Search Results (3,406)

We conducted a systematic literature review, following PRISMA guidelines, to assess whether existing species distribution models for Aedes vexans reflect its known ecological requirements. This mosquito is closely associated with temporary floodwaters, making hydrological dynamics a critical factor for accurate modelling. From 28 peer-reviewed studies, we extracted 477 environmental and ecological variables and organized them into a hierarchical classification scheme with four main categories: weather, land characteristics, water characteristics, and population. We analysed patterns in variable usage and the reported importance of each variable. Our results show that flood-related variables were largely absent, despite the species’ reliance on ephemeral water bodies for reproduction. This may possibly reduce the predictive utility of existing Aedes vexans species distribution models. In contrast, urban-landscape variables were frequently used and often ranked as highly predictive, but such results were primarily found in studies that did not account for sampling bias. Full article

Recently, the nominal species Atractus meridensis was considered a junior synonym of Atractus erythromelas, both species being endemic to the cordillera de Mérida in Venezuela. Here, its taxonomic status is resolved through the examination of the original descriptions, available type specimens, and an extensive comparison of additional museum material. In this review, we provide a new morphological–phylogenetic approach regarding its diagnosis and description for each species, including a model of its current distribution, which demonstrates its allopatric distribution. Both sleepyhead snakes are considered mimetic, but A. erythromelas exhibits a polymorphic color pattern with at least five known morphs, while A. meridensis is dichromatic with only two known color patterns. According to the IUCN, both taxa should be included in the Vulnerable category according to criteria B2abi,ii,iii,iv. Full article

Recent advances in hyperspectral imaging (HSI) and multimodal deep learning have opened new opportunities for crop health analysis; however, most existing models remain limited by dataset scope, lack of interpretability, and weak cross-domain generalization. To overcome these limitations, this study introduces Agri-DSSA, a novel Dual Self-Supervised Attention (DSSA) framework that simultaneously models spectral and spatial dependencies through two complementary self-attention branches. The proposed architecture enables robust and interpretable feature learning across heterogeneous data sources, facilitating the estimation of spectral proxies of chlorophyll content, plant vigor, and disease stress indicators rather than direct physiological measurements. Experiments were performed on seven publicly available benchmark datasets encompassing diverse spectral and visual domains: three hyperspectral datasets (Indian Pines with 16 classes and 10,366 labeled samples; Pavia University with 9 classes and 42,776 samples; and Kennedy Space Center with 13 classes and 5211 samples), two plant disease datasets (PlantVillage with 54,000 labeled leaf images covering 38 diseases across 14 crop species, and the New Plant Diseases dataset with over 30,000 field images captured under natural conditions), and two chlorophyll content datasets (the Global Leaf Chlorophyll Content Dataset (GLCC), derived from MERIS and OLCI satellite data between 2003–2020, and the Leaf Chlorophyll Content Dataset for Crops, which includes paired spectrophotometric and multispectral measurements collected from multiple crop species). To ensure statistical rigor and spatial independence, a block-based spatial cross-validation scheme was employed across five independent runs with fixed random seeds. Model performance was evaluated using $R^2$, RMSE, F1-score, AUC-ROC, and AUC-PR, each reported as mean ± standard deviation with 95% confidence intervals. Results show that Agri-DSSA consistently outperforms baseline models (PLSR, RF, 3D-CNN, and HybridSN), achieving up to $R^2=0.86$ for chlorophyll content estimation and F1-scores above 0.95 for plant disease detection. The attention distributions highlight physiologically meaningful spectral regions (550–710 nm) associated with chlorophyll absorption, confirming the interpretability of the model’s learned representations. This study serves as a methodological foundation for UAV-based and field-deployable crop monitoring systems. By unifying hyperspectral, chlorophyll, and visual disease datasets, Agri-DSSA provides an interpretable and generalizable framework for proxy-based vegetation stress estimation. Future work will extend the model to real UAV campaigns and in-field spectrophotometric validation to achieve full agronomic reliability. Full article

