Search Results (29)

Glaciers are significant sources of fresh water on planet Earth. The Hindukush–Karakoram–Himalayan (HKH) glaciers provide the water supply to more than half of the human population of the globe, for agricultural activities, biodiversity survival, and ecosystem services. In recent years, the loss of glacial ice has been forecasted to cause problems such as sea level rise, changes in water availability, and release of contaminants that reside in the surfaces of glaciers or within them. In this regard, mineralogical sediments play a significant role in the geochemistry of glaciers and element cycling. This study analyzed elemental pollutants found in the glaciers of Pakistan and investigated the diverse bacterial communities residing therein. Samples of ice and sediments were collected from the Gilgit, Hunza, and Swat glaciers in northern Pakistan. Nine elements, including co-factors, heavy metals, and nutrients, were assessed using atomic absorption spectrophotometry. The research findings indicate higher concentrations of the elements K, Fe, Cu, and Cr in Hunza glacier ice (Hgi) and Ni, Zn, As, and Cd in Gilgit glacier ice (Ggi). In terms of glacier sediments, Swat (Sgs), Gilgit (Ggs), and Hunza (Hgs) samples showed the highest concentrations of K, Cu, Ni, Zn, As, Pb, Cd, and, respectively, of Fe, and Cr. The amount of Cu and Cr is the same in Swat glacier ice and Swat glacier foot. However, the concentration of some elements (As, K, Pb, Zn) is higher in Swat glacier ice, while the amount of some elements (Cd, Ni) is greater in Swat glacier foot. Furthermore, microbial cultivation techniques revealed diverse bacterial communities inhabiting the sampled glaciers. Phylogenetic analysis of the bacterial isolates, based on 16S rRNA gene sequences, showed high homology (99–100%) with previously reported species. The resultant phylogenetic tree grouped the bacterial isolates, such as Serratia marcescens, Cupriavidus sp., and Bacillus cereus, with closely related species known for their roles in nutrient cycling, environmental resilience, and metal tolerance. These findings highlight the ecological significance and adaptive potential of microbial communities in glacier environments, emphasizing their role in elemental cycling and environmental resilience. Full article

Background/Objectives: Ants are one of the most abundant animal groups on the planet and have a considerable impact on ecosystems. In the Cabo Verde Archipelago, the study of invertebrates is very scarce and ants are no exception. Methods: In this work we focus on the taxonomic analysis of formicids and study their distribution and the possible presence of invasive species in the Cabo Verde Islands. In addition, the diversity of Cabo Verde ants is compared with that of the closest African coastal countries, Senegal and Mauritania, to study a possible colonization of African ants into the archipelago. For this, we use two molecular markers, cytochrome oxidase I and the wingless gene, to perform phylogenetic analyses and haplotype networks that facilitate identification. Results: Nine taxa were identified, five invasive species, Paratrechina longicornis, Pheidole megacephala, Trichomyrmex destructor, Brachyponera sennaarensis, and Solenopsis globularia, one endemic Monomorium subopacum and three unidentified species of native genera, Monomorium sp., Lepisiota sp. Camponotus sp. Conclusions: Molecular network patterns as well as phylogenetic analyses suggest that ants are widespread throughout the archipelago, a likely consequence of human introductions. Full article

The precise identification and classification of tree species in young forests during their early development stages are vital for forest management and silvicultural efforts that support their growth and renewal. However, achieving accurate geolocation and species classification through field-based surveys is often a labor-intensive and complicated task. Remote sensing technologies combined with machine learning techniques present an encouraging solution, offering a more efficient alternative to conventional field-based methods. This study aimed to detect and classify young forest tree species using remote sensing imagery and machine learning techniques. The study mainly involved two different objectives: first, tree species detection using the latest version of You Only Look Once (YOLOv12), and second, semantic segmentation (classification) using random forest, Categorical Boosting (CatBoost), and a Convolutional Neural Network (CNN). To the best of our knowledge, this marks the first exploration utilizing YOLOv12 for tree species identification, along with the study that integrates digital aerial photogrammetry with Planet imagery to achieve semantic segmentation in young forests. The study used two remote sensing datasets: RGB imagery from unmanned aerial vehicle (UAV) ortho photography and RGB-NIR from PlanetScope. For YOLOv12-based tree species detection, only RGB from ortho photography was used, while semantic segmentation was performed with three sets of data: (1) Ortho RGB (3 bands), (2) Ortho RGB + canopy height model (CHM) + Planet RGB-NIR (8 bands), and (3) ortho RGB + CHM + Planet RGB-NIR + 12 vegetation indices (20 bands). With three models applied to these datasets, nine machine learning models were trained and tested using 57 images (1024 × 1024 pixels) and their corresponding mask tiles. The YOLOv12 model achieved 79% overall accuracy, with Scots pine performing best (precision: 97%, recall: 92%, mAP50: 97%, mAP75: 80%) and Norway spruce showing slightly lower accuracy (precision: 94%, recall: 82%, mAP50: 90%, mAP75: 71%). For semantic segmentation, the CatBoost model with 20 bands outperformed other models, achieving 85% accuracy, 80% Kappa, and 81% MCC, with CHM, EVI, NIRPlanet, GreenPlanet, NDGI, GNDVI, and NDVI being the most influential variables. These results indicate that a simple boosting model like CatBoost can outperform more complex CNNs for semantic segmentation in young forests. Full article

