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Search Results (1,473)

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31 pages, 2831 KiB  
Article
Structural Diversity and Biodiversity of Forest and Hedgerow in Areas Managed for Pheasant Shooting Across the UK
by Peter R. Long, Leo Petrokofsky, William J. Harvey, Paul Orsi, Matthew W. Jordon and Gillian Petrokofsky
Forests 2025, 16(8), 1249; https://doi.org/10.3390/f16081249 - 1 Aug 2025
Abstract
Management for pheasant shooting is a widespread land use in the UK, with potential implications for forest and hedgerow habitats. This study evaluates whether sites managed for pheasant shooting differ ecologically from similar sites not used for shooting. A systematic evidence evaluation of [...] Read more.
Management for pheasant shooting is a widespread land use in the UK, with potential implications for forest and hedgerow habitats. This study evaluates whether sites managed for pheasant shooting differ ecologically from similar sites not used for shooting. A systematic evidence evaluation of comparative studies was combined with a spatial analysis using remote sensing data (2010–2024). The literature review identified only 32 studies meeting strict criteria for comparability, revealing inconsistent and often weak evidence, with few studies reporting detailed forest management or statistically robust outcomes. While some studies noted increased or decreased biodiversity associated with pheasant shooting, the evidence base was generally of low quality. Remote sensing assessed forest structural and spectral diversity, intactness, and hedgerow density across 1131 pheasant-managed and 1131 matched control sites. Biodiversity data for birds, plants, and butterflies were sourced from GBIF records. Structural diversity and hedgerow density were significantly higher on pheasant-managed sites, while no significant differences were found in forest spectral diversity, intactness, or biodiversity indicators. Pheasant management may shape certain habitat features but has limited demonstrable effects on overall biodiversity. Further field-based, controlled studies are required to understand causal mechanisms and inform ecologically sustainable shooting practices. Full article
(This article belongs to the Special Issue Biodiversity and Ecosystem Functions in Forests)
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33 pages, 4670 KiB  
Article
Universal Prediction of CO2 Adsorption on Zeolites Using Machine Learning: A Comparative Analysis with Langmuir Isotherm Models
by Emrah Kirtil
ChemEngineering 2025, 9(4), 80; https://doi.org/10.3390/chemengineering9040080 - 28 Jul 2025
Viewed by 138
Abstract
The global atmospheric concentration of carbon dioxide (CO2) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO2 uptake presents limitations due to the need for case-specific parameter [...] Read more.
The global atmospheric concentration of carbon dioxide (CO2) has exceeded 420 ppm. Adsorption-based carbon capture technologies, offer energy-efficient, sustainable solutions. Relying on classical adsorption models like Langmuir to predict CO2 uptake presents limitations due to the need for case-specific parameter fitting. To address this, the present study introduces a universal machine learning (ML) framework using multiple algorithms—Generalized Linear Model (GLM), Feed-forward Multilayer Perceptron (DL), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Gradient Boosted Trees (GBT)—to reliably predict CO2 adsorption capacities across diverse zeolite structures and conditions. By compiling over 5700 experimentally measured adsorption data points from 71 independent studies, this approach systematically incorporates critical factors including pore size, Si/Al ratio, cation type, temperature, and pressure. Rigorous Cross-Validation confirmed superior performance of the GBT model (R2 = 0.936, RMSE = 0.806 mmol/g), outperforming other ML models and providing comparable performance with classical Langmuir model predictions without separate parameter calibration. Feature importance analysis identified pressure, Si/Al ratio, and cation type as dominant influences on adsorption performance. Overall, this ML-driven methodology demonstrates substantial promise for accelerating material discovery, optimization, and practical deployment of zeolite-based CO2 capture technologies. Full article
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17 pages, 1268 KiB  
Article
Community Composition and Diversity of β-Glucosidase Genes in Soils by Amplicon Sequence Variant Analysis
by Luis Jimenez
Genes 2025, 16(8), 900; https://doi.org/10.3390/genes16080900 - 28 Jul 2025
Viewed by 101
Abstract
Cellulose, the most abundant organic polymer in soil, is degraded by the action of microbial communities. Cellulolytic taxa are widespread in soils, enhancing the biodegradation of cellulose by the synergistic action of different cellulase enzymes. β-glucosidases are the last enzymes responsible for the [...] Read more.
