Search Results (173)

The spectral variation hypothesis (SVH) posits that the complexity of spectral information in remote sensing imagery can serve as a proxy for regional biodiversity. However, the relationship between spectral diversity (SD) and biodiversity differs for different environmental conditions. Previous SVH studies often overlooked these differences. We utilized species data from field surveys in Inner Mongolia and drone-derived multispectral imagery to establish a quantitative relationship between SD and biodiversity. A geographically weighted regression (GWR) model was used to describe the SD–biodiversity relationship and map the biodiversity indices in different experimental areas in Inner Mongolia, China. Spatial autocorrelation analysis revealed that both SD and biodiversity indices exhibited strong and statistically significant spatial autocorrelation in their distribution patterns. Among all spectral diversity indices, the convex hull area exhibited the best model fit with the Margalef richness index (Margalef), the coefficient of variation showed the strongest predictive performance for species richness (Richness), and the convex hull volume provided the highest explanatory power for Shannon diversity (Shannon). Predictions for Shannon achieved the lowest relative root mean square error (RRMSE = 0.17), indicating the highest predictive accuracy, whereas Richness exhibited systematic underestimation with a higher RRMSE (0.23). Compared to the commonly used linear regression model in SVH studies, the GWR model exhibited a 4.7- to 26.5-fold improvement in goodness-of-fit. Despite the relatively low R² value (≤0.59), the model yields biodiversity predictions that are broadly aligned with field observations. Our approach explicitly considers the spatial heterogeneity of the SD–biodiversity relationship. The GWR model had significantly higher fitting accuracy than the linear regression model, indicating its potential for remote sensing-based biodiversity assessments. Full article

The S100B protein, known for its role in the central and enteric nervous systems, has recently been identified in dietary sources such as milk, dairy products, fruits, and vegetables. Given its potential interaction with the gut microbiota, this study explores the relationship between dietary intake of S100B and microbiota biodiversity across different diets. A comprehensive study was conducted, estimating S100B concentrations in 13 dietary patterns recommended in different countries. This is the first study to provide a comparative estimation of S100B exposure from the diet and to explore its potential ecological and epidemiological relevance. The association between S100B levels and microbiota biodiversity was statistically analyzed, showing a direct correlation. Microbial diversity was assessed using the Shannon index, based on data extracted from studies reporting microbiota composition across dietary patterns. Additionally, the relative risk of Crohn’s disease was assessed in different populations to examine potential links between dietary patterns, S100B, and chronic disease prevention. A moderate positive correlation (R² = 0.537) was found between S100B concentration and Shannon index, suggesting that diets higher in S100B (e.g., Mediterranean diet) were associated with higher microbial alpha-diversity. Furthermore, Western-style diets, with the lowest S100B levels, exhibited a higher relative risk for Crohn’s disease (R² = 0.780). These findings highlight the potential role of dietary S100B content in modulating gut microbiota diversity and reducing chronic disease risk. 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

The Bignoniaceae family encompasses numerous species of ecological, medicinal, and cultural significance, yet its ethnobotanical value remains underexplored in many regions of Thailand. This study investigates the diversity, phenology, cultural relevance, and traditional uses of Bignoniaceae species in Maha Sarakham Province, Northeastern Thailand. Through semi-structured interviews with 260 local informants across 13 districts—alongside field observations and herbarium voucher collections—we documented 27 species across 21 genera. These integrated methods enabled the identification of key culturally significant species and provided insights into their traditional uses. Phenological data revealed clear seasonal patterns in flowering and fruiting, aligned with the regional climatic cycle. Quantitative ethnobotanical indices—including Species Use Value (SUV), Genera Use Value (GUV), Relative Frequency of Citation (RFC), Cultural Importance Index (CI), and Cultural Food Significance Index (CFSI)—were employed to evaluate species significance. Results indicate that species such as Dolichandrone serrulata, D. spathacea, and Oroxylum indicum hold high cultural and practical value, particularly in traditional medicine, spiritual practices, and local landscaping. These findings underscore the critical role of Bignoniaceae in sustaining biocultural diversity and emphasize the urgency of preserving traditional botanical knowledge amid environmental and socio-economic change. Moreover, the insights contribute to broader efforts in cultural heritage preservation and biodiversity conservation across tropical and subtropical regions. Full article

