Search Results (1,538)

The prolonged retention of senescent branches and needles (canopy litter) in Cunninghamia lanceolata canopies is an evolutionary adaptation, yet its impacts on stand microenvironment and understory succession remain poorly quantified. To address this gap, we conducted a 5-year field experiment across six planting densities (1800, 2400, 3000, 3600, 4200, and 4800 trees·ha⁻¹), aiming to evaluate the effects of canopy litter removal on canopy structure, forest light environment, and understory biodiversity. Results demonstrated that leaf area index (LAI) and mean tilt angle of the leaf (MTA) significantly increased with density (p < 0.05), leading to marked reductions in photosynthetic photon flux density (PPFD) and light transmittance (T). Canopy litter removal significantly reduced LAI across all densities after 4–5 years (p < 0.05) and consistently enhanced PPFD and transmittance (p < 0.01). MTA and light quality parameters (red:blue and red:far-red ratios) both exhibited variable responses to litter removal, driven by density and time interactions, with effects diminishing over time. Understory vegetation diversity exhibited pronounced temporal dynamics and density-dependent responses to canopy litter removal, with increases in species richness (S), Simpson diversity (D), and Shannon–Wiener diversity (H), while Pielou Evenness (J) responded more variably. The most notable increase in species richness occurred in the 4th year, when 21 new species were recorded, largely due to the expansion of light-demanding bamboos (e.g., Indocalamus tessellatus and Pleioblastus amarus), heliophilic grasses (e.g., Lophatherum gracile) and pioneer ferns (e.g., Pteris dispar and Microlepia hancei). Correlation analyses confirmed PPFD as a key positive driver of all diversity indices (p < 0.01), whereas LAI was significantly negatively correlated with PPFD, light transmittance, and understory diversity (p < 0.01). These findings demonstrate that strategic management of canopy litter incorporating stand density regulation can improve understory light availability, thereby facilitating heliophilic species recruitment and biodiversity enhancement in subtropical coniferous plantations. Full article

Star scientists are highly influential researchers who have made significant contributions to their field, gained widespread recognition, and often attracted substantial research funding. They are critical for the advancement of science and innovation and significantly influence the transfer of knowledge and technology to industry. Identifying potential star scientists before their performance becomes outstanding is important for recruitment, collaboration, networking, and research funding decisions. This study utilizes machine learning techniques and builds four different classifiers, i.e., random forest, support vector machines, naïve bayes, and logistic regression, to predict star scientists in the field of artificial intelligence while highlighting features related to their success. The analysis is based on publication data collected from Scopus from 2000 to 2019, incorporating a diverse set of features such as gender, ethnic diversity, and collaboration network structural properties. The random forest model achieved the best performance with an AUC of 0.75. Our results confirm that star scientists follow different patterns compared to their non-star counterparts in almost all the early-career features. We found that certain features, such as gender and ethnic diversity, play important roles in scientific collaboration and can significantly impact an author’s career development and success. The most important features in predicting star scientists in the field of artificial intelligence were the number of articles, betweenness centrality, research impact indicators, and weighted degree centrality. Our approach offers valuable insights for researchers, practitioners, and funding agencies interested in identifying and supporting talented researchers. Full article

The Lower Rio Grande Valley (LRGV) of Texas is a biodiversity hotspot due to its high alpha, beta, and gamma diversity and high regional endemism, which are at high risk of degradation. The region has lost 95% of its native thornforest habitat primarily due to agricultural and urban expansion. This study aims to evaluate the current vegetative structure and composition of restored thornforest sites located in the LRGV to identify restoration methods and site characteristics that affect forest restoration outcomes. Twelve restored thornforest sites were selected for this study that varied in time since restoration, patch size, degree of isolation, and method of restoration. Canopy, understory, and ground layer vegetation were evaluated at six survey points per restored site (n = 72), and 17 environmental variables were incorporated into univariate and multivariate analyses to identify factors influencing restored plant communities. Actively restored sites showed higher overall richness, abundance, and diversity than passively restored sites. More isolated patches had higher overall richness, abundance, and diversity, and longer times since restoration began increased richness and diversity. Higher abundances of Urochloa maxima, an invasive grass, altered community composition and reduced diversity in each forest layer and overall and reduced richness in the canopy and ground layers. Important considerations for thornforest restoration in the LRGV should include invasive grass prevalence; proximity to riparian and seasonal wetland habitats; landscape factors that influence water availability; and patch geography, including shape, size, and proximity to other forest patches. Full article