Over the past quarter-century, the enhanced availability of satellite imagery, characterized by improved temporal, spectral, radiometric, and spatial resolutions, has enabled valuable insights into the spatial soil variability of annual croplands and orchards. This study investigates the impact of spatial resolution on classifying three-year, multi-temporal vegetation indices derived from satellites with coarse (30 m, Landsat 8), medium (10 m, Sentinel-2), and fine spatial resolutions (3.7 m, PlanetScope). The classification was performed using the fuzzy c-means algorithm, with the fuzziness performance index (FPI) and normalized classification entropy (NCE), which were used to determine the optimal number of management zones (MZs). Our results revealed that the Landsat 8-based NDVI images produced the highest number of clusters (nine for annual cropland and six for orchards), while the finer resolutions from PlanetScope reduced this to three clusters for both cultivation types, more accurately capturing the intra-parcel variability. Except for Landsat 8, the NDVI means of MZs generated based on Sentinel-2 and PlanetScope using the fuzzy c-means algorithm showed statistically significant differences from each other, as determined by a one-way and Welch’s ANOVA (p < 0.05). The use of PlanetScope imagery demonstrated its superiority in generating zones that reflect inherent variability, offering farmers actionable insights at a reconnaissance scale. Multi-temporal satellite imagery has proved effective in monitoring plant growth responses to edaphological soil properties. In our study, the PlanetScope satellites, which offer the highest spatial resolution, consistently produced effective zones for orchard areas. These zones have the potential to enhance farmers’ discovery of knowledge at a reconnaissance scale. With the increasing spatial resolution and enhanced spectral resolution of newer satellite sensors, using cluster analysis with insights from soil scientists promise to help farmers better understand and manage the fertility of their fields in a cost-effective manner. Full article

Biodiversity, from genes to entire ecosystems, is crucial for a healthy planet. However, human activities, including business practices, are causing rapid biodiversity loss. This study focuses on selecting and integrating biodiversity protection practices into the supply chain, offering a chance to make positive changes for the environment and future generations. A new hybrid grey multi-criteria decision-making (MCDM) model is proposed in this paper, which combines the grey Best–Worst Method (BWM) for obtaining criteria weights and the grey Axial Distance-based Aggregated Measurement (ADAM) method for ranking alternatives (practices). The applicability of the proposed model for solving the defined problem was demonstrated by ranking nine practices according to seven criteria. The most effective supply chain management practices in the context of biodiversity conservation were supply chain policies (with a score of 0.044), biodiversity goal setting, monitoring, reporting, and transparency (0.039), and education and awareness raising (0.037). These practices are the best because they combine clear frameworks, measurable goals, and long-term cultural change for effective biodiversity conservation. The lowest ranked practice is compliance with legislation (0.006) since it represents a baseline, reactive approach rather than a proactive or innovative strategy for biodiversity conservation. This study provides a comprehensive framework and hybrid MCDM model that enhances theoretical knowledge and can serve as a basis for developing a practical tool for integrating, assessing, and prioritizing biodiversity-focused practices in supply chains. The main novelties of this paper are the extension of the ADAM method in the grey environment, the development of a new hybrid MCDM model that combines the grey BWM and grey ADAM method, the identification of biodiversity-oriented business strategies in supply chains and the criteria for their evaluation, and a framework for practice evaluation and selection. Full article

