The root-knot nematode, Meloidogyne javanica, is one of the most damaging plant-parasitic nematodes, affecting chickpea and causing substantial yield losses worldwide. The damage potential and population dynamics of this nematode in chickpea in Ethiopia have yet to be investigated. In this study, six chickpea cultivars were tested using 12 ranges of initial population densities (Pi) of M. javanica second-stage juveniles (J2): 0, 0.125, 0.25, 0.5, 1, 2, 4, 8, 16, 32, 64 and 128 J2 (g dry soil)-1 in a controlled glasshouse pot experiment. The Seinhorst yield loss and population dynamics models were fitted to describe population development and the effect on different measured growth variables. The tolerance limit (TTFW) for total fresh weight ranged from 0.05 to 1.22 J2 (g dry soil)-1, with corresponding yield losses ranging from 31 to 64%. The minimum yield for seed weight (mSW) ranged from 0.29 to 0.61, with estimated yield losses of 71 and 39%. The 'Haberu' and 'Geletu' cultivars were considered good hosts, with maximum population densities (M) of 16.27 and 5.64 J2 (g dry soil)-1 and maximum multiplication rate (a) values of 6.25 and 9.23, respectively. All other cultivars are moderate hosts for M. javanica; therefore, it is crucial to initiate chickpea-breeding strategies to manage the tropical root-knot nematode M. javanica in Ethiopia.

The retreat of glaciers due to climate change is reshaping mountain landscapes and biodiversity. While previous research has documented vegetation succession after glacier retreat, our understanding of functional diversity dynamics is still limited. In this case study, we address the effects of glacier retreat on plant functional diversity by integrating plant traits with ecological indicator values across a 140-year chronosequence in a subalpine glacier landscape. We reveal that functional richness and functional dispersion decrease with glacier retreat, while functional evenness and functional divergence increase, suggesting a shift toward more specialized and competitive communities. Our findings highlight the critical role of ecological factors related to soil moisture, soil nutrients and light availability in shaping plant community dynamics. As years since deglaciation was a key factor in regression and machine learning models, encapsulating time-lagged, spatial and historical processes, we highlight the need of including time into phenomenological or mechanistic models predicting biodiversity change following glacier retreat. The integrative approach of this case study provides novel insights into the potential response of alpine plant communities to climate change, offering a deeper understanding of how to predict and anticipate the effects of glacier extinction on biodiversity in rapidly changing environments. (sic)(sic): (sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)140(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic),(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic)(sic).

Aims Human disturbances and environmental changes significantly influence riparian vegetation composition and dynamics by altering hydrological regimes. In high-altitude river systems, snowmelt-driven water-level fluctuations add further complexity to these processes. However, little is known about how riparian plant functional strategies-competitor (C), stress-tolerator (S), and ruderal (R)-respond to dam-induced water-level fluctuations. This study aimed to assess the effects of water-level fluctuations and climatic factors on riparian vegetation functional strategies along the Nyang River, Qinghai-Tibet Plateau.Location This study was conducted along the Nyang River, Qinghai-Tibet Plateau, China. A total of 33 sites were surveyed, spanning upstream, reservoir, and downstream zones, which were categorized based on water-level fluctuation gradients.Methods We classified riparian vegetation functional strategies using Grime's CSR framework based on species trait data. Hydrological and climatic variables, including water-level fluctuations, temperature, precipitation, and snow cover, were derived from the Global Surface Water Mapping Layers and the FLDAS dataset. Soil properties were measured in the field. Redundancy analysis and partial least squares path modeling were applied to identify key drivers of CSR variation across different river zones.Results Riparian vegetation exhibited significant differences in stress tolerance and ruderal strategies across the Nyang River. Plants in the reservoir and upstream zones had higher S-strategy values, whereas downstream vegetation was predominantly characterized by high competitiveness. The primary drivers of CSR variation across the catchment were temperature, precipitation, and snow cover. In reservoir zones, water-level fluctuations (e.g., retention time, river width) were the dominant influences, whereas downstream vegetation was mainly governed by climate variables. In upstream zones, precipitation, water-level fluctuations, and snow cover jointly influenced CSR strategies. Water-level fluctuations directly regulated functional strategies, whereas snow cover had both direct effects and indirect effects via soil moisture changes.Conclusions This study highlights the interactive effects of climate change and flow regulation on riparian vegetation functional strategies in high-altitude river systems. The findings provide critical insights into how water-level fluctuations and climatic factors shape riparian plant strategies, offering valuable information for ecosystem-based river management and conservation in alpine regions.

