Search Results (1,144)

Monitoring the height of secondary forest regrowth is essential for assessing ecosystem recovery, but current methods rely on field surveys, airborne or UAV LiDAR, and 3D reconstruction from high-resolution UAV imagery, which are often costly or limited by logistical constraints. Here, we address the challenge of scaling up canopy height monitoring by evaluating a recent deep learning model, trained on data from the Amazon and Atlantic Forests, developed to extract canopy height from RGB-NIR Planet NICFI imagery. The research questions are as follows: (i) How are canopy height estimates from the model affected by slope and orientation in natural forests, based on a large and well-balanced experimental design? (ii) How effectively does the model capture the growth trajectories of Pinus and Eucalyptus plantations over an eight-year period following planting? We find that the model closely tracks Pinus growth at the parcel scale, with predictions generally within one standard deviation of UAV-derived heights. For Eucalyptus, while growth is detected, the model consistently underestimates height, by more than 10 m in some cases, until late in the cycle when the canopy becomes less dense. In stable natural forests, the model reveals seasonal artifacts driven by topographic variables (slope × aspect × day of year), for which we propose strategies to reduce their influence. These results highlight the model’s potential as a cost-effective and scalable alternative to field-based and LiDAR methods, enabling broad-scale monitoring of forest regrowth and contributing to innovation in remote sensing for forest dynamics assessment. Full article

Over the past few decades, lithium-ion batteries (LIBs) have gained significant attention due to their inherent potential for environmental sustainability and unparalleled energy storage efficiency. Meanwhile, polymer electrolytes have gained popularity in several fields due to their ability to adapt to various battery geometries, enhanced safety features, greater thermal stability, and effectiveness in reducing dendrite growth on the anode. However, their relatively low ionic conductivity compared to liquid electrolytes has limited their application in high-performance devices. This limitation has led to recent studies revolving around the development of poly(ionic liquids) (PILs), particularly imidazolium-mediated polymer backbones as novel electrolyte materials, which can increase the conductivity with fine-tuning structural benefits, while maintaining the advantages of both solid and gel electrolytes. In this study, a curated dataset of 120 data points representing eight different polymers was used to predict ionic conductivity in imidazolium-based PILs as well as the emerging ionene substructures. For this purpose, four ML models: CatBoost, Random Forest, XGBoost, and LightGBM were employed by incorporating chemical structure and temperature as the models’ inputs. The best-performing model was further employed to estimate the conductivity of novel ionenes, offering insights into the potential of advanced polymer architectures for next-generation LIB electrolytes. This approach provides a cost-effective and intelligent pathway to accelerate the design of high-performance electrolyte materials. Full article

Tropical forests continue to experience high levels of habitat loss and degradation, with wildfires becoming a frequent component of human-modified landscapes. Here we investigate the response of palm species to the conversion of old-growth forests to successional mosaics, including forest patches burned during wildfires. Palms (≥50 cm height) were recorded once in 2023–2024, across four habitat classes: terra firme old-growth stands, regenerating forest stands associated with slash-and-burn agriculture, old-growth stands burned once and twice, and active cassava fields, in the Tapajós-Arapiuns Extractive Reserve, in the eastern Brazilian Amazon. The flammability of palm leaf litter and forest litter were also examined to assess the potential connections between palm proliferation and wildfires. A total of 10 palm species were recorded in this social forest (including slash-and-burn agriculture and resulting successional mosaics), with positive, negative, and neutral responses to land use. Species richness did not differ among forest habitats, but absolute palm abundance was greatest in disturbed habitats. Only Attalea spectabilis Mart. (curuá) exhibited increased relative abundance across disturbed habitats, including active cassava field. Attalea spectabilis accounted for almost 43% of all stems in the old-growth forest, 89% in regenerating forests, 90% in burned forests, and 79% in crop fields. Disturbed habitats supported a five-to-ten-fold increment in curuá leaves as a measure of habitat flammability. Although curuá litter exhibited lower flame temperature and height, its lower carbon and higher volatile content is expected to be more sensitive to fire ignition and promote the spread of wildfires. The conversion of old-growth forests into social forests promotes the establishment of palm-dominated forests, increasing the potential for a forest transition further fueled by wildfires, with effects on forest resilience and social reproduction still to be understood. Full article

