Understanding the spatiotemporal dynamics of microbial communities is essential for predicting their ecological roles and interactions with host plants. In a recent study, Wei and colleagues (Microbiol Spectr 13:e02097-24, 2024) investigated fungal diversity across multiple plant and soil compartments in rubber trees over two seasons and two geographically distinct regions in China. Their findings revealed that alpha diversity was primarily influenced by seasonal changes and physicochemical factors, while beta diversity exhibited a strong geographical pattern, shaped by leaf phosphorus and soil available potassium. These results highlight the role of environmental drivers in shaping within-community diversity, while other factors contribute to the differences between fungal communities across the soil-plant continuum. By distinguishing the effects of temporal and spatial factors, this study provides detailed insights into plant-associated microbiomes and emphasizes the need for further research on the functional implications of microbial diversity in the context of changing environmental and agricultural conditions.

Climate change impacts water supply dynamics in the Upper Rio Grande (URG) watersheds of the US Southwest, where declining snowpack and altered snowmelt patterns have been observed. While temperature and precipitation effects on streamflow often receive the primary focus, other hydroclimate variables may provide more specific insight into runoff processes, especially at regional scales and in mountainous terrain where snowpack is a dominant water storage. The study addresses the gap by examining the mechanisms of generating streamflow through multi-modal inferences, coupling the Bayesian Information Criterion (BIC) and Bayesian Model Averaging (BMA) techniques. We identified significant streamflow predictors, exploring their relative influences over time and space across the URG watersheds. Additionally, the study compared the BIC-BMA-based regression model with Random Forest Regression (RFR), an ensemble Machine Learning (RFML) model, and validated them against unseen data. The study analyzed seasonal and long-term changes in streamflow generation mechanisms and identified emergent variables that influence streamflow. Moreover, monthly time series simulations assessed the overall prediction accuracy of the models. We evaluated the significance of the predictor variables in the proposed model and used the Gini feature importance within RFML to understand better the factors driving the influences. Results revealed that the hydroclimate drivers of streamflow exhibited temporal and spatial variability with significant lag effects. The findings also highlighted the diminishing influence of snow parameters (i. e., snow cover, snow depth, snow albedo) on streamflow while increasing soil moisture influence, particularly in downstream areas moving towards upstream or elevated watersheds. The evolving dynamics of snowmelt-runoff hydrology in this mountainous environment suggest a potential shift in streamflow generation pathways. The study contributes to the broader effort to elucidate the complex interplay between hydroclimate variables and streamflow dynamics, aiding in informed water resource management decisions.

Moisture-driven landslides (MDL) are typically associated with elevated soil moisture content and sub-surface pore-water pressure due to temporal clustering of moderate to extreme precipitation over steep terrain leading to mass wasting phenomena such as rock, soil, and debris flows downward along the slope. With their quick response times and short recovery periods, these cascading hazard events are widespread in tectonically active regions, such as the Himalayas, damaging the natural and built environment systems. Due to climate and land use changes, the number of MDLs, including the mountainous Himalayas, is increasing globally. This study first uses Ripley's L-function to compare the spatial clustering of MDLs between two non-overlapping time windows 2007-2015 versus 2016-2022, assuming spatial point process information follows Poisson distribution across the Uttarakhand state (latitude: 28 degrees 42' N - 31 degrees 28' N; longitude: 77 degrees 35'E - 81 degrees 05' E), one of the most landslide-prone areas in the western Himalayas. Then, we investigate the potential physical controls of landslides by considering ranges of conditioning drivers, such as extreme rainfall indices, catchment and soil attributes. While we find evidence of marked spatial clustering of MDLs up to 80 km radial distance, which is more pronounced during the first half (2007-2015) of the time window compared to the latter half (2016-2022), we show that topographic factors contribute significantly to such events with a median contribution of 55% (range 33-60%), followed by the soil properties, and meteorological indices with median contributions lies in the tune of 20-22%. Among topographic factors, slope, form factor, stream power index, and drainage density significantly trigger MDLs. Whereas, soil factors such as cation exchange capacity and soil organic carbon content were identified as the significant factors to mediate landslides. Among meteorological drivers, the number of days with rainfall over 20 mm shows the highest confidence in triggering landslides, followed by the accumulated rainfall of more than 99th percentiles emerging as key conditioning drivers for MDLs. Understanding the spatial dynamics of landslides and their potential drivers enables stakeholders to develop early warning systems, adaptation, and planning, enhancing climate resilience in landslide-prone areas.