Seasonal climate variability and agronomic management profoundly influence both the productivity and nutritive value of temperate hay meadows. We analyzed five years of data (2019, 2020, 2022–2024) from 15 meadows in the central Spanish Pyrenees to quantify how environmental variables (January–June minimum temperatures, rainfall), management variables (fertilization rates (N, P, K), livestock load, cutting date), and vegetation (plant biodiversity (Shannon index)) drive total biomass yield (kg ha⁻¹), protein content (%), and Relative Feed Value (RFV). Using Random Forest regression with rigorous cross-validation, our yield model achieved an R² of 0.802 (RMSE = 983.8 kg ha⁻¹), the protein model an R² of 0.786 (RMSE = 1.71%), and the RFV model an R² of 0.718 (RMSE = 13.86). Variable importance analyses revealed that March rainfall was the dominant predictor of yield (importance = 0.430), reflecting the critical role of early-spring moisture in tiller establishment and canopy development. In contrast, cutting date exerted the greatest influence on protein (importance = 0.366) and RFV (importance = 0.344), underscoring the sensitivity of forage quality to harvest timing. Lower minimum temperatures—particularly in March and May—and moderate livestock densities (up to 1 LU) were also positively associated with enhanced protein and RFV, whereas higher biodiversity (Shannon ≥ 3) produced modest gains in feed quality without substantial yield penalties. These findings suggest that adaptive management—prioritizing soil moisture conservation in early spring, timely harvesting, balanced grazing intensity, and maintenance of plant diversity—can optimize both the quantity and quality of hay meadow biomass under variable climatic conditions. Full article

The clarification of community assembly mechanisms in benthic macroinvertebrates and their respective contributions to the development of beta diversity is a fundamental concern in aquatic ecology. Nonetheless, the intrinsic complexity of community alterations and their non-linear reactions to gradients of explanatory variables present considerable obstacles to measuring the determinants of beta diversity. Fifty sampling points were set up along the major rivers of the Yellow River Irrigation Area (YRIA), the Central Arid Zone (CAZ), and the Southern Mountainous Area (SMA) in Ningxia in April, July, and October 2023. The findings demonstrate that the optimal parameter-based geographical detector (OPGD) model identified a 3000 m circular buffer as the spatial scale at which landscape structure most significantly influences water quality. A degradation in water quality presumably results in diminished differences in species composition among communities. The Sørensen index was determined to be more appropriate for this investigation, and the total beta diversity of the communities was relatively high (β_SOR ≥ 0.82), with no identifiable nested spatial patterns detected. Except in the YRIA, environmental variability contributed more significantly to the variance in beta diversity than spatial factors, and deterministic mechanisms dominated the community assembly of benthic macroinvertebrates across all three months. To improve biodiversity and aquatic ecosystem health, the study region should optimize its landscape structure by reducing the amount of bare land and increasing the percentage of forest land within buffer zones. Additionally, a multi-site conservation strategy should be put into place. Full article

Cork oak (Quercus suber) woodlands hold significant ecological, cultural, and economic value in the Mediterranean basin, particularly due to cork production, one of the most valued non-wood forest products worldwide. However, cork oak ecosystems are increasingly threatened by climate change, land-use intensification, and rural abandonment, leading to widespread signs of decline. To address these challenges, data-driven and scalable methods are more essential than ever. Satellite-based remote sensing (RS) offers a promising approach for large-scale, cost-effective, and timely monitoring of cork oak forests dynamics and health, but an exhaustive review about this topic is missing. This study reviews 35 peer-reviewed articles published between 2010 and 2025, assessing how satellite RS has been applied to monitor cork oak landscapes. The results show that key research topics include forest disturbances, land cover classification, and forest and environmental variables monitoring. Landsat is the most frequently used satellite mission, and NDVI is the most applied vegetation index. Although machine learning techniques and accuracy metrics are heterogeneous, with results that are difficult to compare, relevant performances have been achieved. For instance, the highest classification accuracy (98%) was reached in mapping cork oak mortality. However, the field remains fragmented, with limited attention to key ecological indicators such as biodiversity, resilience, and ecosystem services. RS for cork oak monitoring is still a relatively young discipline with high potential for development, requiring greater methodological consistency and stronger integration with conservation strategies to support adaptive management in the face of future environmental pressures. Full article