Understanding species composition and forest dynamics is essential for predicting biomass productivity and informing conservation in tropical montane ecosystems. We evaluated floristic, demographic, and biomass changes in eighteen 0.1 ha permanent plots in the Colombian Sub-Andean forest, including both primary (ca. 60 y old) and secondary forests (ca. 30 years old). Two censuses of individuals (DBH ≥ 2.5 cm) were conducted over 7–13 years. We recorded 516 species across 202 genera and 89 families. Floristic composition differed significantly between forest types (PERMANOVA, p = 0.001), and black oak (Trigonobalanus excelsa Lozano, Hern. Cam. & Henao) forests formed distinct assemblages. Demographic rates were higher in secondary forests, with mortality (4.17% yr), recruitment (4.51% yr), and relative growth rate (0.02% yr) exceeding those of primary forests. The mean aboveground biomass accumulation and the rate of annual change were higher in primary forests (447.5 Mg ha⁻¹ and 466.8 Mg ha⁻¹ yr⁻¹, respectively) than in secondary forests (217.2 Mg ha⁻¹ and 217.2 Mg ha⁻¹ yr⁻¹, respectively). Notably, black oak forests showed the greatest biomass accumulation and rate of change in biomass. Annual net biomass production was higher in secondary forests (8.72 Mg ha⁻¹ yr⁻¹) than in primary forests (5.66 Mg ha⁻¹ yr⁻¹). These findings highlight the ecological distinctiveness and recovery potential of secondary Sub-Andean forests and underscore the value of multitemporal monitoring to understand forest resilience and assess vulnerability to environmental change. Full article

Timely and accurate monitoring of crop nitrogen (N) status is essential for precision agriculture. UAV-based hyperspectral remote sensing offers high-resolution data for estimating plant nitrogen concentration (PNC), but its cost and complexity limit large-scale application. This study compares the performance of UAV hyperspectral data (S185 sensor) with simulated multispectral data from DJI Phantom 4 Multispectral (P4M), PlanetScope (PS), and Sentinel-2A (S2) in estimating winter wheat PNC. Spectral data were collected across six growth stages over two seasons and resampled to match the spectral characteristics of the three multispectral sensors. Three variable selection strategies (one-dimensional (1D) spectral reflectance, optimized two-dimensional (2D), and three-dimensional (3D) spectral indices) were combined with Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), and Partial Least Squares Regression (PLSR) to build PNC prediction models. Results showed that, while hyperspectral data yielded slightly higher accuracy, optimized multispectral indices, particularly from PS and S2, achieved comparable performance. Among models, SVM and RFR showed consistent effectiveness across strategies. These findings highlight the potential of low-cost multispectral platforms for practical crop N monitoring. Future work should validate these models using real satellite imagery and explore multi-source data fusion with advanced learning algorithms. Full article