Increases in the frequency and intensity of droughts and heat waves are threatening forests around the world. Climate-driven tree dieback and mortality is associated with devastating ecological and societal consequences, including the loss of carbon sequestration, habitat provisioning, and water filtration services. A spatially finegrained understanding of the site characteristics making forests more susceptible to drought is still lacking. Furthermore, the complexity of drought effects on forests, which can be cumulative and delayed, demands investigation of the most appropriate meteorological indicators. To address this research gap, we investigated the drivers of drought-induced forest damage in a particularly drought-affected region of Central Europe using SHapley Additive exPlanations (SHAP) values, an explainable artificial intelligence (XAI) method which allows for the relevance of predictors to be quantified spatially. To develop a reproducible approach that facilitates transferability to other regions, open-source data was used to characterize the meteorological, vegetation, topographical, and soil drivers of tree vulnerability, representing 41 predictors in total. The forest drought response was characterized as a binary variable (damaged or unchanged) at a 30-m resolution based on the Normalized Difference Moisture Index (NDMI) anomaly (%) between a baseline period (2013-2017) and recent years (2018-2022). We revealed critical tipping points beyond which the forest ecosystem shifted towards a damaged state: <81 % tree cover density, <4% of broadleaf trees, and < 24 m canopy height. Our study provides an enhanced understanding of trees' response to drought, which can support forest managers aiming to make forests more climate-resilient, and serves as a prototype for interpretable early-warning systems.

The rapid expansion of cropland in Cambodia, the world's seventh-largest rice exporter, has created an imbalance in land use structure. However, there is a lack of quantitative investigation of the loss of ecological land as a result of the expansion of cropland and its drivers. In this research, spatial autocorrelation, landscape pattern index and transfer matrix methods were used based on land use data from 2000 to 2023. Then, the eXtreme Gradient Boosting-SHapley Additive exPlanations (XGBoost-SHAP) and Geographic Detector were used to explore the drivers of cropland expansion. The findings indicate that the expanse of agricultural land in Cambodia has significantly increased by 13.47%. The proportion of cropland to the land area (37.87%) is close to that of forest (40.19%). Cultivated land is dominated by rice fields, supplemented by drylands. Spatial clustering is obvious in both drylands and rice fields. Drylands are mainly concentrated in the eastern and western mountainous areas and the northern border, while rice fields are concentrated in the central plains. Cultivated land encroached on a total of 30,579.27km2 of ecological land, of which 62.88% was dry land and 37.12% was rice fields. Forests and shrubs are the main source of expansion of cropland. In addition, soil type (0.18), elevation (0.17) and GDP (0.17), population (0.52) and their interactions strongly drove the expansion of dryland and rice fields. Cambodia should conduct scientific research to assess the demand for cropland by population growth and economic progress. It should realize the orderly growth of cultivated land, reduce the damage to ecological land, and promote the coordinated development of society, environment and economy.

Water temperature extremes can pose serious threats to the aquatic ecosystems of mountain rivers. These rivers are influenced by snow and glaciermelt, which change with climate. As a result, the frequency and severity of water temperature extremes may change. While previous studies have documented changes in non-extreme water temperature, it is yet unclear how extreme water temperatures change in a warming climate and how their hydro-meteorological drivers differ from those of non-extremes. This study aims to assess temporal changes and spatial variability in water temperature extremes and enhance our understanding of the driving processes across European mountain rivers in the current climate, at both a regional and continental scale. First, we describe the characteristics of extreme events and explore their relationships with catchment characteristics. Second, we assess trends in water temperature extremes and compare them with trends in mean water temperature. Third, we use random forest models to identify the main driving processes of water temperature extremes. Last, we conduct a co-occurrence analysis to examine the relationship between water temperature extremes and hydro-climatic extremes. Our results show that mean water temperature has increased by +0.38 +/- 0.14 ${+}0.38\pm 0.14$degrees C per decade, leading to more extreme events at high elevations in spring and summer. While non-extreme water temperatures are mainly driven by air temperature, water temperature extremes are also importantly influenced by soil moisture, baseflow, and meltwater. Our study highlights the complexity of water temperature dynamics in mountain rivers at the regional and continental scale, especially during water temperature extremes.

