Forest Trees Research Articles

Complex imprint of air pollution in the basal area increments of three European tree species

The impact of atmospheric pollution on the growth of European forest tree species, particularly European beech, Silver fir and Norway spruce, is examined in five mesic forests in the Czech Republic. Analyzing of basal area increment (BAI) patterns using linear mixed effect models reveals a complex interplay between atmospheric nitrogen (N) and sulphur (S) deposition, climatic variables and changing CO2 concentrations. Beech BAI responds positively to N deposition (in tandem with air CO2 concentration), with soil phosphorus (P) availability emerging as a significant factor influencing overall growth rates. Fir BAI, on the other hand, was particularly negatively influenced by S deposition, although recent growth acceleration suggests growth resilience in post-pollution period. This fir growth surge likely coincides with stimulation of P acquisition following the decline of acidic pollution. The consequence is the current highest productivity among the studied tree species. The growth dynamics of both conifers were closely linked to the stoichiometric imbalance of phosphorus in needles, indicating the possible sensitivity of exogenous controls on nutrient uptake. Furthermore, spruce BAI was positively linked to calcium availability across sites. Despite enhanced water-use efficiency under elevated CO2, spruce growth is constrained by precipitation deficit and demonstrates weakening resilience to increasing growing season air temperatures. Overall, these findings underscore the intricate relationships between atmospheric pollution, nutrient availability, and climatic factors in shaping the growth dynamics of European forest ecosystems. Thus, incorporating biogeochemical context of nutrient availability is essential for realistic modelling of tree growth in a changing climate.

Energy transfer during tree movement for different wind conditions and forest configurations

Catastrophic winds from tropical cyclones and storms cause tree failure in forests, leading to economic and environmental losses. Tree failure, especially uprooting, results from energy transfer from winds to the roots through the tree stem. However, we do not fully understand how trees in forests respond to winds under different weather conditions and whether the responses remain unchanged with the configurations of the forest. In this study, we established two Cryptomeria japonica plots, namely P-100 as a control (3000 stem ha−1), and P-50, which was thinned to halve the control stand density (1500 stem ha−1) in November 2017, in Kasumigaura City, Japan. Two kinds of sensors were installed on the tree stems in the plots and data was collected over the following 2 years. Strain gauge transducers at the stem base were used to calculate turning moments. Inertial measurement unit (IMU) sensors at 6 m height provided the x, y, and z coordinates of the tree motion. At the end of the observation period, we conducted tree-pulling experiments to calculate the maximum turning moments the trees could resist. During the observation period, the trees experienced winds from typhoons, including super typhoon Trami in 2018, causing some tree failure in the thinned plot (P-50). Based on this damage incident, we selected four different forest configurations and 18 windy conditions (7 typhoons and 11 weather conditions in which a maximum wind speed of more than 7 m s−1 was observed). We found two dominant movement frequencies, one at between 0.2 – 0.5 Hz and one at between 2.0 – 2.3 Hz. The higher movement frequency appeared at the lower wind speeds and the lower movement frequency appeared at the higher wind speeds. The transition wind speeds from the higher to the lower movement frequency were found between 1.79 and 7.44 m s–1 in the P-100 unthinned plot and between 1.57 and 5.63 m s–1 in the P-50 thinned plot. In addition, we determined that the resistance to the damage over a 10-min period during typhoon Trami, based on the tree that uprooted, was only 48 % of the maximum tree resistance estimated from the tree-pulling experiments. Our study describing tree dynamic responses to winds under different forest configurations and wind conditions could provide alternative methods for modifying the forest environment, especially through forest management activities such as thinning and harvesting, in order to mitigate wind damage risk in the future.

