Search Results (23)

Aquifer health assessment is essential for sustainable groundwater management, particularly in semi-arid regions with challenging geological conditions. This study presents a novel methodology for assessing aquifer health in the Barakar River Basin, a hard-rock terrain, by integrating tree-based classification, deep learning, and the Soil and Water Assessment Tool (SWAT) model. Employing Random Forest, Decision Tree, and Convolutional Neural Network (CNN) models, the research examines 20 influential factors, including hydrological, water quality, and socioeconomic variables, to classify aquifer health into four categories: Good, Moderately Good, Semi-Critical, and Critical. The CNN model exhibited the highest predictive accuracy, identifying 33% of the basin as having good aquifer health, while Random Forest assessed 27% as Critical heath. Pearson correlation analysis of CNN-predicted aquifer health indicates that groundwater recharge (r = 0.52), return flow (r = 0.50), and groundwater fluctuation (r = 0.48) are the most influential positive factors. Validation results showed that the CNN model performed strongly, with a precision of 0.957, Area Under the Curve–Receiver Operating Characteristic (AUC-ROC) of 0.95, and F1 score of 0.828, underscoring its reliability and robustness. Geophysical Electrical Resistivity Tomography (ERT) field surveys validated these classifications, particularly in high- and low-aquifer health zones. This study enhances understanding of aquifer dynamics and presents a robust methodology with broader applicability for sustainable groundwater management worldwide. Full article

Study Design: Retrospective chart review. Objective: Socioeconomic status (SES) greatly impacts one’s health status and the type of trauma that a patient experiences due to increased risk of exposure and varying availability of resources to treat emergent conditions. There is a need for large-scale databases of pediatric facial trauma to identify discrepancies in occurrence and identify risk factors. Methods: This retrospective examination uses a multi-center database to evaluate pediatric facial trauma patients (n = 644) visiting Atrium Heath Wake Forest Baptist (AHWFB) hospital from 2020 to 2022. Data collected included demographic information, past medical and surgical history, trauma history, interventions, and long-term outcomes such as scarring, deformities, and sensory or motor deficits. The number of incidents for each zip code surrounding AHWFB was compared with SES data including unemployment rate, mean household income, and poverty level. Results: Thirty-five percent of patients sustained a high-energy injury, and 65% sustained a low-energy injury. Within the surrounding counties of AHWFB, there were more incidents of pediatric facial trauma in areas with greater rates of poverty (p = 0.006). Additionally, there were more incidents due to high-energy injuries in areas with lower income (p = 0.044) and more poverty (p = 0.002). Specifically, motor vehicle accidents were more common in areas with lower income (p = 0.017) and more poverty (p = 0.001). Conclusions: These findings in the central Piedmont region of North Carolina are consistent with previous research of SES’s effect on health inequalities and serve as evidence of the need to take steps to prevent pediatric facial trauma in areas of low SES. Full article

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area. Full article

Li-ion batteries are integral to various applications, ranging from electric vehicles to mobile devices, because of their high energy density and user friendliness. The assessment of the Li-ion state of heath stands as a crucial research domain, aiming to innovate safer and more effective battery management systems that can predict and promptly report any operational discrepancies. To achieve this, an array of machine learning (ML) and artificial intelligence (AI) methodologies have been employed to analyze data from Li-ion batteries, facilitating the estimation of critical parameters like state of charge (SoC) and state of health (SoH). The continuous enhancement of ML and AI algorithm efficiency remains a pivotal focus of scholarly inquiry. Our study distinguishes itself by separately evaluating traditional machine learning frameworks and advanced deep learning paradigms to determine their respective efficacy in predictive modeling. We dissected the performances of an assortment of models, spanning from conventional ML techniques to sophisticated, hybrid deep learning constructs. Our investigation provides a granular analysis of each model’s utility, promoting an informed and strategic integration of ML and AI in Li-ion battery state of health prognostics. Specifically, a utilization of machine learning algorithms such as Random Forests (RFs) and eXtreme Gradient Boosting (XGBoost), alongside regression models like Elastic Net and foundational neural network approaches including Multilayer Perceptron (MLP) were studied. Furthermore, our research investigated the enhancement of time series analysis using intricate models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) and their outcomes with those of hybrid models, including a RNN-long short-term memory (LSTM), CNN-LSTM, CNN-Gated Recurrent Unit (GRU) and RNN-GRU. Comparative evaluations reveal that the RNN-LSTM configuration achieved a Mean Squared Error (MSE) of 0.043, R-Squared of 0.758, Root Mean Square Error (RMSE) of 0.208, and Mean Absolute Error (MAE) of 0.124, whereas the CNN-LSTM framework reported an MSE of 0.039, R-Squared of 0.782, RMSE of 0.197, and MAE of 0.122, underscoring the potential of deep learning-based hybrid models in advancing the accuracy of battery state of health assessments. Full article