Youth research has, for decades, focused on examining current societal conditions and their potential shortcomings for young people in areas such as education, income, work, and gender equality. However, it has been less common to integrate planetary boundaries and so-called “overshoot” areas—such as biodiversity loss or climate change—into youth research. This paradigm shift is increasingly necessary, as six out of nine planetary boundaries have already been crossed, and the planet remains on track for approximately 2.7 degrees Celsius (°C) peak warming by 2100. In addition to planetary threats, Morocco faces social challenges, particularly high unemployment. Unemployment is highest among young people aged 15 to 24, reaching 25% over the past decade, nearly double the global youth unemployment rate. This article analyzes a case study we refer to as the “Gardening School” in Morocco, a country facing significant climate stress. It aims to (a) explore new methods for conducting more globally oriented youth research that is ethical and environmentally friendly and (b) examine the wellbeing of young people and their environment, as well as how to support and strengthen both. The findings of this article highlight the potential for youth research to develop new approaches, especially when conducted alongside young people and educational and sustainable environments. These environments enable younger generations to deepen their connection to and understanding of biodiversity, sustainability, and climate change, while learning to use natural resources in a sustainable and ethical manner. This approach ultimately aims to ensure a livable future for the coming generations and foster sustainable employment opportunities. Full article

This study evaluated the effectiveness of Planet satellite imagery in mapping seagrass coverage in Santa Rosa Sound, Florida. We compared very-high-resolution aerial imagery (0.3 m) collected in September 2022 with high-resolution Planet imagery (~3 m) captured during the same period. Using supervised classification techniques, we accurately identified expansive, continuous seagrass meadows in the satellite images, successfully classifying 95.5% of the 11.18 km² of seagrass area delineated manually from the aerial imagery. Our analysis utilized an occurrence frequency (OF) product, which was generated by processing ten clear-sky images collected between 8 and 25 September 2022 to determine the frequency with which each pixel was classified as seagrass. Seagrass patches encompassing at least nine pixels (~200 m²) were almost always detected by our classification algorithm. Using an OF threshold equal to or greater than >60% provided a high level of confidence in seagrass presence while effectively reducing the impact of small misclassifications, often of individual pixels, that appeared sporadically in individual images. The image-to-image uncertainty in seagrass retrieval from the satellite images was 0.1 km² or 2.3%, reflecting the robustness of our classification method and allowing confidence in the accuracy of the seagrass area estimate. The satellite-retrieved leaf area index (LAI) was consistent with previous in situ measurements, leading to the estimate that 2700 tons of carbon per year are produced by the Santa Rosa Sound seagrass ecosystem, equivalent to a drawdown of approximately 10,070 tons of CO₂. This satellite-based approach offers a cost-effective, semi-automated, and scalable method of assessing the distribution and abundance of submerged aquatic vegetation that provides numerous ecosystem services. Full article

Plastics are very versatile materials that have contributed to the development of society since the 19th century; however, their mismanagement has led to an accumulation of plastic waste in almost every ecosystem, affecting the fauna of the planet. However, recently, some studies have shown that some insects might be able to adapt, consuming a wide range of hydrocarbon base polymers. In this work, the adaptive capacity of Zophobas morio larvae when feeding on different synthetic polymers derived from petroleum was studied. Four different thirty-day larval feeding treatments were carried out with synthetic polymers, including expanded polystyrene (PS), low-density polyethylene (LDPE), polyisoprene (PI), and butyl rubber (BR); in addition, a positive control of organic diet was included. Intestinal bacteria were isolated from the treatments and identified by Sanger sequencing. To analyze the chemical composition and physical form of the frass produced, Fourier transform infrared spectroscopy (FITR) was performed, and images of the feces’ surfaces were taken with scanning electron microscopy (SEM), respectively. Zophobas morio larvae were able to consume 54% of PS in 30 days, equivalent to 3.2 mg/d/larva. Nine culturable bacterial strains associated with the decomposition of synthetic polymers were identified in the intestine of the larvae. As for the physicochemical analysis of the feces, FTIR spectra showed the scission of bands corresponding to functional groups of the synthetic polymers in the comparison of the plastic diet treatments versus the feces of antibiotic-treated and plastic-fed larvae, while the comparison of spectra of the plastic and control treatments also identified differences in the absorption peaks. SEM imaging demonstrated that superworm feces differed in dependence on the substrate consumed. The findings demonstrated that Zophobas morio larvae possess a gut biological complex that allows them to feed and survive by consuming various petroleum-derived polymers. Full article