Cellulose, the most abundant organic polymer in soil, is degraded by the action of microbial communities. Cellulolytic taxa are widespread in soils, enhancing the biodegradation of cellulose by the synergistic action of different cellulase enzymes. β-glucosidases are the last enzymes responsible for the degradation of cellulose by producing glucose from the conversion of the disaccharide cellobiose. Different soils from the states of Delaware, Maryland, New Jersey, and New York were analyzed by direct DNA extraction, PCR analysis, and next generation sequencing of amplicon sequences coding for β-glucosidase genes. To determine the community structure and diversity of microorganisms carrying β-glucosidase genes, amplicon sequence variant analysis was performed. Results showed that the majority of β-glucosidase genes did not match any known phylum or genera with an average of 84% of sequences identified as unclassified. The forest soil sample from New York showed the highest value with 95.62%. When identification was possible, the bacterial phyla Pseudomonadota, Actinomycetota, and Chloroflexota were found to be dominant microorganisms with β-glucosidase genes in soils. The Delaware soil showed the highest diversity with phyla and genera showing the presence of β-glucosidase gene sequences in bacteria, fungi, and plants. However, the Chloroflexota genus Kallotanue was detected in 3 out of the 4 soil locations. When phylogenetic analysis of unclassified β-glucosidase genes was completed, most sequences aligned with the Chloroflexota genus Kallotenue and the Pseudomonadota species Sphingomonas paucimobilis. Since most sequences did not match known phyla, there is tremendous potential to discover new enzymes for possible biotechnological and pharmaceutical applications. Full article
(This article belongs to the Section Microbial Genetics and Genomics)
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13 pages, 1075 KiB  
Article
Response of Typical Artificial Forest Soil Microbial Community to Revegetation in the Loess Plateau, China
by Xiaohua Liu, Tianxing Wei, Dehui Fan, Huaxing Bi and Qingke Zhu
Agronomy 2025, 15(8), 1821; https://doi.org/10.3390/agronomy15081821 - 28 Jul 2025
Viewed by 142
Abstract
This study aims to analyze the differences in soil bacterial community structure under different vegetation restoration types, and to explore the role of microorganisms in the process of vegetation restoration on the soil ecosystem of the Grain for Green area in the Loess [...] Read more.
This study aims to analyze the differences in soil bacterial community structure under different vegetation restoration types, and to explore the role of microorganisms in the process of vegetation restoration on the soil ecosystem of the Grain for Green area in the Loess Plateau. High-throughput sequencing technology was used to analyze the alpha diversity of soil bacteria, community structure characteristics, and the correlation between soil environmental factors and bacterial communities in different artificial Hippophae rhamnoides forests. Soil microbial C and N show a decreasing trend with an increase in the 0–100 cm soil layers. The results indicated that the bacterial communities comprised 24 phyla, 55 classes, 110 orders, 206 families, 348 genera, 680 species, and 1989 OTUs. Additionally, the richness indices and diversity indices of the bacterial community in arbor shrub mixed forest are higher than those in shrub pure forest, and the indices of shrub forest on sunny slope are higher than those on shady slope. Across all samples, the dominant groups were Actinobacteria (37.27% on average), followed by Proteobacteria (23.91%), Acidobacteria (12.75%), and Chloroflexi (12.27%). Soil nutrient supply, such as TOC, TN, AN, AP, and AK, had crucial roles in shaping the composition and diversity of the bacterial communities. The findings reveal that vegetation restoration significantly affected soil bacterial community richness and diversity. Furthermore, based on the results, our data provide a starting point for establishing soil bacterial databases in the Loess Plateau, as well as for the plants associated with the vegetation restoration. Full article
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24 pages, 5785 KiB  
Article
Phylogenetic Reassessment of Murinae Inferred from the Mitogenome of the Monotypic Genus Dacnomys Endemic to Southeast Asia: New Insights into Genetic Diversity Erosion
by Zhongsong Wang, Di Zhao, Wenyu Song and Wenge Dong
Biology 2025, 14(8), 948; https://doi.org/10.3390/biology14080948 - 28 Jul 2025
Viewed by 242
Abstract
The Millard’s rat (Dacnomys millardi), a threatened murid endemic to Southeast Asian montane rainforests and the sole member of its monotypic genus, faces escalating endangered risks as a Near Threatened species in China’s Biodiversity Red List. This ecologically specialized rodent exhibits [...] Read more.
