Xin Jiao

Arbuscular mycorrhizal fungi (AMF) are crucial symbiotic microorganisms in terrestrial ecosystems, playing a vital role in maintaining orchard soil health and productivity. However, how organic–and Ca, Mg, Zn fertilizers co-application affect vertical stratification and ecological functions of arbuscular mycorrhizal fungi (AMF) in perennial fruit orchards remains unclear. Based on a five-year in situ peach trial, we established three fertilization regimes: low- (LWF), medium- (MWF), and high-input (HWF) regimes. We systematically analyzed the AMF community structure, diversity, and their correlations with soil physicochemical properties, as well as peach tree physiology, fruit yield, and quality across two soil depths: 0–20 cm (topsoil) and 20–40 cm (subsoil). HWF significantly inhibited AMF root colonization rates and spore density (p < 0.05), while reducing community α-diversity AMF α-diversity (p < 0.05), characterized by the enrichment of genera such as Glomus and a decrease in the relative abundance of Rhizoglomus. Redundancy analysis (RDA) identified available Zn (AZn) and Mg (WMg) as key drivers of this restructuring. Integrating RDA results into depth-specific partial least squares structural equation models (PLS-SEM), we found that subsoil AZn/WMg indirectly boosted yield by reshaping AMF composition (β = 0.34, p = 0.006), mediated via improved canopy status (NDVI, PRI). Total effect analysis confirmed the dominant role of subsoil pathways. These findings challenge the prevailing topsoil-centric view of soil microbial ecology and underscore the importance of considering the full soil profile when evaluating the impacts of agricultural practices on beneficial symbionts. We conclude that sustainable management strategies should account for depth-dependent AMF responses to maintain both productivity and belowground biodiversity across the entire rooting zone.

Understanding urban state transitions is critical for sustainability, yet a unified framework to characterize the interactive dynamics of key urban components—green vegetation (GV), impervious surface (ISA), and bare soil (BS)—has been lacking at a national scale. To bridge this gap, we developed a novel framework that moves beyond static mapping to directly quantify the dynamic interactions within the V-I-S nexus. Leveraging over 200,000 Landsat images on Google Earth Engine, we generated the first continuous, annual V-I-S fraction maps for China (1990-2019) using a unified spectral endmember space with change trajectory model to characterize and quantify multi directional urban state transitions. Through our pixel-level trajectory analysis, where we integrated the magnitude, timing, and trend of V-I-S changes into a set of conversion rules to systematically identify five transition archetypes: stable, expansion, intensification, green recovery, and soil exposure. This framework demonstrated superior capability in tracking the complex intra-urban landscape components with high accuracy (i.e., smaller mean squared error of -2.07% to 3.44%) and low uncertainties (i.e., fractional biases less than 0.10), as well as achieving highly accurate historical state transitions mapping (87.29%) at fine-scale feature resolution. Our analysis reveals a national-scale GV loss (-31,109 km²) alongside ISA (+28,294 km²) and BS (+2,814 km²) gains, primarily driven by expansion (46%) and intensification (28%). Crucially, we uncover a pivotal shift from expansive growth to intensification and greening as cities develop. This study provides the first spatially explicit, nationally consistent record of intra-urban dynamics, offering a powerful tool for modeling urban social-ecological systems and guiding targeted governance for sustainable urban futures.