The “Forest Fires Prediction and Detection” edition highlights the importance of research on fires worldwide. In recent years, the increased frequency of fires caused by climate change has rendered the planet uninhabitable. Several works have been prepared and published in an effort to raise awareness among civil society and government bodies about the importance of developing new technologies for monitoring areas prone to mega-fires. This special issue includes nine important works from various countries. The goal is to better understand the impacts on the world’s most diverse regions, ecosystems, and forest phytophysiognomies. New geotechnologies and fire models were used, both of which are important and could be used in the future to improve short- and long-term planning in firefighting. Full article

A synoptic compendium of the legumes of the Mimosoideae clade in northeastern Mexico is presented for the first time, including changes in their botanical nomenclature and retypification of genera. Furthermore, based on new information recently published, the taxonomic limits of several new genera segregated from Acacia (Acaciella, Mariosousa, Senegalia, and Vachellia) and Prosopis (Neltuma and Strombocarpa) are clarified and included. Based on field work, collection of botanical samples over the past 30 years, and reviewing botanical materials in national and international herbaria, we have completed the diversity of legumes of the Mimosoideae clade of northeastern Mexico. Three tribes (Acacieae, Ingeae, and Mimosaeae), 22 genera, 92 species, and 19 infraspecific categories were recorded. Only the genus Painteria is endemic to Mexico. Eighty-eight species are native to Mexico, and four are exotic: Acacia salicina, Neptunia prostrata, Neltuma chilensis and Albizia lebbeck. Twenty-eight species are endemic to Mexico, nine species are endemic to northeastern Mexico, and four species are endemic to only one state in Mexico. The 22 registered genera represent 44% and 65% of the generic flora of the Mimosoideae clade for Mexico and the planet, respectively, while the 92 species registered represent 3% and 18% of the species of the clade Mimosoideae for the planet and Mexico, respectively. According to the new nomenclature of legumes, the number of genera in the Mimosoideae clade in northern Mexico has increased from 19 to 24. Full article

The full pinion engagement planetary gear trains are comparatively little known especially when it comes to the load sharing between the planets. In this paper, an attempt has been made to compensate for the lack of statistical data by extending the lumped mass model with the Monte Carlo simulation, thus generating thousands of different combinations for the pinhole position errors. A normal distribution has been assumed for the random variables. The static load sharing factor and the mesh load factor have been determined for nine scenarios with different mathematical expectations and mean deviations. Full article

Monitoring rainfall in the Brazilian Legal Amazon (BLA), which comprises most of the largest tropical rainforest and largest river basin on the planet, is extremely important but challenging. The size of the area and land cover alone impose difficulties on the operation of a rain gauge network. Given this, we aimed to evaluate the performance of nine databases that estimate rainfall in the BLA, four from gridded analyses based on pluviometry (Xavier, CPC, GPCC and CRU), four based on remote sensing (CHIRPS, IMERG, CMORPH and PERSIANN-CDR), and one from reanalysis (ERA5Land). We found that all the bases are efficient in characterizing the average annual cycle of accumulated precipitation in the BLA, but with a predominantly negative bias. Parameters such as Pearson’s correlation (r), root-mean-square error (RMSE) and Taylor diagrams (SDE), applied in a spatial analysis for the entire BLA as well as for six pluviometrically homogeneous regions, showed that, based on a skill ranking, the data from Xavier’s grid analysis, CHIRPS, GPCC and ERA5Land best represent precipitation in the BLA at monthly, seasonal and annual levels. The PERSIANN-CDR data showed intermediate performance, while the IMERG, CMORPH, CRU and CPC data showed the lowest correlations and highest errors, characteristics also captured in the Taylor diagrams. It is hoped that this demonstration of hierarchy based on skill will subsidize climate studies in this region of great relevance in terms of biodiversity, water resources and as an important climate regulator. Full article