The Millard’s rat (Dacnomys millardi), a threatened murid endemic to Southeast Asian montane rainforests and the sole member of its monotypic genus, faces escalating endangered risks as a Near Threatened species in China’s Biodiversity Red List. This ecologically specialized rodent exhibits diagnostic morphological adaptations—hypertrophied upper molars and cryptic pelage—that underpin niche differentiation in undisturbed tropical/subtropical forests. Despite its evolutionary distinctiveness, the conservation prioritization given to Dacnomys is hindered due to a deficiency of data and unresolved phylogenetic relationships. Here, we integrated morphological analyses with the first complete mitogenome (16,289 bp in size; no structural rearrangements) of D. millardi to validate its phylogenetic placement within the subfamily Murinae and provide novel insights into genetic diversity erosion. Bayesian and maximum likelihood phylogenies robustly supported Dacnomys as sister to Leopoldamys (PP = 1.0; BS = 100%), with an early Pliocene divergence (~4.8 Mya, 95% HPD: 3.65–5.47 Mya). Additionally, based on its basal phylogenetic position within Murinae, we propose reclassifying Micromys from Rattini to the tribe Micromyini. Codon usage bias analyses revealed pervasive purifying selection (Ka/Ks < 1), constraining mitogenome evolution. Genetic diversity analyses showed low genetic variation (CYTB: π = 0.0135 ± 0.0023; COX1: π = 0.0101 ± 0.0025) in fragmented populations. We propose three new insights into this genetic diversity erosion. (1) Evolutionary constraints: genome-wide evolutionary conservation and shallow evolutionary history (~4.8 Mya) limited mutation accumulation. (2) Anthropogenic pressures: deforestation-driven fragmentation of habitats (>20,000 km2/year loss since 2000) has reduced effective population size, exacerbating genetic drift. (3) Ecological specialization: long-term adaptation to stable niches favored genomic optimization over adaptive flexibility. These findings necessitate suitable conservation action by enforcing protection of core habitats to prevent deforestation-driven population collapses and advocating IUCN reclassification of D. millardi from Data Deficient to Near Threatened. Full article
(This article belongs to the Section Genetics and Genomics)
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12 pages, 9023 KiB  
Article
The Impact of Vegetation Structure on Shaping Urban Avian Communities in Chaoyang District Beijing, China
by Anees Ur Rahman, Kamran Ullah, Shumaila Batool, Rashid Rasool Rabbani Ismaili and Liping Yan
Animals 2025, 15(15), 2214; https://doi.org/10.3390/ani15152214 - 28 Jul 2025
Viewed by 215
Abstract
This study examines the impact of vegetation structure on bird species richness and diversity across four urban parks in Chaoyang District, Beijing. Throughout the year, using the Point Count Method (PCM), a total of 68 bird species and 4279 individual observations were recorded, [...] Read more.
This study examines the impact of vegetation structure on bird species richness and diversity across four urban parks in Chaoyang District, Beijing. Throughout the year, using the Point Count Method (PCM), a total of 68 bird species and 4279 individual observations were recorded, with surveys conducted across all four seasons to capture seasonal variations. The parks with more complex vegetation, such as those with a higher tree canopy cover of species like poplars, ginkgo, and Chinese pines, exhibited higher bird species richness. For example, Olympic Forest Park, with its dense vegetation structure, hosted 42 species, whereas parks with less diverse vegetation supported fewer species. An analysis using PERMANOVA revealed that bird communities in the four parks were significantly different from each other (F = 2.76, p = 0.04075), and every comparison between parks showed significant differences as well (p < 0.001). Variations in the arrangement and level of disturbance within different plant communities likely cause such differences. Principal component analysis (PCA) identified tree canopy cover and shrub density as key drivers of bird diversity. These findings underscore the importance of preserving urban green spaces, particularly those with a diverse range of native tree species, to conserve biodiversity and mitigate the adverse effects of urbanisation. Effective vegetation management strategies can enhance avian habitats and provide ecological and cultural benefits in urban environments. Full article
(This article belongs to the Section Birds)
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22 pages, 1781 KiB  
Article
Analyzing Heart Rate Variability for COVID-19 ICU Mortality Prediction Using Continuous Signal Processing Techniques
by Guilherme David, André Lourenço, Cristiana P. Von Rekowski, Iola Pinto, Cecília R. C. Calado and Luís Bento
J. Clin. Med. 2025, 14(15), 5312; https://doi.org/10.3390/jcm14155312 - 28 Jul 2025
Viewed by 183
Abstract
Background/Objectives: Heart rate variability (HRV) has been widely investigated as a predictor of disease and mortality across diverse patient populations; however, there remains no consensus on the optimal set or combination of time and frequency domain nor on nonlinear features for reliable prediction [...] Read more.