Investigation of mercury (Hg) from atmospheric precipitation is important for evaluating its ecological impacts and developing mitigation strategies. Western China, which includes the Tibetan Plateau and the Xinjiang Uyghur Autonomous Region, is one of the most remote region in the world and is understudied in regards to Hg precipitation. Here we report seesaw-like patterns in spatial variations of precipitation Hg in Western China, based on Hg speciation measurements at nine stations over this remote region. The Hg fraction analyzed included total Hg (HgT), particulate-bound Hg (HgP) and methylmercury (MeHg). Spatially, HgT concentrations and percentage of HgP in precipitation were markedly greater in the westerlies domain than those in the monsoon domain, but the higher wet HgT flux, MeHg concentration and percentage of MeHg in precipitation mainly occurred in the monsoon domain. Similar spatial patterns of wet Hg deposition were also obtained from GEOSChem modeling. We show that the disparity of anthropogenic and natural drivers between the two domains are mainly responsible for this seesaw-like spatial patterns of precipitation Hg in Western China. Our study may provide a baseline for assessment of environmental Hg pollution in Western China, and subsequently assist in protecting this remote alpine ecosystem.

Soil water content (SWC) and soil temperature (ST) are important indicators of environmental change in permafrost regions. In this study, we conducted soil sampling at 89 locations in the Three Rivers Headwaters Region (TRHR) to investigate the individual and synergistic effects of environmental factors on SWC and ST. We used multivariable regression and random forest modelling to analyse the data. The results show that SWC and ST were higher in the southeast TRHR than in the northwest and higher in surface layers than deeper soil layers. The most important factors affecting SWC in the 0-20 cm and 20-40 cm soil layers were soil bulk density and precipitation, while bulk density was the most important factor in the 40-60 cm layer, and soil bulk density and steppe vegetation were the most important factors in the 60-80 cm layer. For ST, altitude, temperature and slope gradient were the drivers in the 0-20 cm surface layer, while altitude and temperature were the most critical drivers in the 20-40 cm, 40-60 cm and 60-80 cm layers. Overall, bulk density and altitude were the key environmental factors influencing SWC and ST values, respectively. The outcomes of this study provide valuable insights into the environmental factors that impact the SWC and ST in permafrost regions, which can guide decision-making processes for sustainable soil management in the context of climate change.

Rapid climate warming across northern high latitudes is leading to permafrost thaw and ecosystem carbon release while simultaneously impacting other biogeochemical cycles including nitrogen. We used a two-year laboratory incubation study to quantify concomitant changes in carbon and nitrogen pool quantity and quality as drivers of potential CO2 production in thawed permafrost soils from eight soil cores collected across the southern Northwest Territories (NWT), Canada. These data were contextualized via in situ annual thaw depth measurements from 2015 to 2019 at 40 study sites that varied in burn history. We found with increasing time since experimental thaw the dissolved carbon and nitrogen pool quality significantly declined, indicating sustained microbial processing and selective immobilization across both pools. Piecewise structural equation modeling revealed CO2 trends were predominantly predicted by initial soil carbon content with minimal influence of dissolved phase carbon. Using these results, we provide a first-order estimate of potential near-surface permafrost soil losses of up to 80 g C m(-2) over one year in southern NWT, exceeding regional historic mean primary productivity rates in some areas. Taken together, this research provides mechanistic knowledge needed to further constrain the permafrost-carbon feedback and parameterize system models, while building on empirical evidence that permafrost soils arc at high risk of becoming weaker carbon sinks or even significant carbon sources under a changing climate.

The tropical belt has widened during the last several decades, and both internal variability and anthropogenic forcings have contributed. Although greenhouse gases and stratospheric ozone depletion have been implicated as primary anthropogenic drivers of tropical expansion, the possible role of other drivers remains uncertain. Here, we analyze the tropical belt width response to idealized perturbations in multiple models. Our results show that absorbing black carbon (BC) aerosol drives tropical expansion, and scattering sulfate aerosol drives contraction. BC, especially from Asia, is more efficient per unit radiative forcing than greenhouse gases in driving tropical expansion, particularly in the Northern Hemisphere. Tropical belt expansion (contraction) is associated with an increase (decrease) in extratropical static stability induced by absorbing (scattering) aerosol. Although a formal attribution is difficult, scaling the normalized expansion rates to the historical time period suggests that BC is the largest driver of the Northern Hemisphere tropical widening but with relatively large uncertainty. Plain Language Summary The tropical belt has widened over the past several decades, and this is associated with poleward movement of the descending branches of the Hadley Cell and the subtropical dry zones. Internal climate variability and anthropogenic forcers-including greenhouse gases and stratospheric ozone depletion-are important contributors. Leveraging idealized single-forcing experiments, we show that anthropogenic aerosols, including black carbon and sulfate, drive significant tropical expansion and contraction, respectively. Aerosols, particularly those emitted from Asia, are more efficient than greenhouse gases in perturbing tropical belt width. Although relatively large uncertainty exists, linearized scaling suggests that black carbon is the dominant driver of the Northern Hemisphere tropical widening over the historical time period.