A newly emerging disease affecting Schizolobium parahyba (commonly known as pachaco), termed “decline and dieback,” has been reported in association with the fungal pathogens Fusarium sp. and Botryodiplodia sp. This study assessed the antagonistic potential of two Trichoderma sp. isolates (CEP-01 and CEP-02) against these phytopathogens under controlled laboratory conditions. The effects of three temperature regimes (5 ± 2 °C, 24 ± 2 °C, and 30 ± 2 °C) on the growth and inhibitory activity of two Trichoderma spp. isolates were evaluated using a completely randomized design. The first experiment included six treatments with five replicates, while the second comprised twelve treatments, also with five replicates. All assays were conducted on PDA medium. No fungal growth was observed at 5 ± 2 °C. However, at 24 ± 2 °C and 30 ± 2 °C, both isolates reached maximum growth within 72 h. At 24 ± 2 °C, both Trichoderma spp. isolates exhibited inhibitory activity against Fusarium sp. FE07 and FE08, with radial growth inhibition percentages (RGIP) ranging from 37.6% to 44.4% and 52,8% to 54.6%, respectively. When combined, the isolates achieved up to 60% inhibition against Fusarium sp., while Botryodiplodia sp. was inhibited by 40%. At 30 ± 2 °C, the antagonistic activity of Trichoderma sp. CEP-01 declined (25.6–32.4% RGIP), whereas Trichoderma sp. CEP-02 showed increased inhibition (60.3%–67.2%). The combination of isolates exhibited the highest inhibitory effect against Fusarium sp. FE07 and FE08 (68.4%–69.3%). Nonetheless, the inhibitory effect on Botryodiplodia sp. BIOT was reduced under elevated temperatures across all treatments. These findings reinforce the potential of Trichoderma spp. isolates as a viable and eco-friendly alternative for the biological control of pathogens affecting S. parahyba, contributing to more sustainable disease management practices. The observed inhibitory capacity of Trichoderma sp., especially under optimal temperature conditions, highlights its potential for application in integrated disease management programs, contributing to forest health and reducing reliance on chemical products. Full article

Climbing vines have recently induced increasing threats to forest growth under favourable environmental changes. In mangrove forests, the native vine Derris trifoliata became invasive and is now one of the main threats. Yet current management relies on manual removal with low efficiency. Exploring an alternative, cost-effective method is required. To assess the potential of a proposed biological control method, this study performed a pot-plant experiment using Cuscuta japonica to infect D. trifoliata and three common mangrove species in Beihai, China. Results showed that D. trifoliata had a higher infection rate and high host mortality (90%) than mangrove (0%). It also had significantly decreased moisture by 4%, nitrogen by 14%, phosphorus by 27%, potassium by 49% and increased soluble sugar by 49% and protein by 20%, whereas only moisture (2% reduction) and one or two minerals of Excoecaria agallocha and Aegiceras corniculatum were influenced. Only Kandelia obovata had neither effective haustoria nor any nutrients impact from the infection. This study indicated that C. japonica can cause more damage to D. trifoliata than to mangrove species and has the potential to be used as a biological control agent for the threatened mangrove forests of A. corniculatum and K. obovata with monitoring and control. Further field tests are required to bring this method into practice. Full article

Phosphorus (P) availability is a major constraint on plant growth in many forest ecosystems, yet the strategies by which different tree species acquire and utilize various forms of soil phosphorus remain poorly understood. This study investigated how coexisting tree species with contrasting mycorrhizal types, specifically arbuscular mycorrhizal (AM) and ectomycorrhizal (ECM) associations, respond to different phosphorus forms under field conditions. An in situ root bag experiment was conducted using four phosphorus treatments (control, inorganic, organic, and mixed phosphorus) across four subtropical tree species. A comprehensive set of fine root traits, including morphological, physiological, and mycorrhizal characteristics, was measured to evaluate species-specific phosphorus foraging strategies. The results showed that AM species were more responsive to phosphorus form variation than ECM species, particularly under inorganic and mixed phosphorus treatments. Significant changes in root diameter (RD), root tissue density (RTD), and acid phosphatase activity (RAP) were observed in AM species, often accompanied by higher phosphorus accumulation in fine roots. For example, RD in AM species significantly decreased under the Na₃PO₄ treatment (0.94 mm) compared to the control (1.18 mm), while ECM species showed no significant changes in RD across treatments (1.12–1.18 mm, p > 0.05). RTD in AM species significantly increased under Na₃PO₄ (0.030 g/cm³) and Mixture (0.021 g/cm³) compared to the control (0.012 g/cm³, p < 0.05), whereas ECM species exhibited consistently low RTD values across treatments (0.017–0.020 g/cm³, p > 0.05). RAP in AM species increased significantly under Na₃PO₄ (1812 nmol/g/h) and Mixture (1596 nmol/g/h) relative to the control (1348 nmol/g/h), while ECM species showed limited variation (1286–1550 nmol/g/h, p > 0.05). In contrast, ECM species displayed limited trait variation across treatments, reflecting a more conservative acquisition strategy. In addition, trait correlation analysis revealed stronger coordination among root traits in AM species. And AM species exhibited high variability across treatments, while ECM species maintained consistent trait distributions with limited plasticity. These findings suggest that AM and ECM species adopt fundamentally different phosphorus acquisition strategies. AM species rely on integrated morphological and physiological responses to variable phosphorus conditions, while ECM species maintain stable trait configurations, potentially supported by fungal symbiosis. Such divergence may contribute to functional complementarity and species coexistence in phosphorus-limited subtropical forests. Full article