Larix gmelinii is the fourth most important tree species in China and a typical zonal climax species in the cold temperate region, with high ecological and resource value. However, intensive logging, high-density afforestation, and insufficient scientific management have led to overly dense, homogeneous, and unstable plantations, severely limiting productivity. To clarify the mechanisms by which structural dynamics regulate productivity, we established a space-for-time sequence (T1–T3, T2-D, CK) under a consistent early-tending background. Using the “1 + 4” nearest-neighbor framework and six spatial structural parameters, we developed tree and forest spatial structure indices (TSSI and FSSI) and integrated nine structural–functional indicators for multivariate analysis. The results showed that TSSI and FSSI effectively characterized multi-level stability and supported stability classification. Along the stand-age gradient, structural stability and spatial use efficiency improved significantly, with FSSI and biomass per hectare (BPH) increasing by 91% and 18% from T1 to T3, though a “structural improvement–functional lag” occurred at T2. Moderate thinning markedly optimized stand configuration, reducing low-stability individuals from 86.45% in T1 to 42.65% in T2-D, while DBH, crown width, FSSI, and BPH (229.87 t·hm⁻²) increased to near natural-forest levels. At the tree scale, DBH, tree height, crown width, and TSSI were positive drivers, whereas a high height–diameter ratio (HDR) constrained growth. At the stand scale, canopy density, species richness, and mean DBH promoted FSSI and BPH, while mean HDR and stand density imposed major constraints. A critical management window was identified when DBH < 25 cm, HDR > 10, and TSSI < 0.25 (approximately 10–30 years post-planting). We propose a stepwise, moderate, and targeted thinning strategy with necessary underplanting to reduce density and slenderness, increase diameter and canopy structure, and enhance diversity, thereby accelerating the synergy between stability and productivity. This framework provides a practical pathway for the scientific management and high-quality development of L. gmelinii plantations. Full article

Although bacterial and fungal communities play essential roles in organic matter degradation and humification during composting, their composition, interactions, abiotic compost properties, and succession patterns remain unclear. In this study, the succession of bacterial and fungal communities during algal sludge composting was explored using 16S and ITS rRNA amplicon sequencing. The compost rapidly entered the thermophilic phase (>50 °C) within the first phase. During the composting process, the diversity of bacterial and fungal communities did not show a significant response to the different composting phases. The physicochemical parameters and microbial community structures changed significantly during the thermophilic and cooling phases, particularly in the former, and gradually stabilized as the compost matured. Integrated random forest and network analyses suggested that the bacteria genera Geobacillus and Parapedobacter, along with the fungus genus Gilmaniella, could serve as potential biomarkers for different composting phases. The functional activity of the bacterial communities was obviously higher during the thermophilic phase than during the other phases, while fungal activity remained relatively high during both the thermophilic and cooling phases. Structural Equation Modeling (SEM) further indicated that bacterial communities primarily mediated nitrogen transformation and humification processes, while fungal communities mainly contributed to humification. These results cast a new light on understanding about microbial function during aerobic algal sludge composting. Full article

This study investigates the causal impact of board governance structures on firm profitability. We develop the Board Structure Influence (BSI) index, a composite metric that captures board independence, diversity, and role distribution—which we conceptualize as three structural pillars of Separation, Variety, and Disparity—to provide a comprehensive measure of governance effectiveness. Using a Difference-in-Differences (DiD) framework centered on the COVID-19 pandemic as an exogenous shock, we identify firms with strong governance and top BSI quartiles and compare their financial performance—measured by net profit margin—against firms with weaker board structures. Our results demonstrate that firms with higher BSI scores experience a statistically significant increase in profitability post-COVID-19. A Causal Forest analysis further reveals that this positive effect is heterogeneous, with the largest firms benefiting most significantly from strong board governance. Robustness checks—including placebo tests, parallel trends validation, and a SUTVA test—affirm the credibility of our findings. This research highlights the strategic importance of board structure for firm resilience during crises. It provides management insights for corporate leaders, investors, and policymakers aiming to align governance reform with financial profitability. Full article