The traditional knowledge of sunggau rafters for Apis dorsata nesting is essential for livelihood and forest conservation. We determine the plant species needed to support the conservation of A. dorsata in Belitung Islands, Indonesia, by investigating the distribution of the sunggau in the three types of forests, the traditional knowledge regarding the plant species used for sunggau, and the vegetation structure of forests in which the sunggau is installed. Distribution of sunggau were explored on heath, swamp heath, and mangrove forests. We conducted a vegetation survey in those forests and an ethnobotanical survey by interviewing bee farmers regarding the plants used for sunggau and other uses. We found 95 sunggau distributed in the heath, swamp heath, and mangrove forest. Based on interviews, we recorded 65 plant species for sunggau and six other uses, including bee forages. Calophyllum sp., Cryptocarya sp., Melaleuca cajuputi, and Syzygium urceolatum are the most important plants according to bee farmers. The last two species dominate all forests, except mangroves, which are dominated by Lumnitzera littorea, according to vegetation surveys. However, the availability of several plants for sunggau is declining. Therefore, the conservation of A. dorsata needs the sustainability of sunggau and bee forage plants. Full article

Human-induced environmental alterations in the Alps may importantly affect small mammal species, but evidence in this sense is limited. We live-trapped small rodents in the Central-Eastern Italian Alps in three close-by habitat types (rocky scree, alpine grassland, and heath) at 2100 m a.s.l. during summer-fall, in 1997 and 2016. We compared small rodent assemblages through a Redundancy Detrended Analysis (RDA). In both surveys, we detected two specialist species, i.e., the common vole (Microtus arvalis) and the snow vole (Chionomys nivalis), and, unexpectedly, the forest generalist bank vole (Myodes glareolus). In 1997, grassland was mainly occupied by the common vole, while the bank vole and the snow vole were sympatric in the other habitats. In 2016, the snow vole was detected only in the scree, while other species did not show distribution changes. We discuss a series of hypotheses that might have driven the differences observed across decades, among which is a species-specific response to abiotic and biotic environmental alterations, with the alpine habitat specialist moving out of sub-optimal habitats. We encourage further research on this topic, e.g., via long-term longitudinal studies. Full article

In nearly every ecological community, most species are represented by a few individuals, and most individuals come from a few of the most common species. Singletons (one individual sampled) and doubletons (two individuals sampled) are very common in moth community studies. In some reports, these specimens are excluded from the analysis once they are considered a consequence of under-sampling or of contamination with tourist species that are just passing through. Throughout 12 nights in 2018 and 12 nights in 2019, two Heath traps, one with an 8 W ultraviolet lamp and the other with a 15 W actinic lamp, were positioned approximately 50 m apart at nine sites of four different biotopes in a mosaic forest ecosystem in the Narew National Park (NE Poland). We were able to differentiate moth assemblages according to the forest biotopes under study and by the year of research. With our results, it becomes more evident that singletons and doubletons sampled using weak light Heath traps should be included in the ecological analysis of Macroheteroceran moth assemblages, and our research strongly suggests that they are an important and consistent element of such a sampling method. We also demonstrate that weak light Heath traps are suitable for building an inventory scheme of moth assemblages in small forest areas and that singletons and doubletons can be crucial elements in long-term monitoring systems. Full article