Background/Objectives: Heart rate variability (HRV) has been widely investigated as a predictor of disease and mortality across diverse patient populations; however, there remains no consensus on the optimal set or combination of time and frequency domain nor on nonlinear features for reliable prediction across clinical contexts. Given the relevance of the COVID-19 pandemic and the unique clinical profiles of these patients, this retrospective observational study explored the potential of HRV analysis for early prediction of in-hospital mortality using ECG signals recorded during the initial moments of ICU admission in COVID-19 patients. Methods: HRV indices were extracted from four ECG leads (I, II, III, and aVF) using sliding windows of 2, 5, and 7 min across observation intervals of 15, 30, and 60 min. The raw data posed significant challenges in terms of structure, synchronization, and signal quality; thus, from an original set of 381 records from 321 patients, after data pre-processing steps, a final dataset of 82 patients was selected for analysis. To manage data complexity and evaluate predictive performance, two feature selection methods, four feature reduction techniques, and five classification models were applied to identify the optimal approach. Results: Among the feature aggregation methods, compiling feature means across patient windows (Method D) yielded the best results, particularly for longer observation intervals (e.g., using LDA, the best AUC of 0.82±0.13 was obtained with Method D versus 0.63±0.09 with Method C using 5 min windows). Linear Discriminant Analysis (LDA) was the most consistent classification algorithm, demonstrating robust performance across various time windows and further improvement with dimensionality reduction. Although Gradient Boosting and Random Forest also achieved high AUCs and F1-scores, their performance outcomes varied across time intervals. Conclusions: These findings support the feasibility and clinical relevance of using short-term HRV as a noninvasive, data-driven tool for early risk stratification in critical care, potentially guiding timely therapeutic decisions in high-risk ICU patients and thereby reducing in-hospital mortality. Full article
(This article belongs to the Section Cardiology)
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25 pages, 5461 KiB  
Article
Spaceborne LiDAR Reveals Anthropogenic and Biophysical Drivers Shaping the Spatial Distribution of Forest Aboveground Biomass in Eastern Himalayas
by Abhilash Dutta Roy, Abraham Ranglong, Sandeep Timilsina, Sumit Kumar Das, Michael S. Watt, Sergio de-Miguel, Sourabh Deb, Uttam Kumar Sahoo and Midhun Mohan
Land 2025, 14(8), 1540; https://doi.org/10.3390/land14081540 - 27 Jul 2025
Viewed by 205
Abstract
The distribution of forest aboveground biomass density (AGBD) is a key indicator of carbon stock and ecosystem health in the Eastern Himalayas, which represents a global biodiversity hotspot that sustains diverse forest types across an elevation gradient from lowland rainforests to alpine meadows [...] Read more.