Understanding the patterns and drivers of species distribution has remained a central theme for biogeographical, conservation, and ecological research. This study aims to investigate the elevational patterns of plant species richness and compare the observed species richness with the predictions of the mid-domain effect (MDE) null model. By combining information from field observations and the published literature, we compiled a comprehensive database of the elevational distribution of plant species for three protected areas in the Western Himalayas. We used generalised linear model (GLM) and null model simulations to explore the elevational patterns of plant species richness. Our study revealed simple linear to complex non-linear patterns depending on the location and range of the elevational gradient. While non-linear unimodal patterns were common, a linear decreasing pattern was also observed. The observed species richness showed consistent deviations from the predictions of the mid-domain effect null model, suggesting that factors beyond the range constraints shape species richness patterns. These observations indicate that richness patterns are not solely generated by random processes, rather climatic gradients, ecological interactions, and topographic heterogeneity can shape these patterns. Understanding these factors can aid in predicting and managing the impacts of ongoing environmental changes on Himalayan biodiversity. Full article

Global climate change is accelerating and human pressures are intensifying, exerting profound impacts on biodiversity and ecosystem service functions. The accurate prediction of species distributions has thus become a critical research direction in ecological conservation and restoration. This study selected Puccinellia tenuiflora, a species distributed across China, as its research subject. Utilizing 169 occurrence records and 10 environmental variables, we applied a parameter-optimized MaxEnt model to simulate the species’ current and future (2050s–2090s) potential suitable habitats under the SSP126, SSP370, and SSP585 scenarios. The results identified the human footprint index (HFI, 43.3%) and temperature seasonality (Bio4, 26.9%) as the dominant factors influencing its distribution. The current suitable area is primarily concentrated in northern China, covering approximately 258.26 × 10⁴ km². Under all future scenarios, a contraction of suitable habitat is projected, with the most significant reduction observed under SSP585 by the 2090s (a decrease of 56.2%). The distribution centroid is projected to shift northeastward by up to 145.36 km. This study elucidates the response mechanism of P. tenuiflora distribution to climate change and human activities. The projected habitat contraction and spatial displacement highlight the potential vulnerability of this species to future climate change. These findings, derived from a rigorously optimized and spatially validated model, provide a scientific basis for the conservation, reintroduction, and adaptive management of P. tenuiflora under climate change. Full article

Leptospirosis is a globally distributed zoonotic disease caused by pathogenic bacteria of the Leptospira genus. Genome editing in Leptospira has been difficult to perform. Currently, the functionality of the CRISPR-Cas system has been demonstrated in species such as Leptospira interrogans. However, the different CRISPR-Cas systems present in most of the 77 species are unknown. Therefore, the objective of this study was to identify these arrays across the genomes of all described Leptospira species using bioinformatics tools. Methods: a bioinformatics workflow was followed: genomes were downloaded from the NCBI database; Cas protein detection was carried out using the CRISPR-CasFinder and RAST web servers; functional analyses of Cas proteins were performed with InterProScan, ProtParam, Swiss Model, Alphafold3, Swiss PDB Viewer, and Pymol; conservation pattern detection was conducted using MEGA12, and Seqlogos; spacer identification was carried out with the Actinobacteriophages database and BLAST version 1.4.0; and bacteriophage detection was performed using PHASTER, and PHASTEST. Results: Cas proteins were detected in 36 out of the 77 species of the Leptospira species, including Cas1 to Cas9 and Cas12. These proteins were classified into Class 1 and Class 2 systems, corresponding to types I, II, and V. Direct repeats and spacers were detected in 19 species, with the direct repeats exhibiting two conserved nucleotide motifs. Analysis of spacer sequences revealed 323 distinct bacteriophages. Additionally, three intact bacteriophages were detected in the genomes of four Leptospira species. Notably, two saprophytic species have complete CRISPR-Cas systems. Conclusions: The presence of Cas proteins, direct repeats, and spacer sequences with homology to bacteriophage genomes provides evidence for a functional CRISPR-Cas system in at least 19 species. Full article