The Effectiveness of Tannin on the Amount of Damage to Forest Trees and Stands Caused by Red Deer in the Western Carpathians

Damage caused by cloven-hoofed game and wild animals is a serious problem in Slovakia and neighboring countries. In 2022, the damage to forests in Slovakia was estimated at a level of almost EUR 2.9 million. Despite several implemented measures, the situation is so severe that successfully restoring some trees in some heavily attacked stands is impossible. This paper deals with the preventive elimination of damage caused by red deer in forest stands by the biological method of applying tannin-based mineral licks. Specifically, licks containing tannin obtained from the edible chestnut were used for testing. Several positive effects are attributed to tannins in developing livestock and animal fitness status. To date, however, the question of using tannins as biological agents for the possible protection of forest stands has not been addressed. Therefore, red deer damage was assessed at two localities with similar natural conditions in the central part of the Western Carpathians to explore such a possibility. After the initial sample measurement of the bark-stripping damage caused by the red deer in the autumn, mineral licks with tannin were placed in one selected study area over the winter. The levels of and differences in forest damage by deer between areas with the application of licks with tannin and without tannin were detected in spring of the following year. In the location treated with tannin, the number of damaged sample plots decreased, and the mean plot damage did not increase (no statistically significant differences were registered in mean plot damage between autumn and spring). Meanwhile, in the control location, the total amount of bark-stripped area doubled, mainly due to a statistically significant increase in mean plot damage between autumn and spring. Based on this, we conclude that tannin had a significant preventive effect and considerably reduced the extent of bark stripping. Such promising results indicate that tannin licks can effectively mitigate the problem of bark-stripping damage in areas heavily attacked by red deer.

Long-Term Monitored Norway Spruce Plots in the Ore Mountains-30 Years of Changes in Forest Health, Soil Chemistry and Tree Nutrition after Air Pollution Calamity.

The Ore Mountains were historically one of the most polluted areas in Europe, where high sulphur dioxide concentrations and a high level of atmospheric deposition led to a vast decline in Norway spruce stands in the mountain ridge plateau. In this article, we evaluate the trends in the atmospheric deposition load, soil chemistry, tree nutrition, crown defoliation and height increment in a network of twenty research plots monitored for last thirty years in this region. The decrease in sulphur and nitrogen deposition was most pronounced at the end of 1990s. Extreme values of sulphur deposition (100-200 kg.ha-1.year-1) were recorded in throughfall under mature Norway spruce stands in the late 1970s, and after felling of the damaged stand, the deposition levels were comparable to open plot bulk deposition. Nitrogen deposition decreased more slowly compared with sulphur, and a decrease in base cation deposition was observed concurrently. The current deposition load is low and fully comparable to other mountain areas in central Europe. Accordingly, the health of young spruce stands, as assessed by defoliation and height increment, has improved and now corresponds to the Czech national average. On the other hand, no significant changes were observed in the soil chemistry, even though some of the plots were limed. Acidic or strongly acidic soil prevails, often with a deficiency of exchangeable calcium and magnesium in the mineral topsoil, as well as decreases in available phosphorus. This is reflected in the foliage chemistry, where we see an imbalance between a relatively high content of nitrogen and decreasing contents of phosphorus, potassium and calcium. Despite the observed positive trends in air quality and forest health, the nutritional imbalance on acidified soils poses a risk for the future of forest stands in the region.

Impact of the extreme 2015-16 El Niño climate event on forest and savanna tree species of the Amazonia-Cerrado transition

Extreme drought events, driven by the El Niño Southern Oscillation (ENSO), are linked to increased tree mortality and alterations in vegetation structure, dynamics, and floristic composition in tropical forests. Existing analyses, primarily focusing on Africa, Central America, and Amazonia, overlook the floristic impacts on biome transitions. This study evaluates the profound effects of the severe 2015/2016 ENSO event on tree density and floristic composition in the critical transition zone between Amazonia and Cerrado, South America's largest biomes. Our findings not only document significant biodiversity loss but also offer insights into species resilience, guiding conservation strategies under changing climate conditions. We inventoried long-term plots before and after the extreme drought event, sampling 12,465 individuals from 526 species, 224 genera, and 65 families, in Open Ombrophilous Forest (OF), Seasonal Forest (SF), Cerradão (CD), and Typical Cerrado (TC). We document the disappearance from our plots of 97 species after the ENSO, with only 61 new species being recorded. The total loss of individuals across the transition zone was almost 10 %. The SF and CD forest plots showed the greatest replacements, species losses, and reductions in tree density. Their markedly seasonal baseline climate probably drove these changes. In most phytophysiognomies, there was an increase in pioneer species and drier environment habitat specialist species, indicating that although many species are vulnerable to extreme climate events, others benefit, especially those with a short life cycle. We found that the vegetation of the Amazonia-Cerrado transition overall is vulnerable to climate anomalies, with widespread loss of tree density and change in floristic composition. Our study also provides a species-by-species list of the most vulnerable and resistant trees which helps point to overall climate change vulnerabilities and assist with initiatives to recover degraded areas.