The distribution of forest aboveground biomass density (AGBD) is a key indicator of carbon stock and ecosystem health in the Eastern Himalayas, which represents a global biodiversity hotspot that sustains diverse forest types across an elevation gradient from lowland rainforests to alpine meadows and contributes to the livelihoods of more than 200 distinct indigenous communities. This study aimed to identify the key factors influencing forest AGBD across this region by analyzing the underlying biophysical and anthropogenic drivers through machine learning (random forest). We processed AGBD data from the Global Ecosystem Dynamics Investigation (GEDI) spaceborne LiDAR and applied filtering to retain 30,257 high-quality footprints across ten ecoregions. We then analyzed the relationship between AGBD and 17 climatic, topographic, soil, and anthropogenic variables using random forest regression models. The results revealed significant spatial variability in AGBD (149.6 ± 79.5 Mg ha−1) across the region. State-wise, Sikkim recorded the highest mean AGBD (218 Mg ha−1) and Manipur the lowest (102.8 Mg ha−1). Within individual ecoregions, the Himalayan subtropical pine forests exhibited the highest mean AGBD (245.5 Mg ha−1). Topographic factors, particularly elevation and latitude, were strong determinants of biomass distribution, with AGBD increasing up to elevations of 2000 m before declining. Protected areas (PAs) consistently showed higher AGBD than unprotected forests for all ecoregions, while proximity to urban and agricultural areas resulted in lower AGBD, pointing towards negative anthropogenic impacts. Our full model explained 41% of AGBD variance across the Eastern Himalayas, with better performance in individual ecoregions like the Northeast India-Myanmar pine forests (R2 = 0.59). While limited by the absence of regionally explicit stand-level forest structure data (age, stand density, species composition), our results provide valuable evidence for conservation policy development, including expansion of PAs, compensating avoided deforestation and modifications in shifting cultivation. Future research should integrate field measurements with remote sensing and use high-resolution LiDAR with locally derived allometric models to enhance biomass estimation and GEDI data validation. Full article
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13 pages, 704 KiB  
Article
Population Substructures of Castanopsis tribuloides in Northern Thailand Revealed Using Autosomal STR Variations
by Patcharawadee Thongkumkoon, Jatupol Kampuansai, Maneesawan Dansawan, Pimonrat Tiansawat, Nuttapol Noirungsee, Kittiyut Punchay, Nuttaluck Khamyong and Prasit Wangpakapattanawong
Plants 2025, 14(15), 2306; https://doi.org/10.3390/plants14152306 - 26 Jul 2025
Viewed by 191
Abstract
This study investigates the genetic diversity and population structure of Castanopsis tribuloides, a vital tree species in Asian forest ecosystems. Understanding the genetic patterns of keystone forest species provides critical insights into forest resilience and ecosystem function and informs conservation strategies. We [...] Read more.
This study investigates the genetic diversity and population structure of Castanopsis tribuloides, a vital tree species in Asian forest ecosystems. Understanding the genetic patterns of keystone forest species provides critical insights into forest resilience and ecosystem function and informs conservation strategies. We analyzed population samples collected from three distinct locations within Doi Suthep Mountain in northern Thailand using Short Tandem Repeat (STR) markers to assess both intra- and inter-population genetic relationships. DNA was extracted from leaf samples and analyzed using a panel of polymorphic microsatellite loci specifically optimized for Castanopsis species. Statistical analyses included the assessment of forensic parameters (number of alleles, observed and expected heterozygosity, gene diversity, polymorphic information content), population differentiation metrics (GST), inbreeding coefficients (FIS), and gene flow estimates (Nm). We further examined population history through bottleneck analysis using three models (IAM, SMM, and TPM) and visualized genetic relationships through principal coordinate analysis and cluster analysis. Our results revealed significant patterns of genetic structuring across the sampled populations, with genetic distance metrics showing statistically significant differentiation between certain population pairs. The PCA and cluster analyses confirmed distinct population groupings that correspond to geographic distribution patterns. These findings provide the first comprehensive assessment of C. tribuloides population genetics in this region, establishing baseline data for monitoring genetic diversity and informing conservation strategies. This research contributes to our understanding of how landscape features and ecological factors shape genetic diversity patterns in essential forest tree species, with implications for managing forest genetic resources in the face of environmental change. Full article
(This article belongs to the Section Plant Genetic Resources)
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27 pages, 10737 KiB  
Article
XT-SECA: An Efficient and Accurate XGBoost–Transformer Model for Urban Functional Zone Classification
by Xin Gao, Xianmin Wang, Li Cao, Haixiang Guo, Wenxue Chen and Xing Zhai
ISPRS Int. J. Geo-Inf. 2025, 14(8), 290; https://doi.org/10.3390/ijgi14080290 - 25 Jul 2025
Viewed by 174
Abstract
The remote sensing classification of urban functional zones provides scientific support for urban planning, land resource optimization, and ecological environment protection. However, urban functional zone classification encounters significant challenges in accuracy and efficiency due to complicated image structures, ambiguous critical features, and high [...] Read more.