Debris flows are a type of natural disaster induced by vegetation-water-soil coupling under external dynamic conditions. Research on the mechanism by which underground plant roots affect the initiation of gulley debris flows is currently limited. To explore this mechanism, we designed 14 groups of controlled field-based simulation experiments. Through monitoring, analysis, calculation, and simulation of the changes in physical parameters, such as volumetric water content, pore-water pressure, and matric suction, during the debris flow initiation process, we revealed that underground plant roots change the pore structure of soil masses. This affects the response time of pore-water pressure to volumetric water content, as well as hydrological processes within soil masses before the initiation of gully debris flows. Underground plant roots increase the peak volumetric water content of rock and soil masses, reduce the rates of increase of volumetric water content and pore-water pressure, and increase the dissipation rate of pore-water pressure. Our results clarify the influence of underground roots on the initiation of gulley debris flows, and also provide support for the initiation warning of gully debris flow. When the peak value of stable volumetric water content is taken as the early warning value, the early warning time of soil with underground plant roots is delayed by 534 to 1253 s. When the stable peak value of pore-water pressure is taken as the early warning value, the early warning time of soil with underground plant roots is delayed by 193 to 1082 s. This study provides a basis for disaster prevention and early warning of gully debris flows in GLP, and also provides ideas and theoretical basis under different vegetation-cover conditions area similar to GLP.

This study was carried out to evaluate the interaction between terrestrial food crop plants and microplastics (MPs) with a focus on understanding their uptake, effects on growth, physiological, biochemical, and yield characteristics of two different cultivars of Solanum tuberosum L. i.e., Variety-1, Astrix (AL-4) and Variety-2, Harmes (WA-4). Polyethylene (PE), polystyrene (PS), and polypropylene (PP) spheres of size 5 mu m were applied to the soil at concentrations of 0 %, 1 %, and 5 %. Morphological parameters, including seed germination rate, shoot and root lengths, leaf area, and fresh and dry biomass of plants, got reduced significantly with the increase in MP concentration. PS MPs caused the most negative impact, particularly at 5 %, leading to the greatest decline in growth and Na, Mg, Zn, Cu, Ni, and Mn nutrient content. The highest DPPH scavenging activity was observed in the 5 % PS MPs treatment with approximately a 45.34 % increase from the control, indicating its potential to enhance antioxidant activity in response to stress caused by PS MPs. Both reducing and non-reducing sugar contents and total proteins were also decreased significantly. Vitamin C content exhibited a significant increase in response to MPs, with the highest levels recorded under 5 % PS MPs treatments. This suggests an adaptive antioxidant response to mitigate oxidative damage induced by MPs. SEM analysis revealed tissue infiltration of MP particles in shoots, leaves, and tubers of both varieties. Among MPs, PS had the most detrimental effects, followed by PP and PE, with higher concentrations increasing the negative impact.

Tree destruction induced by heavy rainfall, an overlooked type of forest degradation, has been exacerbated along with global climate change. On the Chinese Loess Plateau, especially in afforested gully catchments dominated by Robinia pseudoacacia, destructive rainfall events have increasingly led to widespread forest damage. Previous study has manifested the severity of heavy rainfall-induced tree destruction and its association with topographic change, yet the contributions of tree structure and forest structure remain poorly understood. In this study, we quantified the destroyed trees induced by heavy rainfall using light detection and ranging (LiDAR) techniques. We assessed the influence of tree structure (tree height, crown diameter, and crown area), forest structure (tree density, gap fraction, leaf area index, and canopy cover), and terrain parameters (elevation, slope, and terrain relief) using machine learning models (random forest and logistic regression). Based on these, we aimed to clarify the respective and combined contributions of structural and topographic factors to rainfall-induced tree destruction. Key findings revealed that when considered in isolation, greater tree height, crown diameter, crown area, leaf area index (LAI), and canopy cover suppressed tree destruction, whereas higher gap fractions increased the probability of tree destruction. However, the synergistic increases of tree structural factors (tree height, crown diameter, and crown area) and forest structural factors (LAI and canopy cover) significantly promoted tree destruction, which can counteract the inhibitory effect of terrain on destruction. In addition, increases in tree structure or canopy density (LAI and canopy cover) also increased the probability of tree destruction at the same elevation. Our findings challenge conventional assumptions in forest management by demonstrating the interaction of tree structure and canopy density can significantly promote tree destruction during heavy rainfall. This highlights the need to avoid overly dense afforestation in vulnerable landscapes and supports more adaptive, climate-resilient restoration strategies.

This study integrates a dynamic plant growth model with a three-dimensional (3D) radiative transfer model (RTM) for maize traits retrieval using high spatial-spectral resolution airborne data. The research combines the Discrete Anisotropic Radiative Transfer (DART) model with the Dynamic L-System-based Architectural maize (DLAmaize) growth model to simulate field reflectance. Comparison with the 1D RTM SAIL revealed limitations in representing row structure effects, field slope, and complex light-canopy interactions. Novel Global Sensitivity Analyses (GSA) were carried out using dependence-based methods to overcome limitations traditional variance-based approaches, enabling better characterization of hyperspectral sensitivity to changes in leaf biochemistry, canopy architecture, and soil moisture. GSA provided complementary results to assess estimation uncertainties of the proposed traits retrieval method across growth stages. A hybrid inversion framework combining DART simulations with an active learning strategy using Kernel Ridge Regression was implemented for traits estimation. The approach was validated using ground data and HyPlant-DUAL airborne hyperspectral images from two field campaigns in 2018 and achieved high retrieval accuracy of key maize traits: leaf area index (LAI, R2=0.91, RMSE=0.42 m2/m2), leaf chlorophyll content (LCC, R2=0.61, RMSE=3.89 mu g/cm2), leaf nitrogen content (LNC, R2=0.86, RMSE=1.13 x 10-2 mg/cm2), leaf dry matter content (LMA, R2=0.84, RMSE=0.15 mg/cm2), and leaf water content (LWC, R2=0.78, RMSE=0.88 mg/cm2). The validated models were used to generate two-date 10 m resolution maps, showing good spatial consistency and traits dynamics. The findings demonstrate that integrating 3D RTMs with dynamic growth models is suited for maize trait mapping from hyperspectral data in varying growing conditions.