Enhanced machine learning approaches for OSA patient screening: model development and validation study

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. The study data were obtained from the clinical records of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training-test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The artificial neural network model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 80.4% (95% CI 76.7–84.1%), 69.9% (95% CI 69.8–69.9%), 86.5% (95% CI 81.6–91.3%), 61.5% (95% CI 56.6–66.4%), 53.2% (95% CI 47.7–58.7%), 65.9% (95% CI 60.2–71.5%), and 0.165, respectively, for the artificial neural network model. The AUROCs for the LR, NB, SVM, RF, and DT models were 80.2%, 79.7%, 79.2%, 78.4%, and 70.4%, respectively. The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the artificial neural network model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

Comparative Analysis of Supervised Machine Learning Algorithms for Diabetes Prediction

Diabetes Mellitus is a chronic disease with far-reaching consequences, necessitating innovative diagnostic approaches. Current methods have limitations, highlighting the need for advanced techniques. This research leveraged machine learning algorithms to predict diabetes using the Pima Indian Diabetes Dataset. Six algorithms were developed and compared: Logistic Regression, Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Random Forest, and Decision Tree. Standard evaluation metrics assessed their performance. The Random Forest Classifier emerged as the top-performing algorithm, achieving an impressive 91% accuracy and demonstrating exceptional reliability. This study showcases machine learning's potential to transform diabetes diagnosis and management. The developed web application provides a user-friendly interface for predicting diabetes, facilitating timely intervention and improved health outcomes. The findings contribute significantly to the development of intelligent disease prediction systems, promoting early detection and enhanced patient care. By harnessing machine learning, this research pioneers a new frontier in diabetes diagnosis, paving the way for improved patient outcomes and enhanced healthcare services.

Detection of Paecilomyces formosus associated with declining urban forests and beetles in Iran

Urban forest trees are vital components of urban ecosystems, offering a range of benefits that are essential for improving the livability and sustainability of cities, providing numerous advantages for both the environment and public health. They enhance air quality by filtering pollutants, assist in regulating urban temperatures, and alleviate the urban heat island effect, which can result in substantial energy savings. Trees are often vulnerable to pathogens and pests that can cause significant damage. A survey of different trees in five provinces of Iran revealed a severe decline and dieback disease on woody plants. The affected trees included ailanthus, cedrus, cypress, ash, haloxylon, walnut, magnolia, black mulberry, paulownia, pine, oriental plane, apricot, wild pear, and elm trees. Samples of symptomatic branches and trunks were collected, and the causal fungal pathogen was isolated on potato dextrose agar (PDA) media. A total of 90 fungal isolates were obtained from trees (60 isolates) and insects (30 isolates) and then morphological investigations were done for all isolates. Molecular identification was confirmed through sequencing of the ITS and tub2 regions. This study reports 14 new hosts for Paecilomyces formosus in Iran and worldwide. Pathogenicity tests were conducted on detached branches of apricot, ailanthus, cypress, pine, sycamore, and walnut trees. The study showed that most isolates were pathogenic to six woody plants, and some isolates were associated with disease for eight woody plant species. Additionally, potential vectors and reservoirs for P. formosus were assessed in different beetles, including Aeolesthes sarta, Scolytus kirschii, and Orthotomicus erusus in Tehran, Alborz, Qazvin, Lorestan, and Zanjan Provinces. The results confirmed the potential of beetles for the transmission and maintenance of P. formosus.