The remote sensing classification of urban functional zones provides scientific support for urban planning, land resource optimization, and ecological environment protection. However, urban functional zone classification encounters significant challenges in accuracy and efficiency due to complicated image structures, ambiguous critical features, and high computational complexity. To tackle these challenges, this work proposes a novel XT-SECA algorithm employing a strengthened efficient channel attention mechanism (SECA) to integrate the feature-extraction XGBoost branch and the feature-enhancement Transformer feedforward branch. The SECA optimizes the feature-fusion process through dynamic pooling and adaptive convolution kernel strategies, reducing feature confusion between various functional zones. XT-SECA is characterized by sufficient learning of complex image structures, effective representation of significant features, and efficient computational performance. The Futian, Luohu, and Nanshan districts in Shenzhen City are selected to conduct urban functional zone classification by XT-SECA, and they feature administrative management, technological innovation, and commercial finance functions, respectively. XT-SECA can effectively distinguish diverse functional zones such as residential zones and public management and service zones, which are easily confused by current mainstream algorithms. Compared with the commonly adopted algorithms for urban functional zone classification, including Random Forest (RF), Long Short-Term Memory (LSTM) network, and Multi-Layer Perceptron (MLP), XT-SECA demonstrates significant advantages in terms of overall accuracy, precision, recall, F1-score, and Kappa coefficient, with an accuracy enhancement of 3.78%, 42.86%, and 44.17%, respectively. The Kappa coefficient is increased by 4.53%, 51.28%, and 52.73%, respectively. Full article
(This article belongs to the Topic Artificial Intelligence Models, Tools and Applications)
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17 pages, 3481 KiB  
Article
Influence of Ziziphus lotus (Rhamnaceae) Plants on the Spatial Distribution of Soil Bacterial Communities in Semi-Arid Ecosystems
by Nabil Radouane, Zakaria Meliane, Khaoula Errafii, Khadija Ait Si Mhand, Salma Mouhib and Mohamed Hijri
Microorganisms 2025, 13(8), 1740; https://doi.org/10.3390/microorganisms13081740 - 25 Jul 2025
Viewed by 296
Abstract
Ziziphus lotus (L.) Lam. (Rhamnaceae), a key shrub species native to North Africa, is commonly found in arid and semi-arid regions. Renowned for its resilience under harsh conditions, it forms vegetation clusters that influence the surrounding environment. These clusters create microhabitats that promote [...] Read more.
Ziziphus lotus (L.) Lam. (Rhamnaceae), a key shrub species native to North Africa, is commonly found in arid and semi-arid regions. Renowned for its resilience under harsh conditions, it forms vegetation clusters that influence the surrounding environment. These clusters create microhabitats that promote biodiversity, reduce soil erosion, and improve soil fertility. However, in agricultural fields, Z. lotus is often regarded as an undesirable species. This study investigated the bacterial diversity and community composition along spatial gradients around Z. lotus patches in barley-planted and non-planted fields. Using 16S rRNA gene sequencing, 84 soil samples were analyzed from distances of 0, 3, and 6 m from Z. lotus patches. MiSeq sequencing generated 143,424 reads, representing 505 bacterial ASVs across 22 phyla. Alpha-diversity was highest at intermediate distances (3 m), while beta-diversity analyses revealed significant differences in community composition across distances (p = 0.035). Pseudomonadota dominated close to the shrub (44% at 0 m) but decreased at greater distances, whereas Bacillota and Actinobacteriota displayed distinct spatial patterns. A core microbiome comprising 44 ASVs (8.7%) was shared across all distances, with the greatest number of unique ASVs identified at 3 m. Random forest analysis highlighted Skermanella and Rubrobacter as key discriminatory taxa. These findings emphasize the spatial structuring of bacterial communities around Z. lotus patches, demonstrating the shrub’s substantial influence on bacterial dynamics in arid ecosystems. Full article
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18 pages, 2429 KiB  
Article
Conserved and Specific Root-Associated Microbiome Reveals Close Correlation Between Fungal Community and Growth Traits of Multiple Chinese Fir Genotypes
by Xuan Chen, Zhanling Wang, Wenjun Du, Junhao Zhang, Yuxin Liu, Liang Hong, Qingao Wang, Chuifan Zhou, Pengfei Wu, Xiangqing Ma and Kai Wang
Microorganisms 2025, 13(8), 1741; https://doi.org/10.3390/microorganisms13081741 - 25 Jul 2025
Viewed by 259
Abstract
Plant microbiomes are vital for the growth and health of their host. Tree-associated microbiomes are shaped by multiple factors, of which the host is one of the key determinants. Whether different host genotypes affect the structure and diversity of the tissue-associated microbiome and [...] Read more.