Nitrogen (N) deposition significantly affects forest dynamics, carbon stocks and biodiversity, and numerous assessments of N fluxes and impacts exist in temperate latitudes. In tropical latitudes, however, there are few such assessments. In this study, we measured the inorganic N concentration (wet deposition) deposited in rainfall and rainfall pH throughout one year at the boundary of a forest reserve in Malaysian Borneo. We considered that the N deposition may be either from forest and agricultural fires or derived from agricultural fertiliser. Therefore, we determined the wind trajectories using the HYSPLIT model provided by NOAA, the location of fires throughout the landscape throughout one year using NASA’s FIRM system, and obtained the land use cover map of Malaysia and Indonesia. We then correlated our monthly cumulative wet N deposition with the cumulative number of fires and the cumulative area of oil palm plantation that wind trajectories arriving at our study site passed over before reaching the rainfall sampling site. At 7.45 kg N ha⁻¹ year⁻¹, our study site had the highest annual wet inorganic N deposition recorded for a Malaysian forest environment. The fire season and the cumulative agricultural area crossed by the winds had no significant effect on N deposition, rainfall N concentration, or rainfall pH. We suggest that future research should use ¹⁵N isotopes in rainfall to provide further information on the sources of N deposition in tropical forests such as this. Full article

Assessing a taxon’s response to change in environmental variables is fundamental knowledge to understanding trends in species diversity, abundance, and distribution patterns. This is particularly needed on Borneo, where knowledge on Odonata populations in different habitats is poor. To address this gap, we present the first study investigating the relationship between morphology and species distribution of Odonata communities in a heath (kerangas)-dominated mixed-mosaic-lowland forest in southern Borneo. We sampled 250-m line transects in three habitat types: mixed peatcswamp, kerangas, and low-pole peatcswamp, with weekly surveys from December 2019 to February 2020. A total of 309 individuals were detected from 25 species. Anisoptera and Zygoptera diversity was the highest in mixed peatcswamp and lowest in low pole, while abundance was the highest in low pole and lowest in kerangas; with kerangas notably harboring a very small sample size. Odonata community assemblages differed most between mixed peat swamp and low pole. Morphological data were compared between suborders and habitats. Anisoptera showed significantly larger thoraces, hindwings, and hindwing-to-body ratio than Zygoptera. Anisoptera in low pole were significantly smaller in body, thorax, and hindwing compared to both kerangas and mixed peat swamp. Anisoptera showed a strong association with pools and Zygoptera with flowing water. Heterogeneity, habitat characteristics, presence of specialists, body size, and the interaction between species’ morphological traits and habitat characteristics likely explained the trends observed. Full article

It is increasingly recognized that invasion by alien plant species such as Acacia spp. can impact tropical forest ecosystems, although quantifications of nutrient fluxes for invaded lowland tropical rain forests in aseasonal climates remain understudied. This paper describes the methodology and presents data collected during a year-long study of litterfall production and leaf litter decomposition rates in two distinct tropical lowland forests in Borneo affected by Acacia invasion. The study is the first to present a comprehensive dataset on the impacts of invasive Acacia species on Bornean forests and can be further used for future research to assess the long-term impact of Acacia invasion in these forest ecosystems. Extensive studies of nutrient cycling processes in aseasonal tropical lowland rainforests occurring on different soil types remain limited. Therefore, this dataset improves understanding of nutrient cycling and ecosystem processes in tropical forests and can be utilized by the wider scientific community to examine ecosystem responses in tropical forests. Full article