Stacked machine learning approach for predicting evolved hydrogen from sugar industry wastewater

The wastewater generated by the sugar industry contains high levels of organic pollutants, posing significant environmental and health risks. Traditional treatment methods often fall short in effectively managing these pollutants, necessitating innovative approaches for wastewater treatment and hydrogen production. In this study, machine learning (ML) algorithms are explored to predict hydrogen evolution from sugar industry wastewater treated via photocatalytic oxidation using solar-driven photocatalysts. By leveraging ML-driven models, we aim to streamline experimental procedures, reduce costs, and enhance energy efficiency. Our proposed stacking model, which combines Random Forest, Gradient Boosting, XGBoost, and Extra Trees, achieved superior results with R2 values of 0.9933, 0.9866, and 0.9906, and mean squared errors (MSE) of 0.0350, 0.0412, and 0.0412 for GLFO, GBFO, and GLRO, respectively. These results demonstrate the significant advantages of using ensemble techniques, particularly our proposed stacking method, which outperformed all individual models. The blending model, which averaged predictions from XGBoost and Gradient Boosting, also performed well with R2 values of 0.9866, 0.9811, and 0.9820, and MSEs of 0.0702, 0.0815, and 0.0795 for GLFO, GBFO, and GLRO, respectively. This study highlights the efficacy of ensemble methods in predicting hydrogen production, providing a novel and effective approach for wastewater treatment and renewable energy generation.

Filamentous Fungi Associated with Disease Symptoms in Non-Native Giant Sequoia (Sequoiadendron giganteum) in Germany-A Gateway for Alien Fungal Pathogens?

Filamentous fungi associated with disease symptoms in non-native giant sequoia (Sequoiadendron giganteum) in Germany were investigated in ten cases of disease in Northwest Germany. During the study period from 2018 to 2023, a total of 81 species of Dikaria were isolated from woody tissue and needles of giant sequoia and morphotyped. Morphotypes were assigned to species designations based on ITS-sequence comparison and, in part, multi-locus phylogenetic analyses. Nine species were recognised as new reports for Germany or on giant sequoia: Amycosphaerella africana, Botryosphaeria parva, Coniochaeta acaciae, C. velutina, Muriformistrickeria rubi, Pestalotiopsis australis, P. monochaeta, Phacidiopycnis washingtonensis, and Rhizosphaera minteri. The threat posed to giant sequoia and other forest trees in Germany by certain, especially newly reported, fungal species is being discussed. The detection of a considerable number of new fungal records in the trees studied suggests that giant sequoia cultivation may be a gateway for alien fungal species in Germany.

Model development using hybrid method for prediction of drug release from biomaterial matrix

A comprehensive multi-scale computational strategy was developed in this study based on mass transfer and machine learning for simulation of drug concentration distribution in a biomaterial matrix. The controlled release was modeled and validated via the hybrid model. Mass transfer equations along with kinetics models were solved numerically and the results were then used for machine learning models. We investigated the performance of three regression models, namely Decision Tree (DT), Random Forest (RF), and Extra Tree (ET) in predicting medicine concentration (C) based on r and z data. Hyper-parameter optimization is conducted using Glowworm Swarm Optimization (GSO). Results revealed high predictive accuracy across all models, with ET demonstrating superior performance, achieving a coefficient of determination value (R2) of 0.99854, an RMSE of 1.1446E-05, and a maximum error of 6.49087E-05. DT and RF also exhibit notable performance, with coefficients of determination equal to 0.99571 and 0.99655, respectively. These results highlight the effectiveness of ensemble tree-based methods in accurately predicting chemical concentrations, with Extra Tree (ET) Regression emerging as the most promising model for this specific dataset.

A method for determining the spatial pattern of forest trees based on the uniformity theory

A method for determining the spatial pattern of forest trees based on the uniformity theory

Climatic limitations of nearly endangered Juniperus rigida populations at their range edges in semiarid China