Black rhinoceros (Diceros bicornis) face threats to survival due to poaching in the wild and an incomplete understanding of preventive health monitoring under human care. Black rhinos under human care develop iron overload disorder (IOD) which is associated with predisposition to other disease, compromised immune function, hemolytic crisis, and death. Management of IOD is challenging but has been mitigated in some cases with dietary intervention and phlebotomy and documented through serum biomarker evaluation. Chelation therapy to reduce iron is rare in rhinos partially because of limited product availability and route of administration. An iron-specific chelator HBED (N,N’-Di(2-hydroxybenzyl)ethylenediamine-N,N’-diacetic acid) was investigated for oral use in southern black rhinos (n = 3) after successful testing was performed with equids as a model. Using a cross-over design, we tested the efficacy of short-term HBED administration. HBED was dosed at 40 mg/kg body weight for 10 days and resulted in increased urinary excretion of iron but unaltered fecal iron excretion in rhinos compared to control trials. Two rhinos maintained blood chemistry and cell distribution considered normal for the species. The third rhino experienced a hemolytic event after stopping HBED administration in the same time frame and at the same dose as the two conspecifics but fully recovered. Careful monitoring and tapering the drug at the completion of treatment is warranted, especially if the rhino’s iron load is considered high. HBED’s potential to induce iron excretion safely, as well as prevent excessive dietary iron uptake, may significantly benefit the black rhino population under human care. Full article

When a gas is overvolted at or near atmospheric pressure, it results in a streamer discharge formation. Electrode geometries exert significant impact on the electrical breakdown of gases by altering the spatial profile of the electric field. In many applications the efficient generation of radicals is critical and is determined by the characteristics of the streamer discharge. We examine the effect of electrode geometry on the streamer characteristics and the production of radicals. This is performed for three different electrode geometries: plane–plane, pin–plane, and pin–pin. A two-dimensional rotationally symmetric fluid model is used for the streamer discharge simulation in the hydrogen/air gas mixture. The spatial profile of electron density and the electric field for point electrodes show significant differences when compared to plane electrodes. However, the efficiency of radical generation shows similar trends for the electrode configurations studied. We also present the results of spatial electrical energy density distribution which in turn determines spatial excited species distribution. These results can inform the design of specific applications. Full article

A multiscale 3D CFD model of CO₂ methanation over Ni/Al₂O₃ was developed in COMSOL Multiphysics 6.3 for a lab-scale isothermal fixed-bed Sabatier reactor and validated against published data. The multiscale approach integrated bulk convection–diffusion, fluid flow, and pressure distribution with intraparticle diffusion–reaction phenomena coupled with Langmuir–Hinshelwood–Hougen–Watson-based kinetics, thus solving mass-transfer limitations without empirical effectiveness factors. Model validation was carried out by (i) kinetics, (ii) reactor performance, and (iii) hydrodynamics. Simulation results showed strong diffusion-dominated species transport at the bed entrance that lessened downstream as partial pressures decreased and products accumulated, resulting in a diffusion-relieved regime near the outlet. Sensitivity studies identified 320–350 °C and up to 10 bar as favorable conditions for high CH₄ yield. Additionally, slightly H₂-rich feed accelerated approach to equilibrium, while lower flow rates achieved near-complete conversion within the first half of the reactor bed. Simulations were carried out in COMSOL Multiphysics 6.3 on a dual Intel Xeon Platinum 8168 (48 cores at 2.7 GHz) workstation with 512 GB RAM to solve a 12-million-element mesh. The developed framework identifies a practical operating window and quantifies the conversion–throughput trade-off with flow rate, guiding operating condition selection and providing a basis for process intensification and lab-to-pilot scale-up of CO₂ methanation. Full article

Urbanization imposes complex environmental gradients that threaten plant diversity and urban ecosystem integrity. Understanding the multifactorial drivers that govern species distribution in urban contexts is essential for biodiversity conservation and sustainable landscape planning. This study addresses this challenge by examining the environmental determinants of urban flora in a rapidly developing city. We integrated data from 397 floristic sampling sites and 13 environmental monitoring locations across Düzce, Türkiye. A multidimensional suite of environmental predictors—including microclimatic variables (soil temperature, moisture, light), edaphic properties (pH, EC (Electrical Conductivity), texture, carbonate content), precipitation chemistry (pH and major ions), macroclimatic parameters (CHELSA bioclimatic variables), and spatial metrics (elevation, proximity to urban and natural features)—was analyzed using nonlinear regression models and machine learning algorithms (RF (Random Forest), XGBoost, and SVR (Support Vector Regression)). Shannon diversity exhibited strong variation across land cover types, with the highest values in broad-leaved forests and pastures (>3.0) and lowest in construction and mining zones (<2.3). Species richness and evenness followed similar spatial trends. Evenness peaked in semi-natural habitats such as agricultural and riparian areas (~0.85). Random Forest outperformed other models in predictive accuracy. Elevation was the most influential predictor of Shannon diversity, while proximity to riparian zones best explained richness and evenness. Chloride concentrations in rainfall were also linked to species composition. When the models were recalibrated using only native species, they exhibited consistent patterns and maintained high predictive performance (Shannon R² ≈ 0.937474; Richness R² ≈ 0.855305; Evenness R² ≈ 0.631796). Full article