Due to the evident increase in tick-borne diseases worldwide, it is necessary to constantly update information on the distribution and zoonotic potential of hard ticks. We studied diversity, population structure, and seasonal dynamics of hard tick fauna, faunal similarity and the climate impact on tick occurrence in natural habitats (NHs) (forest communities) and anthropogenic habitats (AHs) (orchards, grasslands, degraded forests) in eastern and central parts of Continental Croatia. Host-seeking hard ticks were sampled by the flag-dragging method in lowland AHs (Bansko Hill (BH); 2023–2024 yr.) and in mountainous NHs (Medvednica Mountain (MM); 2019–2021, 2024 yr.). Overall, 2726 specimens belonging to eight hard tick species (Ixodes ricinus, I. frontalis, I. hexagonus, I. kaiseri, Haemaphysalis inermis, H. concinna, Dermacentor marginatus, D. reticulatus) were identified in AHs, while in NHs 1543 hard ticks, belonging to three species (I. ricinus, I. frontalis, D. reticulatus), were collected. The most abundant species in both habitat types (47.83% in AHs, 99.80% in NHs) was I. ricinus, showing unimodal seasonal activity within studied NHs and bimodal activity at AHs. Comparison of hard tick fauna in different habitats using the Sørenson index on BH and MM showed a high percentage of similarity (50.0–88.8). At AHs, a significant (p < 0.05) negative correlation was determined between the abundance (N) and the mean monthly air temperatures (°C) for H. inermis (r = −0.5931; p = 0.0421) and D. reticulatus (r = −0.6289; p = 0.0285), while their numbers positively correlated (r = 0.5551; p = −0.2667; r = 0.4430; p = 0.1492) with air humidity (%). In contrast, the number of sampled host-seeking I. ricinus ticks at natural forest habitats on MM was positively associated with air temperature and negatively with air humidity at elevations from 200 to 1000 m a.s.l. (r = −0.7684; p = 0.0259; at 200 m a.s.l.). Collected specimens of I. frontalis mark the first record for Osijek–Baranja County, while the sampled D. reticulatus on MM represents the first catch at 1000 m a.s.l. in Croatia. This new data on the distribution and seasonality of medically important hard tick species in Continental Croatia contributes to identifying tick-risk foci and high-risk periods. Full article

Changes in vegetation type shape the soil microenvironment, thereby regulating the changes in the organic carbon pool by influencing microbial communities and the accumulation of microbial necromass carbon (MNC). This study investigated microbial biomass—via phospholipid fatty acids (PLFAs) analysis—and MNC accumulation across three cold–temperate forest types: Larix gmelinii forest (L), Larix gmelinii–Betula platyphylla Sukaczev mixed forest (LB), and Betula platyphylla Sukaczev forest (B). The results showed that the L had the lowest contents of pH, water content (WC), soil organic carbon (SOC), total nitrogen (TN), available nitrogen (AN), and total phosphorus (TP), but the highest contents of dissolved organic carbon (DOC), available phosphorus (AP), and carbon to nitrogen ratio (C/N) (p < 0.05). LB had the lowest PLFAs content and the highest ratio of Gram-positive bacteria/Gram-negative bacteria (G+/G−), and total fungi/total bacteriai (F/B) of L was the highest. B had the highest alpha diversity index, and significantly positively correlated with pH, SOC, TN, AN, and TP. TP and C/N were the primary elements for significant differences in microbial community structure. The order of MNC content and its contribution to SOC was B > LB > L. MNC was significantly negatively correlated with PLFAs, DOC, and AP, and significantly positively correlated with pH, SOC, TN, AN, TP, Shannon–Wiener and Pielou indices. In conclusion, this study demonstrates that Betula platyphylla Sukaczev forest retains more carbon, nitrogen, and phosphorus, microbial alpha diversity, and acquires more MNC, which can provide a basis for subsequent forest management and carbon sequestration projects. Full article

The European sweet chestnut (Castanea sativa Mill.) is an ecologically and culturally significant forest tree species in Croatia; however, its genetic diversity and population structure remain insufficiently characterized. This study aimed to evaluate the genetic diversity, structure, and connectivity of chestnut populations on Zrin Mountain, the country’s largest continuous chestnut area. Using seven nuclear SSR markers, we genotyped 153 individuals from three populations (PET, HRK, and BAC). All populations exhibited moderate genetic diversity (mean He = 0.571), with BAC showing the highest allelic richness and number of private alleles. AMOVA revealed that most genetic variance (67%) occurred among individuals, while population differentiation was low to moderate (FST = 0.064; PhiPT = 0.146), consistent with high inferred gene flow (Nm = 7.48). Both STRUCTURE and PCoA indicated that HRK was the most genetically distinct population, whereas PET and BAC were more similar. Overall, these findings demonstrate substantial gene flow and connectivity among Croatian chestnut populations, providing a foundation for sustainable management and conservation strategies in a broader European context. Full article