Invasive Acacia species can alter nutrient cycling processes in forest ecosystems, particularly affecting total litterfall production and litter decomposition patterns. This study examined the effects of exotic Acacia mangium Willd. on total litterfall production, nutrient concentrations in leaf litterfall fractions, leaf litter decomposition, and nutrient release in lowland heath (HF) and mixed dipterocarp forests (MDF) in Brunei Darussalam, Borneo. Above-ground litterfall traps were installed in HF and MDF with and without invasive Acacia present, representing four habitat types in total, and monthly collections were conducted for 12 months. Litter decomposition bags were deployed to determine the rates of decomposition and nutrient release. Habitats invaded by Acacia exhibited higher total litterfall production, increased leaf litter concentrations of nitrogen, potassium, and calcium, and increased addition of all nutrients measured in litter (nitrogen, phosphorus, potassium, calcium, and magnesium, especially in the Acacia-invaded mixed dipterocarp forest (AMDF) and nitrogen and potassium in Acacia-invaded heath forest (AHF)), reduced nitrogen and potassium use efficiencies in AHF, and reduced stand-level nitrogen and calcium use efficiencies in AMDF. Litter decomposition rates and nutrient release were lower in AMDF than in the three other habitats. The significantly higher total litterfall production coupled with higher nutrient addition in the two Acacia-invaded habitats is expected to progressively increase the abilities of these habitats to produce large quantities of nutrient-rich litter and will likely eventually lead to an enrichment of nutrients in the soil, thus facilitating further invasion by Acacia, particularly in the MDF. Full article

High-resolution maps of soil health measurements could help farmers finetune input resources and management practices for profit maximization. Within-field soil heath variations can be mapped using local topography and apparent electrical conductivity (ECa) as predictors. To address these issues, a study was conducted in Texas Blackland Prairie soils with the following objectives: (i) to assess and map within-field soil health variations using machine learning; (ii) to evaluate the usefulness of topography and ECa as soil health predictors; and (iii) to quantify the relationship between ECa and soil health index and use ECa to estimate soil health spatial distribution. We collected 218 topsoil (0–15 cm) samples following a 35 m × 35 m grid design and analyzed for one-day CO₂, organic C, organic N, and soil health index (SHI) based on the Haney Soil Health Tool. A random forest model was applied to predict and map those properties on a 5 m × 5 m grid where ECa, and terrain attributes were used as predictors. Furthermore, the empirical relationship between SHI and ECa was established and mapped across the field. Results showed that the study area was variable in terms of one-day CO₂, organic C, organic N, SHI, and ECa distribution. The ECa, wetness index, multiresolution valley bottom flatness, and topographic position index were among the top predictors of soil health measurements. The model was sufficiently robust to predict one day CO₂, organic C, organic N (R² between 0.24–0.90), and SHI (R² between 0.47–0.90). Overall, we observed a moderate to strong spatial dependency of soil health measurements which could impact within-field yield variability. The study confirmed the applicability of easy to obtain ECa as a good predictor of SHI, and the predicted maps at high resolution which could be useful in site-specific management decisions within these types of soils. Full article

Wet, dry, and occult atmospheric deposition may be modified by vegetation canopies. The aim of this study was to verify canopy drip effect studies conducted in 2012, in 2013, and in 2015/2016. For this purpose, 26 moss samples were taken at each of eight monitoring sites of the European Moss Survey 2020/2021 in Germany from a corresponding number of subplots, each representing the site categories “under tree canopy” and adjacent “open land”. The sampling, as well as the chemical analyses, of 12 metals (Al, As, Cd, Cr, Cu, Fe, Hg, Pb, Ni, Sb, V, Zn) and nitrogen (N) and the recording of sample- and site-describing metadata were conducted according to the ICP Vegetation experimental protocol. The results demonstrate an overall higher metal and nitrogen accumulation in moss samples of “canopy” sites compared to neighboring “open land sites” (grassland, heath). The ratios between the “canopy” and “open land” sites of 1.18 to 1.69 and significant correlations of r > 0.8 in case of five elements agree well with corresponding values from samplings in 2012, 2013, and 2015/2016. These results should be used for modeling atmospheric deposition aiming at more realistic results. With regard to the question of whether, and to what extent, moss samples should preferably be taken from “open land” or “canopy” sites, the following can be concluded: The recommendations of ICP Vegetation with regard to the minimum distance to be maintained from trees and shrubs should not be interpreted to mean that “open” sites are fundamentally more suitable for moss sampling in Germany than, for example, clearings in forests. The mostly higher variability of the measured values compared to the “canopy” sites rather suggests that in the open country a much higher number of influencing factors could be significant for the element accumulation in mosses in addition to the background pollution through atmospheric deposition. This is also supported by the fact that the metal contents in the moss samples of the “open” sites can clearly exceed those of the neighboring “canopy” sites in individual cases. With regard to “open” land, grassland sites seem to be less suitable for moss sampling than bog and heathland sites. In grassland, moss occurrences are often sparser and/or cut short by meadow mowing, so that the removal of three-year shoots on grassland, as recommended by ICP Vegetation, must be replaced in places by one-year shoots. The comparatively higher state dynamics of grassland sites also make the resampling of moss at previously sampled sites more difficult. Full article