The treat of climate warming is expected to increase the frequency and severity of droughts and significantly affect tree growth and distribution ranges, particularly in regions without monsoons. The distribution limits of tree species are highly susceptible to climate change. A deeper understanding of how tree species respond to climate across their distribution ranges is crucial for accurately predicting forest dynamics in the face of climate warming. Using tree ring width data from 586 trees at 19 sites spanning the northernmost and southernmost limits of Juniperus rigida (near-threatened, IUCN: NT), we assessed the growth responses of J. rigida to climate at different distribution range limits. We observed significant differences in tree-growth variability between the southern and northern populations of J. rigida, characterized by an increasing growth trend at the southern range limit, but a decreasing trend at the northern limit since 1996. Moreover, tree growth at their northern limit was hindered by high temperatures and SPEI during summer months, whereas it was constrained by low temperatures in March and precipitation in June at the southern limit. Particularly noteworthy is the increased sensitivity of growth to SPEI and precipitation in recent decades (1996–2018) compared to previous decades (1961–1995), particularly at their northern distribution edge. We therefore conclude that the growth of J. rigida trees at the southern limit could benefit from climate warming, while increasing warming and drought could limit tree growth or even cause tree mortality at the northern range limits. These findings may serve as an early warning for the growth and survival of J. rigida trees in open forests at their northern distribution edges in the face of future climate warming.

Molecular Diversity of Ectomycorrhizal Fungi in Relation to the Diversity of Neighboring Plant Species.

(1) Background: Plant diversity has long been assumed to predict soil microbial diversity. The mutualistic symbiosis between forest trees and ectomycorrhizal (EM) fungi favors strong correlations of EM fungal diversity with host density in terrestrial ecosystems. Nevertheless, in contrast with host tree effects, neighboring plant effects are less well studied. (2) Methods: In the study presented herein, we examined the α-diversity, community composition, and co-occurrence patterns of EM fungi in Quercus acutissima across different forest types (pure forests, mixed forests with Pinus tabuliformis, and mixed forests with other broadleaved species) to ascertain how the EM fungi of focal trees are related to their neighboring plants and to identify the underlying mechanisms that contribute to this relationship. (3) Results: The EM fungal community exhibited an overall modest but positive correlation with neighboring plant richness, with the associations being more pronounced in mixed forests. This neighboring effect was mediated by altered abiotic (i.e., SOC, TN, LC, and LP) and biotic (i.e., bacterial community) factors in rhizosphere soil. Further analysis revealed that Tomentella_badia, Tomentella_galzinii, and Sebacina_incrustans exhibited the most significant correlations with plant and EM fungal diversity. These keystone taxa featured low relative abundance and clear habitat preferences and shared similar physiological traits that promote nutrient uptake through contact, short-distance and medium-distance smooth contact-based exploration types, thereby enhancing the potential correlations between EM fungi and the neighboring plant community. (4) Conclusions: Our findings contribute to the comprehension of the effect of neighboring plants on the EM fungal community of focal trees of different forest communities and the biodiversity sensitivity to environmental change.

Optimal Mapping of Soil Erodibility in a Plateau Lake Watershed: Empirical Models Empowered by Machine Learning

Soil erodibility (K) refers to the inherent ability of soil to withstand erosion. Accurate estimation and spatial prediction of K values are vital for assessing soil erosion and managing land resources. However, as most K-value estimation models are empirical, they suffer from significant extrapolation uncertainty, and traditional studies on spatial prediction focusing on individual empirical K values have neglected to explore the spatial pattern differences between various empirical models. This work proposed a universal framework for selecting an optimal soil-erodibility map using empirical models enhanced by machine learning. Specifically, three empirical models, namely, the erosion-productivity impact calculator model (K_EPIC), the Shirazi model (K_Shirazi), and the Torri model (K_Torri) were used to estimate K values. Random Forest (RF) and Gradient-Boosting Decision Tree (GBDT) algorithms were employed to develop prediction models, which led to the creation of three K-value maps. The spatial distribution of K values and associated environmental covariates were also investigated across varying empirical models. Results showed that RF achieved the highest accuracy, with R2 of K_EPIC, K_Shirazi, and K_Torri increasing by 46%, 34%, and 22%, respectively, compared to GBDT. And distinctions among environmental variables that shape the spatial patterns of empirical models have been identified. The K_EPIC and K_Shirazi are influenced by soil porosity and soil moisture. The K_Torri is more sensitive to soil moisture conditions and terrain location. More importantly, our study has highlighted disparities in the spatial patterns across the three K-value maps. Considering the data distribution, spatial distribution, and measured K values, the K_Torri model outperformed others in estimating soil erodibility in the plateau lake watershed. This study proposed a framework that aimed to create optimal soil-erodibility maps and offered a scientific and accurate K-value estimation method for the assessment of soil erosion.