Root-knot nematodes (RKN) are globally distributed and highly pathogenic. By determining the threshold at which damage occurs, we can create effective measures to protect plants from nematodes. In our study, we investigated the impact of ten initial population densities (Pi-log series) of M. javanica, i.e., 0, 2.38, 2.68, 2.98, 3.28, 3.58, 3.88, 4.18, 4.48 and 4.78 juveniles (J2) g(-1) soil on tomato cv. S22 plants in pots. The graphical estimation of yield losses caused by RKN was calculated using Seinhorst's yield loss model based on the relationship between the RKN population and damage to tomato plants. The relationship between initial nematode population density (Pi) and plant yield was analyzed using Seinhorst's model, where T is the tolerance limit, m is the minimum yield, and z is a constant describing yield decline. This allowed us to determine the threshold at which nematode infestation significantly reduces tomato growth. Seinhorst's model, y = m + (1-m) 0.95(Pi/T-1) for Pi > T; y = 1 for Pi <= T for RKN, was fitted to the data of shoot length and fresh weight of infected and uninoculated control plants to estimate the damage threshold level. The impact of M. javanica on plant physiological parameters, including chlorophyll content, carotenoid and nitrate reductase activity, root-gall formation, and disease incidence, was also determined in this study. The tolerance limits for relative tomato shoot length and fresh weight were 3.34 J2 of M. javanica g(-1) soil. The minimum relative values (y(m)) for shoot length and fresh weights were 0.39 and 0.42, respectively. We found that the damage threshold level was between 3.28 and 3.58. The root galls index, nematode population and reproduction factors were 3.75, 113 and 29.42, respectively, at an initial population density (Pi) of 3.58 J2 g(-1) soil. The chlorophyll (0.43 mg g(-1)), carotenoids (0.06 mg g(-1)) and nitrate reductase activity (0.21 mu mol min(-1) g(-1)). Our study highlights the importance of the accurate estimation of damage thresholds, which can guide timely and effective nematode management strategies.

Plant-parasitic nematodes pose a silent yet devastating threat to global agriculture, causing significant yield losses and economic damage. Traditional detection methods such as soil sampling, microscopy, and molecular diagnostics are slow, labor-intensive, and often ineffective in early-stage infestations. Nano biosensors: cuttingedge analytical tools that leverage nanomaterials like carbon nanotubes, graphene, and quantum dots to detect nematode-specific biochemical markers such as volatile organic compounds (VOCs) and oesophageal gland secretions, with unprecedented speed and accuracy. The real breakthrough lies in the fusion of artificial intelligence (AI) and nano-biosensor technology, forging a new frontier in precision agriculture. By integrating AI's powerful data analysis, pattern recognition, and predictive capabilities with the extraordinary sensitivity and specificity of nano-biosensors, it becomes possible to detect biomolecular changes in real-time, even at the earliest stages of disease progression. AI-driven nano biosensors can analyze real-time data, enhance detection precision, and provide actionable insights for farmers, enabling proactive and targeted pest management. This synergy revolutionizes nematode monitoring, paving the way for smarter, more sustainable agricultural practices. This review explores the transformative potential of AI-powered nano-biosensors in advancing precision agriculture. By integrating these technologies with smart farming systems, we move closer to real-time, costeffective, and field-deployable solutions, ushering in a new era of high-tech, eco-friendly crop protection.

Soil-plant-atmosphere interaction (SPAI) plays a significant role on the safety and serviceably of geotechnical infrastructure. The mechanical and hydraulic soil behaviour varies with the soil water content and pore water pressures (PWP), which are in turn affected by vegetation and weather conditions. Focusing on the hydraulic reinforcement that extraction of water through the plant roots offers, this study couples advances in ecohydrological modelling with advances in geotechnical modelling, overcoming previous crude assumptions around the application of climatic effects on the geotechnical analysis. A methodology for incorporating realistic ecohydrological effects in the geotechnical analysis is developed and validated, and applied in the case study of a cut slope in Newbury, UK, for which field monitoring data is available, to demonstrate its successful applicability in boundary value problems. The results demonstrate the positive effect of vegetation on the infrastructure by increasing the Factor of Safety. Finally, the effect of climate change and changes in slope vegetation cover are investigated. The analysis results demonstrate that slope behaviour depends on complex interactions between the climate and the soil hydraulic properties and cannot be solely anticipated based on climate data, but suctions and changes in suction need necessarily to be considered.