Climate change alters plant biomass allocation and aboveground–belowground trade-offs in grassland ecosystems, potentially affecting critical functions such as carbon sequestration. However, uncertainties persist regarding how precipitation gradients regulate (1) responses of aboveground biomass (AGB), belowground biomass (BGB), and total biomass in alpine grasslands, and (2) precipitation-mediated AGB-BGB allocation strategies. To address this, we conducted a large-scale field survey across precipitation gradients (400–700 mm/y) in the Sanjiangyuan alpine grasslands, Qinghai–Tibet Plateau. During the 2024 growing season, a total of 63 sites (including 189 plots and 945 quadrats) were sampled along five aridity classes: <400, 400–500, 500–600, 600–700, and >700 mm/y. Our findings revealed precipitation as the dominant driver of biomass dynamics: AGB exhibited equal growth rates relative to BGB within the 600–700 mm/y range, but accelerated under drier/wetter conditions. This suggests preferential allocation to aboveground parts under most precipitation regimes. Precipitation explained 31.71% of AGB–BGB trade-off variance (random forest IncMSE), surpassing contributions from AGB (17.61%), specific leaf area (SLA, 13.87%), and BGB (12.91%). Structural equation modeling confirmed precipitation’s positive effects on SLA (β = 0.28, p < 0.05), AGB (β = 0.53, p < 0.05), and BGB (β = 0.60, p < 0.05), with AGB-mediated cascades (β = 0.33, p < 0.05) dominating trade-off regulation. These results advance our understanding of mechanistic drivers governing allometric AGB–BGB relationships across climatic gradients in alpine ecosystems of the Sanjiangyuan Region on the Qinghai–Tibet Plateau. Full article

Chlorophyll plays a vital role in wheat growth and fertilization management. Accurate and efficient estimation of chlorophyll content is crucial for providing a scientific foundation for precision agricultural management. Unmanned aerial vehicles (UAVs), characterized by high flexibility, spatial resolution, and operational efficiency, have emerged as effective tools for estimating chlorophyll content in wheat. Although multi-source data derived from UAV-based multispectral imagery have shown potential for wheat chlorophyll estimation, the importance of multi-source deep feature fusion has not been adequately addressed. Therefore, this study aims to estimate wheat chlorophyll content by integrating spectral and textural features extracted from UAV multispectral imagery, in conjunction with partial least squares regression (PLSR), random forest regression (RFR), deep neural network (DNN), and a novel multi-source deep feature neural network (MDFNN) proposed in this research. The results demonstrate the following: (1) Except for the RFR model, models based on texture features exhibit superior accuracy compared to those based on spectral features. Furthermore, the estimation accuracy achieved by fusing spectral and texture features is significantly greater than that obtained using a single type of data. (2) The MDFNN proposed in this study outperformed other models in chlorophyll content estimation, with an R² of 0.850, an RMSE of 5.602, and an RRMSE of 15.76%. Compared to the second-best model, the DNN (R² = 0.799, RMSE = 6.479, RRMSE = 18.23%), the MDFNN achieved a 6.4% increase in R², and 13.5% reductions in both RMSE and RRMSE. (3) The MDFNN exhibited strong robustness and adaptability across varying years, wheat varieties, and nitrogen application levels. The findings of this study offer important insights into UAV-based remote sensing applications for estimating wheat chlorophyll under field conditions. Full article