To investigate the geographical and species differences regarding mineral element content of camel milk, this research used camel milk from the Tacheng, Altay, and Ili regions of Xinjiang and cow milk, goat milk, and horse milk from the Tacheng region as subjects. The contents of 22 mineral elements were measured using inductively coupled plasma mass spectrometry (ICP-MS) and inductively coupled plasma optical emission spectrometry (ICP-OES). The results showed that the contents of macro elements Ca, P, K, and Na in camel milk were significantly higher than those in other milk sources (p < 0.01). The contents of trace elements such as Se, Sr, and Ni were very significantly higher than those in other milk sources (p < 0.01). The content of 12 mineral elements in camel milk was very significantly higher than in other types of milk (p < 0.01). Principal component analysis (PCA) and factor analysis emphasized the relationship between element distribution and different milk sources, and the linear discriminant analysis (LDA) model could identify the species type of milk. Geographical analysis indicated that trace elements such as Sr, Ni, and Cr were highly significantly enriched in Tacheng camel milk (p < 0.01). The established LDA model achieved traceability of the geographical origin of Xinjiang camel milk. This research reveals the mineral nutritional advantages of camel milk and its geographical differentiation patterns, providing theoretical support for exploring the functional properties of camel milk and for identifying species and regions through minerals. It is important to promote the upgrading of the specialty dairy product industry. Full article

The Larimichthys crocea represents a critically important economic marine species in China’s East Yellow Sea. However, its populations have experienced significant decline due to overexploitation. Despite implemented conservation measures—including stock enhancement, spawning ground protection, and seasonal fishing moratoria—the recovery of yellow croaker resources remains markedly slow. To address this, our study employed the Maximum Entropy (MaxEnt) model to evaluate and characterize the habitat selection patterns of Larimichthys crocea, thereby providing a theoretical foundation for scientifically informed stock enhancement and resource recovery strategies. Species occurrence data were compiled from field surveys conducted during April and November (2019–2023), supplemented with records from the GBIF database and peer-reviewed literature. Concurrent environmental variables, including primary productivity, current velocity, depth, temperature, salinity, silicate, nitrate, phosphate, and pH, were obtained from the Copernicus and NOAA databases. After rigorous screening, 136 distribution points (April) and 369 points (November) were retained for analysis. The model performance was robust, with an AUC (Area Under the Curve) value of 0.935 for April (2019–2023) and 0.905 for November (2019–2023), indicating excellent predictive accuracy (AUC > 0.9). April (2019–2023): Nitrate, salinity, phosphate, and silicate were identified as the primary environmental factors influencing habitat suitability. November (2019–2023): Silicate, salinity, nitrate, and primary productivity emerged as the dominant drivers. Spatially, Larimichthys crocea exhibited high-density distributions in offshore regions of Zhejiang and Jiangsu, particularly near the Yangtze River estuary. Populations were also associated with island-reef systems, forming continuous distributions along Zhejiang’s offshore waters. In Jiangsu, aggregations were concentrated between Nantong and Yancheng. This study delineates habitat suitability zones for Larimichthys crocea, offering a scientific basis for optimizing stock enhancement programs, designing targeted conservation measures, and establishing marine protected areas. Our findings enable policymakers to develop sustainable fisheries management strategies, ensuring the long-term viability of this ecologically and economically vital species. Full article

