ANN showed improved predictive performance, indicating the presence of complex relationships among predictors. Across the applied modeling approaches, burn severity, topographic conditions, and soil moisture emerged as important drivers of post-fire vegetation recovery. In particular, Soil Moisture Layer 1 (SM₁) showed the strongest positive association with NDVI recovery, followed by Soil Moisture Layer 4 (SM₄), highlighting the importance of water availability for vegetation regeneration under post-fire conditions. Overall, the results confirm that vegetation recovery is strongly controlled by environmental conditions rather than time alone. The findings contribute to a better understanding of post-fire ecosystem dynamics in Mediterranean landscapes and provide a useful framework for supporting wildfire management and restoration planning. Full article