This study explores the ethnobotanical significance of plant species used in the Heet Sip Song (Twelve Monthly Merit-Making) ceremonies in Roi Et Province, Northeastern Thailand. A total of 80 plant species across 73 genera and 42 families were documented. The findings reveal that plants play multifaceted roles in ceremonial life, serving both symbolic and practical purposes rooted in spiritual belief systems and seasonal agricultural cycles. Quantitative analyses using Cultural Significance Index (CSI), Species Use Value (SUV), Genera Use Value (GUV), and Relative Frequency of Citation (RFC) highlighted the prominence of key species such as Oryza sativa, Musa acuminata, and Saccharum officinarum in ritual contexts. While staple crops dominate in frequency and cultural value, less commonly cited wild species fulfill specialized functions, reflecting deep local ecological knowledge. The integration of ritual and plant use promotes biodiversity conservation by maintaining plant populations and reinforcing sustainable harvesting practices. These results emphasize the vital role of traditional knowledge in conserving both biological and cultural diversity. As environmental pressures increase, this study underscores the importance of supporting community-led conservation efforts that honor indigenous practices and their contributions to ecological resilience. Full article

Mangrove forests are vital ecosystems along tropical coasts, playing crucial roles in water purification and biodiversity conservation. Diatoms, as sensitive ecological indicators, were employed in this study to evaluate the health of the mangrove forests along the Sanya River. The research involved analyzing the community structure of planktonic diatoms and water physicochemical properties during spring and winter, as well as carrying out a comprehensive assessment of the ecological health of the region in terms of four seasonal–spatial–environmental–biological indices. A total of 22 genera of planktonic diatoms were identified. In winter, Melosira sp. (34.94%), Skeletonema sp. (25.50%), and Chaetoceros sp. (15%) were dominant, with relative abundances of 34.94%, 25.50%, and 15.00%. In spring, Melosira sp. became the absolutely dominant species, averaging 70.16%. Diatom cell abundance shows both significant seasonal and spatial variation. In winter, it ranged from 0.53 to 17.4 × 10⁹ cells-L⁻¹, peaking in the midstream region, whereas in spring, it ranged from 2.48 to 21.0 × 10⁹ cells-L⁻¹, peaking at the mouth of the estuary. A higher abundance of diatoms in spring strengthens primary productivity and supports the subsequent functioning of the food chain. Diatom indices (Shannon–Wiener index H’, Pielou evenness index J, and Margalef richness index D) indicated an intermediate ecological health level for the Sanya River mangrove forests. Diversity was higher in winter than in spring, with the lowest values recorded in the midstream region. Redundancy analysis (RDA) indicated that T, pH, and PO₄³⁻ were the primary environmental drivers of diatom community succession. In spring, pH was positively correlated with T and PO₄³⁻, respectively. They drove the succession of diatom communities from diverse assemblages in winter to a single dominant species. Full article

Polymer application combined with nitrogen (N) fertilization can increase soil N transformation efficiency. However, the mechanism of polymer influencing soil biocommunity characteristics and nitrogen transformation is still unclear. In this field experiment, a self-developed water-soluble polymer material (PPM, a mixture of anionic polyacrylamide, polyvinyl alcohol, and manganese sulfate) was combined with N fertilization N100 (300 kg/hm² of N), PN100 (PPM + 300 kg/hm² of N), and PN80 (PPM + 240 kg/hm² of N) to investigate soil biodiversity, enzyme activities, and metabolomics. The results showed that under the application of PPM, the contents of soil total nitrogen (TN), alkali hydrolyzable nitrogen (ANS), nitrate nitrogen, organic carbon (SOC), and microbial biomass nitrogen (MBN) increased with a decrease in the N application rate, while soil bulk density, pH, and EC (electrical conductivity) decreased. The Chao 1 index of soil bacterial and nematode communities of the PN80 treatment was 30.6% and 10.7% higher than that of the N100 treatment, respectively, and the Shannon index was 2.72% and 2.64% higher than that of the N100 treatment, respectively. In the short term, the application of PPM affected the structure and composition of soil bacterial and nematode communities. In particular, the relative abundances of omnivorous (Aporcelaimellus) and bacterivorous (Prismatolaimus) nematodes were significantly higher than those of the N100 treatment. These changes further regulated the soil metabolites, promoting soil nitrogen transformation. This study will provide a scientific basis for nitrogen reduction in drip-irrigated wheat planting in arid regions. Full article