Deciphering fungicide resistance in Phytophthora: mechanisms, prevalence, and sustainable management approaches.

The genus Phytophthora contains more than 100 plant pathogenic species that parasitize a wide range of plants, including economically important fruits, vegetables, cereals, and forest trees, causing significant losses. Global agriculture is seriously threatened by fungicide resistance in Phytophthora species, which makes it imperative to fully comprehend the mechanisms, frequency, and non-chemical management techniques related to resistance mutations. The mechanisms behind fungicide resistance, such as target-site mutations, efflux pump overexpression, overexpression of target genes and metabolic detoxification routes for fungicides routinely used against Phytophthora species, are thoroughly examined in this review. Additionally, it assesses the frequency of resistance mutations in various Phytophthora species and geographical areas, emphasizing the rise of strains that are resistant to multiple drugs. The effectiveness of non-chemical management techniques, including biological control, host resistance, integrated pest management plans, and cultural practices, in reducing fungicide resistance is also thoroughly evaluated. The study provides important insights for future research and the development of sustainable disease management strategies to counter fungicide resistance in Phytophthora species by synthesizing current information and identifying knowledge gaps.

β-Aminobutyric acid promotes stress tolerance, physiological adjustments, as well as broad epigenetic changes at DNA and RNA nucleobases in field elms (Ulmus minor)

Backgroundβ-Aminobutyric acid (BABA) has been successfully used to prime stress resistance in numerous plant species; however, its effectiveness in forest trees has been poorly explored thus far. This study aimed to investigate the influence of BABA on morphological, physiological, and epigenetic parameters in field elms under various growth conditions. Epigenetic changes were assessed in both DNA and RNA through the use of reversed-phase ultra-performance liquid chromatography (UPLC) coupled with sensitive mass spectrometry.ResultsThe presented results confirm the influence of BABA on the development, physiology, and stress tolerance in field elms. However, the most important findings are related to the broad epigenetic changes promoted by this amino acid, which involve both DNA and RNA. Our findings confirm, for the first time, that BABA influences not only well-known epigenetic markers in plants, such as 5-methylcytosine, but also several other non-canonical nucleobases, such as 5-hydroxymethyluracil, 5-formylcytosine, 5-hydroxymethylcytosine, N6-methyladenine, uracil (in DNA) and thymine (in RNA). The significant effect on the levels of N6-methyladenine, the main bacterial epigenetic marker, is particularly noteworthy. In this case, the question arises as to whether this effect is due to epigenetic changes in the microbiome, the plant genome, or both.ConclusionsThe plant phenotype is the result of complex interactions between the plant’s DNA, the microbiome, and the environment. We propose that different types of epigenetic changes in the plant and microbiome may play important roles in the largely unknown memory process that enables plants to adapt faster to changing environmental conditions.

Effectively removal of PPCPs by catalytic activated biochar derived from hazelnut shell: Modeled and predicted by machine learning