Among the oldest marine species on the planet, the genus Salinispora is often encountered inhabiting sediments and other marine creatures in tropical and subtropical marine settings. This bacterial genus produces a plethora of natural products. The purpose of this study was to examine the potential for salinispora-based natural products (NPs) to combat the SARS-CoV-2 virus. The RCSB PDB was used to obtain the crystal structures of proteins 3CL^pro and PL^pro. All 125 NPs were obtained from online databases. Using Autodock Vina software v1.2.0 the molecular docking process was carried out after the proteins and ligands were prepared. Assessments of binding affinities and interacting amino acids were rigorously examined prior to MD simulations. The docking experiments revealed 35 NPs in total for both 3CL^pro and PL^pro, with high docking scores ranging from −8.0 kcal/mol to −9.0 kcal/mol. However, a thorough binding residue analyses of all docked complexes filtered nine NPs showing strong interactions with HIS: 41 and CYS: 145 of 3CL^pro. Whereas, for PL^pro, merely six NPs presented good interactions with residues CYS: 111, HIS: 272, and ASP: 286. Further research was conducted on residue–residue and ligand–residue interactions in both the filtered docked complexes and the Apo-protein structures using the Protein Contacts Atlas website. All complexes were found to be stable in CABS-flex 2.0 MD simulations conducted at various time frames (50, 125, 500, and 1000 cycles). In conclusion, salinaphthoquinone B appears to be the most promising metabolite, based on favorable amino acid interactions forming stable confirmations towards 3CL^pro and PL^pro enzymes, acting as a dual inhibitor. Full article

Modular construction (MC) has gained attention due to its potential for fast construction, reduced construction waste, and lower environmental impact while having several other issues on stimulating sustainability. The circular economy (CE) focuses on better resource management through a closed-loop system. Even though MC enhances sustainable practice, several pitfalls barricade sustainability in MC (high initial investment, design consideration, and technology challenges). Nevertheless, the synergy between CE and MC has not been investigated in past studies to address the issues in MC to achieve sustainability. This study investigates the integration of CE principles in MC to enhance sustainability. This study used a qualitative approach via the Delphi technique by conducting three semi-structured expert interview rounds with the use of a purposive sampling method. The collected data were analysed using manual content analysis. This study identified nine notable issues in MC to achieve sustainability, and all CE 9-R (rethink, refuse, reduce, reuse, repair, refurbish, remanufacture, recycle, and recover) principles could address those identified issues. Accordingly, thirty implementation strategies were recognised to fill the gap between the problems in MC and the potential of CE principles to solve the issues. The results provide insights for construction practitioners, policymakers, and researchers on integrating CE principles into MC processes to achieve sustainability goals. Ultimately, this study highlights the significance of a holistic approach by theoretically combining MC and CE principles as a benchmark for future studies. As a contribution, CE strives to make the planet a safe place to live by combatting resource depletion. Full article

Crop models are useful for evaluating crop growth and yield at the field and regional scales, but their applications and accuracies are restricted by input data availability and quality. To overcome difficulties inherent to crop modeling, input data can be enhanced by the incorporation of remotely sensed and field observations into crop growth models. This approach has been recognized to be an important way to monitor crop growth conditions and to predict yield at the field and regional scale. In recent years, satellite remote sensing has provided high-temporal and high-spatial-resolution data that allow for generating continuous time series of biophysical parameters such as vegetation indices, leaf area index, and phenology. The objectives of this study were to use remote sensing along with field observations as inputs to the Decision Support System for Agro-Technology (DSSAT) model to estimate soybean and maize growth and yield. The study used phenology and leaf area index (LAI) data derived from Planet Fusion (daily, 3 m) satellite imagery along with field observation data on crop growth stage, LAI and yield collected at the United State Department of Agriculture, Agricultural Research Service, Beltsville Agricultural Research Center (BARC), Beltsville, Maryland. For maize, a total of 17 treatments (site years) were used (ten treatments for model calibration and seven treatments for validation), while for soybean (maturity groups three and four), a total of 18 treatments were used (nine for calibration and nine for validation). The calibrated model was tested against an independent, multi-location and multi-year set of phenology and yield data (2017–2020) from BARC fields. The model accurately simulated maize and soybean days to flowering and maturity and produced reasonable yield estimates for most fields and years. Model run for independent locations and years produced good results for phenology and yields for both maize and soybean, as indicated by index of agreement (d) values ranging from 0.65 to 0.93 and normalized root-mean-squared error values ranging from 1 to 20%, except for soybean maturity group four. Overall, model performances with respect to phenology and grain yield for maize and soybean were good and consistent with other DSSAT evaluation studies. The inclusion of remote sensing along with field observations in crop-growth model inputs can provide an effective approach for assessing crop conditions, even in regions lacking ground data. Full article