Norway spruce (Picea abies (L.) H. Karst.) is a primary forest-forming species in the European part of Russia, both in terms of its distribution and economic importance. A number of studies indicate that one of the reasons for the disturbance of spruce forests is linked to rising temperatures, particularly the detrimental effects of extreme droughts. The aim of our research is to identify changes in the structural and functional organization of mature spruce forests at the center of the East European Plain. The study was conducted in intact spruce forests using resurveyed vegetation relevés within the Smolensk–Moscow Upland, with relevés repeated after 40 years (in 1985 and 2025). Changes in structural and functional parameters of spruce communities were analyzed. The results showed that significant disturbances of the tree layer led to changes in the vegetation of subordinate layers, as well as the successional dynamics of spruce forests. It was found that following the collapse of old-growth spruce stands, two types of secondary succession developed: (1) with the renewal of spruce and (2) with active development of shrubs (hazel and rowan) and undergrowth of broadleaved species. It was also demonstrated that the typological diversity of the studied communities changed over 40 years not only due to the loss of the tree layer and the formation of new “non-forest” types but also because several mixed spruce-broadleaved communities transitioned into broadleaved ones, and pine–spruce communities of boreal origin shifted to nemoral types. An analysis of the complete species composition of spruce forests based on Ellenberg’s scales scoring revealed changes in habitat conditions over the 40-year period. A noticeable trend was an increase in the proportion of thermophilic and alkaliphilic species, indicating a shift toward a nemoral vegetation spectrum. It is expected that under the current forest management regime, the next 40 to 60 years will see a decline in the proportion of spruce within mixed stands, potentially culminating in the complete collapse of monospecific spruce forests in the center of the East European Plain. Full article

This study investigates the spatial heterogeneity and species diversity of grassland vegetation in the agro-pastoral ecotone of China’s Loess Plateau, integrating Taylor’s power law model with the minimum area concept to address scale-dependent ecological patterns. Field surveys were conducted across four vegetation types: small-leaf poplar forest (SP), pine–caragana mixed forest (PC), caragana shrubland (RC), and saline grassland (SG). Nested quadrats (0.25–8 m²) were used to establish species–area relationships (SARs), while binary occurrence frequency data fitted to Taylor’s power law quantified spatial heterogeneity parameters (δ_i, δ_c, CACD) and derived diversity indices (H′, J′, D). the results showed that species composition differed significantly among vegetation types, with RC exhibiting the highest richness (25 species) and SG the lowest (12 species). SAR analysis showed distinct z-values: SP had the lowest z (0.14), indicating minimal area effects and high homogeneity, while SG had the highest area sensitivity. Spatial heterogeneity (δ_c) was highest in RC and lowest in SP. Over 82.5% of herb-layer species exhibited aggregated distributions (δ_i > 0). The dominant species Leymus secalinus (Georgi) Tzvelev shifted from regular (δ_i < 0) under SP/SG to aggregated (δ_i > 0) under PC/RC. Diversity metrics peaked in PC plots (highest H′ and richness, lowest dominance), whereas SP showed high dominance but low diversity. CACD values (critical aggregation diversity) were maximized under SG. The integration of power law modeling and minimum area analysis effectively captures scale-dependent vegetation patterns. Pine–caragana mixed forests (PC) optimize biodiversity and spatial heterogeneity, suggesting moderated canopy structures enhance ecological stability. These findings provide a theoretical basis for sustainable grassland management in ecologically sensitive agro-pastoral zones. Full article