Context: Planting forests is an important strategy to combat biodiversity loss and ecosystem service degradation, but its effects on biodiversity and ecosystem services remain uncertain. Objectives: This study aimed to investigate the restoration of plants along successional and environmental gradients in planted forests by examining how understory plant diversity (species richness, composition, functional diversity), functional diversity—the range of species’ traits influencing ecosystem functions and services and their environmental drivers—evolve in temperate plantations over time. Methods: We examined a total of 36 plots with different stand ages in Chongli District, China, and compared the differences in species richness, biodiversity, composition, and functional diversity across different successional stages and over time. We also analyzed the response mechanisms of species richness and functional diversity to environmental factors at both the local and landscape scales. Results and Discussion: Our results showed species diversity, species richness, and functional diversity tended to increase with time in most plots and stabilized after 45 years. Although species richness was lower in mature plots (>100 years), functional diversity was higher, and species composition was significantly differentiated. This trade-off reflects environmental filtering selecting for competitively dominant species with distinct functional traits, while continuous species turnover prevents compositional convergence. The increase in functional diversity was not directly related to the rise in species richness, but it depended on the relative dominance of several species with different functional characteristics in the ecosystem. Simulation analysis confirmed this pattern aligns with a Simpson’s index-driven trait complementarity mechanism. At the local scale, stand age was the most significant positive factor influencing species richness and functional diversity. Soil total nitrogen and organic matter only negatively affected species richness in interactions. At the landscape scale, landscape heterogeneity plays an important role in restoring functional diversity. Historical afforestation since the 1950s restricted comparisons to secondary forests, lacking primary forest baselines. Conclusions: The results suggest that the effects of the successional stage and multiscale environmental factors should be comprehensively considered in the restoration strategy of restored forests. Full article

Amid escalating environmental crises—ranging from biodiversity loss to climate instability—the circular economy has emerged as a promising pathway to align economic growth with ecological limits. The objective of this study is to examine the asymmetric impact of a novel composite circular economy index (CEI)—constructed via entropy weighting—on the load capacity factor (LCF), a holistic sustainability metric, across 27 EU member states over 2010–2023. Employing the method of moments quantile regression (MMQR) and controlling for GDP, foreign direct investment, trade openness, employment, and population growth, the main findings indicate pronounced heterogeneity: positive CEI shocks yield a 1.219 percent increase in LCF at the 90th quantile versus just 0.229 percent at the 10th, revealing a “sustainability premium” for high-performing economies, while negative shocks inflict a −5.253 percent decline at the 90th quantile, exposing their greater vulnerability. Low-LCF countries, by contrast, display relative resilience to downturns, likely due to less entrenched circular systems. Panel Granger causality tests further reveal bidirectional feedback loops between LCF and economic growth, investment, and labor markets, alongside a unidirectional effect from trade openness to enhanced sustainability. These insights carry clear policy implications: high-LCF nations require safeguards against circularity backsliding, whereas low-LCF members need capacity-building to convert latent resilience into sustained gains—together forming a nuanced blueprint for achieving the EU’s 2050 climate-neutrality ambitions. Full article

Apple scab is a disease caused by Venturia inaequalis; it alters the vegetative cycle of apple trees and affects the fruits in orchards or during post-harvest storage. Utilizing rotten apples in cidermaking is a promising technique to mitigate crop losses; nonetheless, uncertainties persist regarding the beneficial effects of damaged fruits. This study involves a thorough chemical analysis of cider produced from both healthy and scab-infected fruits to identify compositional changes caused by microbial proliferation and to assess their impact on cider quality. Apples infected by post-harvest apple scab, as opposed to uninfected apples, were employed in cidermaking. The peel microbiota was described by plate count, and next-generation sequencing-based metabarcoding methods were used to describe the peel microbiota, while HPLC and GC MS-MS were used to characterize the cider compositions. Apples infected with post-harvest scab host a specific fungal consortium with higher biodiversity, as evidenced by the Shannon evenness index, especially in the fungi kingdom. The presence of apple scab slows fermentation by up to 23%, lowers ethanol accumulation by up to 0.4%, and affects certain cider constituents: sugars, alcohols, amino acids, fatty acids, and esters. The statistical treatment of data relative to the chemical profile (PLS and PCA on the 31 compounds with VIP > 1) distinguishes ciders made from altered or safe fruits. Scab-infected apples can be valorized in the agri-food industry; however, microbiota alterations must not be underestimated. It is necessary to implement adequate mitigation strategies. Full article