Many nutrient-poor coarse-textured Kalmia L.–black spruce forest sites in eastern Canada turn to ericaceous heath dominated by Kalmia angustifolia L. after clearcutting and fire. While the mechanisms of post-fire forest and heath formation have been well documented, the origin of shrub savanna vegetation has received limited attention. This study demonstrates the significance of post-fire island regeneration of black spruce in Kalmia heath to the origin of shrub savannah alternate state. The study was conducted in Three Brooks, 10 km west of Grand Falls-Windsor, Newfoundland (48°51′ N; 55°37′ E). Black spruce forest in the site was clearcut, then a wildfire burned the area, and the site was subsequently planted with black spruce. Plant species cover, black spruce growth (stem density, stem height, basal diameter, and yearly volume increment), and foliar nutrients of planted spruce and soil properties (pH, humus and Ae horizon depth, and nutrients) in tree islands were compared with adjacent Kalmia heath. Black spruce islands had significantly lower cover of Kalmia and higher stem density of black spruce compared to Kalmia heath (7100 stems/ha in islands vs. 1920 stems/ha in heath). Height, basal diameter, and yearly volume increment of black spruce were more than three times higher in spruce islands than in Kalmia heath. Foliar nutrients of black spruce growing in Kalmia heath had significantly lower N and Mg (33 and 38%, respectively) but had significantly higher Mn and Zn (46 and 33%, respectively) than in black spruce islands. Black spruce growth inhibition in Kalmia heath is attributed to soil nutrient imbalance due to Kalmia evidenced by reduced concentrations of N and Mg and increased concentrations of Al, Fe, and other inorganic ions in the foliage. These results suggest that post-fire black spruce islands in severely burned patches provide “safe sites” for spruce regeneration, whereas Kalmia heath developing in non-severe burn area inhibits spruce regeneration and creates shrub savannah community as an alternate vegetation state. Full article

Here we report, for the first time, the occurrence of the bacteria from the species complex Pseudomonas syringae in Iceland. We isolated this bacterium from 35 of the 38 samples of angiosperms, moss, ferns and leaf litter collected across the island from five habitat categories (boreal heath, forest, subalpine and glacial scrub, grazed pasture, lava field). The culturable populations of P. syringae on these plants varied in size across 6 orders of magnitude, were as dense as 10⁷ cfu g⁻¹ and were composed of strains in phylogroups 1, 2, 4, 6, 7, 10 and 13. P. syringae densities were significantly greatest on monocots compared to those on dicots and mosses and were about two orders of magnitude greater in grazed pastures compared to all other habitats. The phylogenetic diversity of 609 strains of P. syringae from Iceland was compared to that of 933 reference strains of P. syringae from crops and environmental reservoirs collected from 27 other countries based on a 343 bp sequence of the citrate synthase (cts) housekeeping gene. Whereas there were examples of identical cts sequences across multiple countries and continents among the reference strains indicating mixing among these countries and continents, the Icelandic strains grouped into monophyletic lineages that were unique compared to all of the reference strains. Based on estimates of the time of divergence of the Icelandic genetic lineages of P. syringae, the geological, botanical and land use history of Iceland, and atmospheric circulation patterns, we propose scenarios whereby it would be feasible for P. syringae to have evolved outside the reach of processes that tend to mix this bacterial complex across the planet elsewhere. Full article