The present study aimed to establish a physiologically based toxicokinetic (PBTK) model to investigate the absorption, retention, and transport of inhaled nano-sized titanium dioxide (TiO₂-NPs) particles in rats, thereby providing a basis for understanding the absorption, distribution, and elimination mechanisms of TiO₂-NPs in various organs. A detailed respiratory module and the Hill coefficient equation were adopted in the PBTK model. Calibration and validation of the model were conducted using the only two available inhalation biodistribution datasets for TiO₂-NPs found in the literature, encompassing different doses and exposure conditions. The overall fit with both datasets was acceptable with R² value of 0.95 in respiratory system and 0.88 in the secondary organs. The sensitivity analysis indicated that the alveolar–interstitial transfer rate (K_{alv_inter}) and tissue–blood distribution coefficients (P_lu, P_li, P_ki) significantly influenced the retention of TiO₂-NPs in pulmonary regions and distribution to secondary organs, with these parameters exhibiting time-dependent behavior. The PBTK model demonstrates a good predictive performance for TiO₂-NPs content in all rat organs, with simulated values consistently ranging within 0.5- to 2-fold of the measured data. In last, we developed a PBTK model that can well predict the in vivo distribution of inhaled TiO₂-NPs and provided a novel computational tool for cross-species extrapolation of human inhalation exposure and subsequent biodistribution. Full article

The desert riparian forest oasis, dominated by Populus euphratica and Tamarix chinensis, is an important barrier to protect the economic production and habitat of the Tarim River Basin. However, there is still a lack of high-precision spatial distribution data of desert ri-parian forest species below 10 m. The recently launched PlanetScope CubeSat constella-tion, which provides daily earth observation imagery with a resolution of 3 m, offers a highly favorable dataset for mapping the high-resolution distribution of P. euphratica and T. chinensis and an unprecedented opportunity to explore the optimal phenology window to distinguish between them. In this study, time-series PlanetScope images were first used to extract phenological metrics of P. euphratica, dividing the annual life cycle into four phenology windows: duration of leaf expansion (DLE), duration of leaf maturity (DLM), duration of leaf fall (DLF), and duration of the dormancy period (DDP). The random forest model was used to obtain the classification accuracy of 16 phenological window combinations. Results indicate that after gap filling of vegetation index time series, the identification accuracy for P. euphratica and T. chinensis exceeded 0.90. Among individual phenology windows, the DLE window exhibited the highest classification accuracy (average F1-score 0.87). Among the two phenology window combinations, the DLE-DLF and DLE-DLM windows have the highest classification accuracy (average F1-score 0.90). Among the three phenology window combinations, DLE-DLM-DLF displayed the highest classification accuracy (average F1-score 0.91). Nevertheless, the inclusion of features within the DDP window led to a decrease in accuracy by 1–2% points, which was unfavorable for discriminating tree species. Additionally, features observed during the phenology asynchrony period were found to be more valuable for distinguishing between tree species. Our findings highlight the potential of PlanetScope constellation imagery in tree species classification, offering guidance for selecting optimal image acquisition timing and identifying the most valuable images within time series data for future large-scale tree mapping. Full article

This study investigates how climate gradients shape tick–host associations, testing the hypothesis that variations in climate leverage some associations, which can be ecosystem-specific. To test this hypothesis, we modelled tick–host associations across the Western Palearctic using climatic variables and a large dataset of georeferenced tick (seven species, n = 23,462) and vertebrate host records (n = 6.5 million across 162 species aggregated into 50 genera). Niche overlap with hosts is highly variable but consistently significant (p < 0.05) in every tested ecosystem of the target territory. Montane grasslands exhibit the lowest values of tick–host niche overlap, meaning that they support the smallest but still resilient set of available hosts. Host phylogenetic diversity (PD) depends on the ecosystem rather than tick species; PD is lowest in montane grasslands (supporting previous results) and in the case of D. reticulatus in savannas and scrubland. Nestedness of tick–host networks, known to be related to the resilience of parasite–host networks, is highest in climatically extreme ecosystems, reflecting adaptability of tick–host networks, as measured by niche overlap on modelled distribution. Multidimensional scaling confirms that host community composition and niche overlap vary significantly across ecosystems, supporting the hypothesis of host rewiring under diverse climatic conditions. These findings may have important implications for the concept of community composition and the circulation of tick-borne pathogens. Full article