Plant microbiomes are vital for the growth and health of their host. Tree-associated microbiomes are shaped by multiple factors, of which the host is one of the key determinants. Whether different host genotypes affect the structure and diversity of the tissue-associated microbiome and how specific taxa enriched in different tree tissues are not yet well illustrated. Chinese fir (Cunninghamia lanceolata) is an important tree species for both economy and ecosystem in the subtropical regions of Asia. In this study, we investigated the tissue-specific fungal community structure and diversity of nine different Chinese fir genotypes (39 years) grown in the same field. With non-metric multidimensional scaling (NMDS) analysis, we revealed the divergence of the fungal community from rhizosphere soil (RS), fine roots (FRs), and thick roots (TRs). Through analysis with α-diversity metrics (Chao1, Shannon, Pielou, ACE, Good‘s coverage, PD-tree, Simpson, Sob), we confirmed the significant difference of the fungal community in RS, FR, and TR samples. Yet, the overall fungal community difference was not observed among nine genotypes for the same tissues (RS, FR, TR). The most abundant fungal genera were Russula in RS, Scytinostroma in FR, and Subulicystidium in TR. Functional prediction with FUNGuild analysis suggested that ectomycorrhizal fungi were commonly enriched in rhizosphere soil, while saprotroph–parasite and potentially pathogenic fungi were more abundant in root samples. Specifically, genotype N104 holds less ectomycorrhizal and pathogenic fungi in all tissues (RS, FR, TR) compared to other genotypes. Additionally, significant correlations of several endophytic fungal taxa (Scytinostroma, Neonothopanus, Lachnum) with the growth traits (tree height, diameter, stand volume) were observed. This addresses that the interaction between tree roots and the fungal community is a reflection of tree growth, supporting the “trade-off” hypothesis between growth and defense in forest trees. In summary, we revealed tissue-specific, as well as host genotype-specific and genotype-common characters of the structure and functions of their fungal communities. Full article
(This article belongs to the Special Issue Rhizosphere Microbial Community, 4th Edition)
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20 pages, 8029 KiB  
Article
Fire-Induced Floristic and Structural Degradation Across a Vegetation Gradient in the Southern Amazon
by Loriene Gomes da Rocha, Ben Hur Marimon Junior, Amauri de Castro Barradas, Marco Antônio Camillo de Carvalho, Célia Regina Araújo Soares, Beatriz Schwantes Marimon, Gabriel H. P. de Mello Ribeiro, Edmar A. de Oliveira, Fernando Elias, Carmino Emidio Júnior, Dennis Rodrigues da Silva, Marcos Leandro Garcia, Jesulino Alves da Rocha Filho, Marcelo Zortea, Edmar Santos Moreira, Samiele Camargo de Oliveira Domingues, Eraldo A. T. Matricardi, David Galbraith, Ted R. Feldpausch, Imma Oliveras and Oliver L. Phillipsadd Show full author list remove Hide full author list
Forests 2025, 16(8), 1218; https://doi.org/10.3390/f16081218 - 24 Jul 2025
Viewed by 276
Abstract
Climate change and landscape fragmentation have made fires the primary drivers of forest degradation in Southern Amazonia. Understanding their impacts is crucial for informing public conservation policies. In this study, we assessed the effects of repeated fires on trees with a diameter ≥10 [...] Read more.