Stem radius growth (GRO), tree water deficit (TWD), and maximum daily shrinkage (MDS) were monitored throughout 2023 in a semi-arid Mediterranean forest stand in Burdur, Türkiye, where Pinus nigra subsp. pallasiana (Lamb.) Holmboe and Pinus brutia Ten. naturally co-occur. These indicators, derived from electronic band dendrometers, were analyzed in relation to key climatic variables. Results indicated that P. brutia had a longer growth period, while P. nigra exhibited a higher average daily increment under the environmental conditions of 2023 at the study site. Annual stem growth was nearly equal for both species. Based on dendrometer observations, P. brutia exhibited lower normalized TWD and higher normalized MDS values under varying vapor pressure deficit (VPD) and soil water potential (SWP) conditions. A linear mixed-effects model further confirmed that P. brutia consistently maintained lower TWD than P. nigra across a wide climatic range, suggesting a comparatively lower degree of drought-induced water stress. GRO was most influenced by air temperature and VPD, and negatively by SWP. TWD was strongly affected by both VPD and SWP, while MDS was primarily linked to minimum air temperature and VPD. Moreover, MDS in P. brutia appeared more sensitive to climate variability compared to P. nigra. Although drought limited stem growth in both species during the study year, the lower TWD and higher MDS observed in P. brutia may indicate distinct physiological strategies for coping with drought. These findings offer preliminary insights into interspecific differences in water regulation under the particular climatic conditions observed during the study year in this semi-arid Mediterranean ecosystem. Full article

Plant microbiomes are vital for the growth and health of their host. Tree-associated microbiomes are shaped by multiple factors, of which the host is one of the key determinants. Whether different host genotypes affect the structure and diversity of the tissue-associated microbiome and how specific taxa enriched in different tree tissues are not yet well illustrated. Chinese fir (Cunninghamia lanceolata) is an important tree species for both economy and ecosystem in the subtropical regions of Asia. In this study, we investigated the tissue-specific fungal community structure and diversity of nine different Chinese fir genotypes (39 years) grown in the same field. With non-metric multidimensional scaling (NMDS) analysis, we revealed the divergence of the fungal community from rhizosphere soil (RS), fine roots (FRs), and thick roots (TRs). Through analysis with α-diversity metrics (Chao1, Shannon, Pielou, ACE, Good‘s coverage, PD-tree, Simpson, Sob), we confirmed the significant difference of the fungal community in RS, FR, and TR samples. Yet, the overall fungal community difference was not observed among nine genotypes for the same tissues (RS, FR, TR). The most abundant fungal genera were Russula in RS, Scytinostroma in FR, and Subulicystidium in TR. Functional prediction with FUNGuild analysis suggested that ectomycorrhizal fungi were commonly enriched in rhizosphere soil, while saprotroph–parasite and potentially pathogenic fungi were more abundant in root samples. Specifically, genotype N104 holds less ectomycorrhizal and pathogenic fungi in all tissues (RS, FR, TR) compared to other genotypes. Additionally, significant correlations of several endophytic fungal taxa (Scytinostroma, Neonothopanus, Lachnum) with the growth traits (tree height, diameter, stand volume) were observed. This addresses that the interaction between tree roots and the fungal community is a reflection of tree growth, supporting the “trade-off” hypothesis between growth and defense in forest trees. In summary, we revealed tissue-specific, as well as host genotype-specific and genotype-common characters of the structure and functions of their fungal communities. Full article