Global coral reef ecosystems face various levels of disturbance pressure. Understanding the depth-structured variation in coral reef fish communities can help us to better grasp and predict the adaptive changes of the ecosystem under different stressors. This study applied eDNA metabarcoding technology to analyze the spatial distribution of the coral reef fish at various depths (0 m, 5 m, 10 m, 15 m, 20 m, 30 m, 40 m, 50 m, and 60 m) within the Xisha Islands of China. The results indicated that the eDNA technology detected a total of 213 amplicon sequence variants (ASVs), including 33 species that were not identified using traditional methods. Herbivorous fish generally dominated in relative abundance across different depths. Moreover, the similarity among depth groups was largely absent, and significant differences existed in fish assemblages across depth gradients, consistent with the unique depth preferences of fish microhabitats. Importantly, our findings revealed distinct depth-structured variation among different functional groups of coral reef fish. Large carnivorous fish initially increased and then decreased along the depth gradient from 0 to 60 m, with a turning point around 20 m, while large herbivorous fish displayed the opposite trend. Small carnivorous and small herbivorous fish consistently declined along the same depth gradient. Additionally, the Margalef index (D) and Function richness (FRic) both displayed a consistent downward trend with increasing depth, while the Shannon–Wiener index (H′), Pielou index (J′), Quadratic entropy (RaoQ), Functional dispersion (FDis), and Functional evenness (FEve) initially increased and then decreased, peaking around 20 m. This study revealed that eDNA metabarcoding is an effective tool for evaluating coral reef fish biodiversity, community composition, and spatial distribution. It enhances our understanding of distribution dynamics and offers valuable insights for coral reef conservation and restoration efforts. Full article

How can the shape of biodiversity inform us about cities’ ecoclimatic fitness and guide their development? Can we use species as the harbingers of climatic extremes? Eco-climatically sensitive species carry information about hydroclimatic change in their distribution, fitness, and preferential gradients of habitat suitability. Conversely, environmental features outside of the species’ fitness convey information on potential ecological anomalies in response to extremes to adapt or mitigate, such as through urban parks. Here, to quantify ecosystems’ fitness, we propose a novel computational model to extract multivariate functional ecological networks and their basins, which carry the distributed signature of the compounding hydroclimatic pressures on sentinel species. Specifically, we consider butterflies and their habitat suitability (HS) to infer maximum suitability gradients that are meaningful of potential species networks and flows, with the smallest hydroclimatic resistance across urban landscapes. These flows are compared to the distribution of urban parks to identify parks’ ecological attractiveness, actual and potential connectivity, and park potential to reduce hydroclimatic impacts. The ecosystem fitness index (EFI) is novelly introduced by combining HS and the divergence of the relative species abundance (RSA) from the optimal log-normal Preston plot. In Shenzhen, as a case study, eco-flow networks are found to be spatially very extended, scale-free, and clustering for low HS gradient and EFI areas, where large water bodies act as sources of ecological corridors draining into urban parks. Conversely, parks with higher HS, HS gradients, and EFIs have small-world connectivity non-overlapping with hydrological networks. Diverging patterns of abundance and richness are inferred as increasing and decreasing with HS. HS is largely determined by temperature and precipitation of the coldest quarter and seasonality, which are critical hydrologic variables. Interestingly, a U-shape pattern is found between abundance and diversity, similar to the one in natural ecosystems. Additionally, both abundance and richness are mildly associated with park area according to a power function, unrelated to longitude but linked to the degree of urbanization or park centrality, counterintuitively. The Preston plot’s richness–abundance and abundance-rank patterns were verified to reflect the stationarity or ecological meta-equilibrium with the environment, where both are a reflection of community connectivity. Ecological fitness is grounded on the ecohydrological structure and flows where maximum HS gradients are indicative of the largest eco-changes like climate-driven species flows. These flows, as distributed stress-response functions, inform about the collective eco-fitness of communities, like parks in cities. Flow-based networks can serve as blueprints for designing ecotones that regulate key ecosystem functions, such as temperature and evapotranspiration, while generating cascading ecological benefits across scales. The proposed model, novelly infers HS eco-networks and calculates the EFI, is adaptable to diverse sensitive species and environmental layers, offering a robust tool for precise ecosystem assessment and design. Full article