Climate change and landscape fragmentation have made fires the primary drivers of forest degradation in Southern Amazonia. Understanding their impacts is crucial for informing public conservation policies. In this study, we assessed the effects of repeated fires on trees with a diameter ≥10 cm across three distinct vegetation types in this threatened region: Amazonian successional forest (SF), transitional forest (TF), and ombrophilous forest (OF). Two anthropogenic fires affected all three vegetation types in consecutive years. We hypothesized that SF would be the least impacted due to its more open structure and the presence of fire-adapted savanna (Cerrado) species. As expected, SF experienced the lowest tree mortality rate (9.1%). However, both TF and OF were heavily affected, with mortality rates of 28.0% and 29.7%, respectively. Despite SF’s apparent fire resilience, all vegetation types experienced a significant net loss of species and individuals. These results indicate a fire-induced degradation stage in both TF and OF, characterized by reduced species diversity and structural integrity. Our findings suggest that recurrent fires may trigger irreversible vegetation shifts and broader ecosystem tipping points across the Amazonian frontier. Full article
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20 pages, 437 KiB  
Article
A Copula-Driven CNN-LSTM Framework for Estimating Heterogeneous Treatment Effects in Multivariate Outcomes
by Jong-Min Kim
Mathematics 2025, 13(15), 2384; https://doi.org/10.3390/math13152384 - 24 Jul 2025
Viewed by 379
Abstract
Estimating heterogeneous treatment effects (HTEs) across multiple correlated outcomes poses significant challenges due to complex dependency structures and diverse data types. In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Convolutional Neural Networks and Long [...] Read more.
Estimating heterogeneous treatment effects (HTEs) across multiple correlated outcomes poses significant challenges due to complex dependency structures and diverse data types. In this study, we propose a novel deep learning framework integrating empirical copula transformations with a CNN-LSTM (Convolutional Neural Networks and Long Short-Term Memory networks) architecture to capture nonlinear dependencies and temporal dynamics in multivariate treatment effect estimation. The empirical copula transformation, a rank-based nonparametric approach, preprocesses input covariates to better represent the underlying joint distributions before modeling. We compare this method with a baseline CNN-LSTM model lacking copula preprocessing and a nonparametric tree-based approach, the Causal Forest, grounded in generalized random forests for HTE estimation. Our framework accommodates continuous, count, and censored survival outcomes simultaneously through a multitask learning setup with customized loss functions, including Cox partial likelihood for survival data. We evaluate model performance under varying treatment perturbation rates via extensive simulation studies, demonstrating that the Empirical Copula CNN-LSTM achieves superior accuracy and robustness in average treatment effect (ATE) and conditional average treatment effect (CATE) estimation. These results highlight the potential of copula-based deep learning models for causal inference in complex multivariate settings, offering valuable insights for personalized treatment strategies. Full article
(This article belongs to the Special Issue Current Developments in Theoretical and Applied Statistics)
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24 pages, 11000 KiB  
Article
Differences and Influencing Factors of Soil Bacterial Communities Under Different Forest Types on the Southern Slope of the Qilian Mountains
by Shuang Ji, Huichun Xie, Shaobo Du, Shaoxiong Zhang, Zhiqiang Dong, Hongye Li and Xunxun Qiu
Biology 2025, 14(8), 927; https://doi.org/10.3390/biology14080927 - 23 Jul 2025
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Abstract
Understanding the distribution patterns of soil bacterial community structure and diversity across different forest types is essential for elucidating the mechanisms underlying microbial community assembly and its ecological drivers, particularly under the pressures of climate change. In this study, we examined six forest [...] Read more.
Understanding the distribution patterns of soil bacterial community structure and diversity across different forest types is essential for elucidating the mechanisms underlying microbial community assembly and its ecological drivers, particularly under the pressures of climate change. In this study, we examined six forest types—including four monocultures and two mixed-species stands—to systematically evaluate the structural composition, diversity metrics, and functional potential of soil bacterial communities. Significant differences in microbial structure and functional composition were observed among forest types. Mixed forests exhibited higher soil nutrient levels, more complex structures, and greater water retention capacity, resulting in significantly higher bacterial and functional diversity compared to monoculture forests. Bacterial diversity was greater in subsurface layers than in surface layers. Surface communities in monoculture forests showed relatively high structural heterogeneity, whereas deeper communities in mixed forests displayed more pronounced differentiation. The dominant bacterial phyla were mainly related to carbon and nitrogen metabolism, compound degradation, and anaerobic photosynthesis. Surface bacterial communities were primarily influenced by catalase activity, alkali-hydrolysable nitrogen, bulk density, and pH, whereas subsurface communities were largely controlled by pH, with supplementary regulation by nitrogen and potassium availability. Therefore, forest type and soil depth jointly influence the diversity, composition, and functional attributes of soil microbial communities by modulating soil physicochemical conditions. Full article
(This article belongs to the Section Microbiology)
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