Elevational gradients in temperature, moisture, and vegetation strongly influence soil nutrient content and stoichiometry in mountainous regions. However, exactly how total, microbial, and enzymatic carbon (C), nitrogen (N), and phosphorus (P) stoichiometry vary with elevation in karst peak-cluster depressions remains poorly understood. To address this, we studied soil total, microbial, and enzymatic C:N:P stoichiometry in seasonal rainforests within karst peak-cluster depressions in southwestern China at different elevations (200, 300, 400, and 500 m asl) and depths (0–20 and 20–40 cm). We found that soil organic carbon (SOC), total nitrogen (TN), and the C:P and N:P ratios increased significantly with elevation, whereas total phosphorus (TP) decreased. Microbial phosphorus (MBP) also declined with elevation, while the microbial N:P ratio rose. Activities of nitrogen- (β-N-acetylglucosaminidase and L-leucine aminopeptidase combined) and phosphorus-related enzymes (alkaline phosphatase) increased markedly with elevation, suggesting potential phosphorus limitation for plant growth at higher elevations. Our results suggest that total, microbial, and enzymatic soil stoichiometry are collectively shaped by topography and soil physicochemical properties, with elevation, pH, and exchangeable calcium (ECa) acting as the key drivers. Microbial stoichiometry exhibited positive interactions with soil stoichiometry, while enzymatic stoichiometry did not fully conform to the expectations of resource allocation theory, likely due to the functional specificity of phosphatase. Overall, these findings enhance our understanding of C–N–P biogeochemical coupling in karst ecosystems, highlight potential nutrient limitations, and provide a scientific basis for sustainable forest management in tropical karst regions. Full article

The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. It also plays a crucial role in evaluating tree growth dynamics, which are closely linked to tree health, structural stability, and vulnerability. Although a range of devices and methodologies are currently under investigation, the widespread adoption of laser scanners remains constrained by their high cost. This study therefore aimed to compare high-end laser scanners (Trimble TX8 and GeoSLAM ZEB Horizon) with cost-effective alternatives, represented by the Apple iPhone 14 Pro and the LA03 scanner developed by mapry Co., Ltd. (Tamba, Japan). It further sought to evaluate the feasibility of employing these more affordable devices, even for small-scale forest owners or managers. Given the growing availability of 3D-based forest inventory algorithms, a selection of such processing pipelines was used to assess the practical potential of the scanning devices. The tested low-cost device produced moderate results, achieving a tree detection rate of up to 78% and a relative root mean square error (rRMSE) of 19.7% in diameter at breast height (DBH) estimation. However, performance varied depending on the algorithms applied. In contrast, the high-end mobile laser scanning (MLS) and terrestrial laser scanning (TLS) systems outperformed the low-cost alternative across all metrics, with tree detection rates reaching up to 99% and DBH estimation rRMSEs as low as 5%. Nevertheless, the low-cost device may still be suitable for scanning small sample plots at a reduced cost and could potentially be deployed in larger quantities to support broader forest inventory initiatives. Full article

Although treeline ecotones are significant components of vulnerable mountain ecosystems and key indicators of climate change, treelines of the Southern Hemisphere remain largely outside of research focus. In this study, we investigate, for the first time, the current and future distribution of the treeline species Nothofagus pumilio in the Southern Andes using a Species Distribution Modelling approach. The lack of modelling studies in this region can be contributed to missing occurrence data for the species. In a preliminary study, both point and raster data were generated using a novel Instagram ground truthing approach and remote sensing. Here we tested the performance of the two datasets: a typical binary species dataset consisting of occurrence points and pseudo-absence points and a continuous dataset where species occurrence was determined by supervised classification. We used a Random Forest (RF) classification and a RF regression approach. RF is applicable for both datasets, has a very good performance, handles multicollinearity and remains largely interpretable. We used bioclimatic variables from CHELSA as predictors. The two models differ in terms of variable importance and spatial prediction. While a temperature variable is the most important variable in the RF classification, the RF regression model was mainly modelled by precipitation variables. Heat deficiency is the most important limiting factor for tree growth at treelines. It is evident, however, that water availability and drought stress will play an increasingly important role for the future competitiveness of treeline species and their distribution. Modelling with binary presence–absence point data in the RF classification model led to an overprediction of the potential distribution of the species in summit regions and in glacier areas, while the RF regression model, trained with continuous raster data, led to a spatial prediction with small-scale details. The time-consuming and costly acquisition of complex species information should be accepted in order to provide better predictions and insights into the potential current and future distribution of a species. Full article