Ecosystem water use efficiency (WUE), an indicator of the trade-off between carbon uptake and water loss, is widely used to assess ecosystem responses to climate change. However, large-scale studies of WUE typically assume a single, fixed lag or accumulation period of climatic drivers across regions. This static assumption neglects spatially heterogeneous temporal responses of WUE to climate, potentially biasing attribution analyses and reducing predictive skill. Here, we developed a pixel-level model to quantify the temporal effects of climatic drivers on WUE by explicitly accounting for no-effect, lagged, cumulative, and combined effects and allowing effect timescales to vary spatially. We found that more than 80% of pixels across China exhibited lagged and/or cumulative effects for each driver, with distinct temporal effect patterns among vegetation types and drivers. In herbaceous cover croplands, precipitation exhibited the shortest lag (0.31 ± 0.56 months) and the longest accumulation time (1.71 ± 0.96 months). Accounting for these spatially heterogeneous temporal effects increased the explanatory power of climatic drivers for WUE variation by 17.7% compared with models without temporal effects. We further showed that for most vegetation types, precipitation and air temperature were more strongly associated with temporal variation in WUE, whereas solar radiation contributed more to spatial variability. These findings indicate that location-specific temporal effects can modulate the climatic controls on WUE. Our framework is readily applicable beyond China and can support a shift toward dynamic climate responses in climate–ecosystem interaction modeling, thereby improving forecasts of ecosystem dynamics and informing climate-adaptive vegetation management.

Accurate inventories of grasslands are important for studies of greenhouse gas (GHG) dynamics, as grasslands store about one-third of the global terrestrial carbon stocks. This paper develops a framework for large-area grassland mapping based on the probability of grassland occurrence and the interactive pathways of fractional vegetation and soil-related endmember nexuses. In this study, grassland occurrence probability maps were produced based on data on bio-climate factors obtained from MODIS/Terra Land Surface Temperature (MOD11A2), MODIS/Terra Vegetation Indices (MOD13A3), and Tropical Rainfall Measuring Mission (TRMM 3B43) using the random forests (RF) method. Time series of 8-day fractional vegetation-related endmembers (green vegetation, non-photosynthetic vegetation, sand land, saline land, and dark surfaces) were generated using linear spectral mixture analysis (LSMA) based on MODIS/Terra Surface Reflectance data (MOD09A1). Time-series endmember fraction maps and grassland occurrence probabilities were employed to map grassland distribution using an RF model. This approach improved the accuracy by 5% compared to using endmember fractions alone. Additionally, based on the grassland occurrence probability maps, we identified extensive ecologically sensitive regions, encompassing 1.54 (10⁴ km²) of desert-to-steppe (D-S) and 2.34 (10⁴ km²) of steppe-to-meadow (S-M) transition regions. Among these, the D-S area is located near the threshold of 310 mm/yr in precipitation, an annual temperature of 10.16 °C, and a surface comprehensive drought index (TVPDI) of 0.59. The S-M area is situated close to the line of 437 mm/yr in precipitation, an annual temperature of 5.49 °C, and a TVPDI of 0.83.

Soil organic matter (SOM) plays pivotal roles in characterizing dryland structure and function; however, remotely sensed spatially-detailed SOM mapping in these regions remains a challenge. Various digital soil mapping approaches based on either single-period remote sensing or spectral indices in other ecosystems usually produce inaccurate, poorly constrained estimates of dryland SOM. Here, a framework for spatially-detailed SOM mapping was proposed based on cross-wavelet transform (XWT) that exploits ecologically meaningful features from intra-annual fractional vegetation and soil-related endmember records. In this framework, paired green vegetation (GV) and soil-related endmembers (i.e., dark surface (DA), saline land (SA), sand land (SL)) sequences were adopted to extract 30 XWT features in temporally and spatially continuous domains of cross-wavelet spectrum. We then selected representative features as exploratory covariates for SOM mapping, integrated with four state-of-the-art machine learning approaches, i.e., ridge regression (RR), least squares-support vector machines (LS-SVM), random forests (RF), and gradient boosted regression trees (GBRT). The results reported that SOM maps from 13 coupled filtered XWT features and four machine learning approaches were consistent with soil-landscape knowledge, as evidenced by a spatially-detailed gradient from oasis to barren. This framework also presented more accurate and reliable results than arithmetically averaged features of intra-annual endmembers and existing datasets. Among the four approaches, both RF and GBRT were more appropriate in the XWT-based framework, showing superior accuracy, robustness, and lower uncertainty. The XWT synthetically characterized soil fertility from the consecutive structure of intra-annual vegetation and soil-related endmember sequences. Therefore, the proposed framework improved the understanding of SOM and land degradation neutrality, potentially leading to more sustainable management of dryland systems.