In tropical regions, the establishment of large-scale exotic plantations has addressed the demand for timber resources but has also disrupted the structural stability of native vegetation and altered soil nutrient cycling, thereby impairing ecosystem functions. Identifying effective restoration strategies for these plantations is crucial for sustainable forest management and ecological security. This study examined Acacia mangium Willd., Cunninghamia lanceolata (Lamb.) Hook., and Pinus caribaea Morelet. plantations in Hainan Tropical Rainforest National Park under three treatments: plantation control, girdling, and natural secondary forest. Vegetation surveys and soil analyses were conducted to explore the relationships between community structure, soil physicochemical properties, and enzyme activities. Diversity indices, Pearson correlations, and redundancy analysis were used to assess plant–soil relationships. The results showed that girdling significantly accelerated succession in C. lanceolata and P. caribaea plantations, increased species diversity, and enhanced the dominance of native species. Shrub-layer diversity indices (H_shrub, D_shrub, E_shrub) were the main drivers of soil properties and enzyme activities, while tree-layer effects were weaker. Girdling regulated soil nutrients and biological activity primarily via changes in community structure. These findings highlight the importance of optimizing shrub-layer structure and enhancing diversity for tropical plantation restoration. Combining forest type conversion with moderate interventions can promote coordinated plant–soil development over time. Full article

Mangroves play a critical role in global carbon sequestration, biodiversity conservation, and climate change mitigation. Accurately quantifying mangrove biomass is essential for sustainable forest management and carbon accounting. Yet, the structural complexity and species diversity of mangrove ecosystems pose significant challenges for accurate estimation. In this study, we developed an integrated model that combines multispectral imagery and radar data. Using Sentinel-2 and ALOS-2 satellite imagery combined with field measurements, these data were used to construct linear regression and random forest models for the Matang Mangrove Reserve, Malaysia. We further analyzed the relationships between vegetation indices, radar polarization modes, and biomass. Results indicate that the average biomass is approximately 146 t/ha. The Optimized Soil-Adjusted Vegetation Index (OSAVI) and horizontal–vertical (HV) polarization showed the strongest correlation with field-measured biomass, with an R² of 0.735 and a root mean square error (RMSE) of 46.794 t/ha. This study provides a scientific basis and technical support for mangrove carbon stock assessment, ecosystem management, and climate change mitigation strategies, and highlights the potential of integrating optical and radar remote sensing for large-scale mangrove biomass monitoring. Full article

Understanding how urban environmental features shape physical activity is crucial for building health-supportive cities, especially under climate change pressures such as rising temperatures and extreme weather. Previous studies emphasized density and accessibility, but the spatial mechanisms driving facility usage remain understudied. This study investigates how land use diversity, the distribution of physical activity facilities, street network structure, and road accessibility shape physical activity behaviours at the neighbourhood scale. Using a 500 m × 500 m grid framework in Xiamen, China, a random forest model combined with Shapley Additive Explanations (SHAP) is employed to quantify the importance of environment indicators. The results demonstrate that road accessibility and street connectivity exert the strongest influence on physical activity facility use, followed by land use diversity and 15 min reachable residential Points of Interests (POIs). Spatial autocorrelation and cluster analysis further reveal that high-impact areas are concentrated in central and southern zones, whereas peripheral regions face accessibility deficits. These findings highlight the value of integrating transport planning and land use configuration to address spatial disparities in facility usage. The study contributes a replicable methodological framework and provides practical insights for advancing equitable and activity-friendly neighbourhood design. Full article

Background/Objectives: Today, Artificial intelligence (AI) and machine learning (ML) significantly enhance predictive analytics in the healthcare landscape, enabling timely and accurate predictions that lead to proactive interventions, personalized treatment plans, and ultimately improved patient care. As healthcare systems increasingly adopt data-driven approaches, the integration of AI and data analysis has garnered substantial interest, as reflected in the growing number of publications highlighting innovative applications of AI in clinical settings. This review synthesizes recent evidence on application areas, commonly used models, metrics, and challenges. Methods: We conducted a systematic literature review between using Web of Science and Google Scholar databases from 2021–2025 covering a diverse range of AI and ML techniques applied to disease prediction. Results: Twenty-two studies met criteria. The most frequently used machine learning approaches were tree-based ensemble models (e.g., Random Forest, XGBoost, LightGBM) for structured clinical data, and deep learning architectures (e.g., CNN, LSTM) for imaging and time-series tasks. Evaluation most commonly relied on AUROC, F1-score, accuracy, and sensitivity. key challenges remain regarding data privacy, integration with clinical workflows, model interpretability, and the necessity for high-quality representative datasets. Conclusions: Future research should focus on developing interpretable models that clinicians can understand and trust, implementing robust privacy-preserving techniques to safeguard patient data, and establishing standardized evaluation frameworks to effectively assess model performance. Full article