As a new type of environmentally friendly adsorbent, biochar has been increasingly applied in water pollution treatment. Herein, a catalyzed and activated biochar adsorbent (CABC) which was derived from hazelnut shell and modified by FeCl3 and ZnCl2 was synthesized successfully. The CABC was applied to remove two typical Pharmaceutical and Personal Products (PPCPs), sulfamethoxazole (SMX) and diclofenac (DCF). The CABC obtained high specific surface area of 1413.02 m2/g. The removal rates for SMX and DCF by CABC could reach 97.04 % and 98.65 %, respectively. The adsorption process was consistent with the pseudo-second-order kinetic model and the Langmuir isothermal model, which illustrated that the adsorption process conformed to chemical adsorption and monolayer adsorption. The thermodynamics indicated that the adsorption process was endothermic and could occur spontaneously. After 5 cycles, the CABC still remained a high removal efficiency of 77.7 % and 80.5 % for SMX and DCF, which showed a good reusability. The removal rates of CABC for multiple PPCPs had achieved over 77.7–98.0 %. In different actual wastewater, the removal rates of CABC for SMX and DCF ranged from 56.7 % to 97 %, demonstrating its excellent universality and practical value for utilization. Furthermore, three machine learning models based on Decision Tree, Random Forest and Gradient Boosting Decision Tree algorithm were applied to predict the removal efficiency and determine the relative importance of different influencing factors. The gradient boosting regressor model obtained the best predict performance with R2 at 0.9806 and 0.9840 for training set and testing set. And the importance analysis had shown that pH was the most important influencing factor with the importance value over 0.35. The adsorption mechanism between CABC and pollutants included pore adsorption, electrostatic interactions, intramolecular hydrogen bonding, the reaction with oxygen-containing functional groups and metal complexation. The prepared biochar adsorbent CABC possessed the potential for PPCPs removing. In addition, the machine learning method can play more and more undeniable role in subsequent water treatment. In future research, machine learning can be used to predict the adsorption performance of materials, enabling targeted design of materials. This will greatly save experimental time and cost losses, and can improve research efficiency.

Modeling of land use and land cover changes using google earth engine and machine learning approach: implications for landscape management

A precise and up-to-date Land Use and Land Cover (LULC) valuation serves as the fundamental basis for efficient land management. Google Earth Engine (GEE), with its numerous machine learning algorithms, is now the most advanced open-source global platform for rapid and accurate LULC classification. Thus, this study explores the dynamics of the LULC changes between 1993 and 2023 using Landsat imagery and the machine learning algorithms in the Google Earth Engine (GEE) platform. Focus group discussion and key informant interviews were also used to get further data regarding LULC dynamics. Support Vector Machine (SVM), Random Forest (RF), and Classification and Regression Trees (CART) were demonstrated for LULC classification. Six LULC types (agricultural land, grazingland, shrubland, built-up area, forest and bareland) were identified and mapped for 1993, 2003, 2013, and 2023. The overall accuracy and kappa coefficient demonstrated that the RF using images comprising auxiliary variables (spectral indices and topographic data) performed better than SVM and CART. Despite being the most common type of LULC, agricultural land shows a trend of shrinking during the study period. The built-up area and bareland exhibits a trend of progressive expansion. The amount of forest and shrubland has decreased over the last 20 years, whereas grazinglands have exhibited expanding trends. Population growth, agricultural land expansion, fuelwood collection, charcoal production, built-up areas expansion, illegal settlement and intervention are among causes of LULC shifts. This study provides reliable information about the patterns of LULC in the Robit watershed, which can be used to develop frameworks for watershed management and sustainability.

Different patterns of fine-root exudation rates along soil depth of Pinus densiflora, Chamaecyparis obtusa, and Cryptomeria japonica in coniferous forests

Tree fine-root exudation and organic carbon compounds in the soil affect physiological functions through degradation by microorganisms around the roots and the enhancement of allelopathic effects on invasion attempts by other plant species. However, the underlying mechanism of fine-root action in vertically heterogeneous resource-distribution patterns along the soil profile is little known. Here, we quantified root exudation and its morphological and chemical traits in the soil layers down to 100-cm depth for three conifer species, Pinus densiflora (ectomycorrhiza), Chamaecyparis obtusa, and Cryptomeria japonica (arbuscular mycorrhiza), in a cool temperate Japanese forest. By using glass filter-trap method, root exudation rates in all three species varied with soil depth in a species dependent pattern. Specifically, the root exudation rate of P. densiflora was significantly higher in the middle soil layer, and lower in the topsoil layer, whereas the exudation rates significantly decreased in C. japonica and did not vary in C. obtusa along soil depth. Exudation strongly correlated with variation in root traits in C. obtusa and C. japonica but not in P. densiflora. These findings suggest that differences in exudation patterns among tree species are likely compensated for by structural modifications in fine roots in response to soil depth. In addition, the traits with mycorrhizal types might be related to adequate root exudation and enhanced soil-nutrient acquisition. Distinct differences among species in exudation, associated with vertical root distribution, will increase our understanding of root survival strategies for nutrient acquisition and carbon sequestration in forest tree species.