Solar-induced chlorophyll fluorescence (SIF) is increasingly known as an effective proxy for plant photosynthesis, and therefore, has great potential in monitoring gross primary production (GPP). However, the relationship between SIF and GPP remains highly uncertain across space and time. Here, we analyzed the SIF (reconstructed, SIFc)–GPP relationships and their spatiotemporal variability, using GPP estimates from FLUXNET2015 and two spatiotemporally contiguous SIFc datasets (CSIF and GOSIF). The results showed that SIFc had significant positive correlations with GPP at the spatiotemporal scales investigated (p < 0.001). The generally linear SIFc–GPP relationships were substantially affected by spatial and temporal scales and SIFc datasets. The GPP/SIFc slope of the evergreen needleleaf forest (ENF) biome was significantly higher than the slopes of several other biomes (p < 0.05), while the other 11 biomes showed no significant differences in the GPP/SIFc slope between each other (p > 0.05). Therefore, we propose a two-slope scheme to differentiate ENF from non-ENF biome and synopsize spatiotemporal variability of the GPP/SIFc slope. The relative biases were 7.14% and 11.06% in the estimated cumulative GPP across all EC towers, respectively, for GOSIF and CSIF using a two-slope scheme. The significantly higher GPP/SIFc slopes of the ENF biome in the two-slope scheme are intriguing and deserve further study. In addition, there was still considerable dispersion in the comparisons of CSIF/GOSIF and GPP at both site and biome levels, calling for discriminatory analysis backed by higher spatial resolution to systematically address issues related to landscape heterogeneity and mismatch between SIFc pixel and the footprints of flux towers and their impacts on the SIF–GPP relationship.

Understanding species response to climate change is essential for the conservation and utilization of species resources under rapid climate change in the future. In this study, the present and future suitable distribution range of Keteleeria davidiana, a tertiary relict gymnosperm, was predicted based on the maximum entropy model (MaxEnt). A total of 158 occurrence records were collected after removing the duplicated records. Six low-correlation climate variables were used to predict species distributions. The three key climate factors that affect the distribution of K. davidiana were temperature seasonality (34.96%), mean temperature of the coldest quarter (28.30%) and precipitation seasonality (13.58%). The most suitable zone of the temperature seasonality for K. davidiana was between 377.4 and 843.4. The highly suitable area was located in the mountainous regions of central and southeast China, which accounted for 13.39% of the whole study area. With climate warming in the future, the highly suitable distribution area of K. davidiana was estimated to decrease by 35% (SSP1-2.6 scenario) or 85% (SSP5-8.5 scenario). This study has provided a sufficient scientific basis for the future in situ and ex situ conservation of K. davidiana.

Soil microorganisms are greatly affected by their microenvironment. To reveal the influence of different land use patterns on the composition and diversity of soil bacterial and fungal communities, this study analyzed microbial (bacteria and fungi) community composition and diversity under different land use patterns (vegetable land, wasteland, woodland, cultivated land) based on 16S rRNA, 18S rRNA, and high-throughput sequencing method in the Taojia River Basin. Spearman analysis and redundancy analysis (RDA) were used to explore the correlation between soil physicochemical properties and soil fungal and bacterial community composition, and a partial least squares path model (PLS-PM) was constructed to express the causal relationship between soil physicochemical properties and soil bacterial and fungal community diversity. The results showed that the soil bacterial species richness was highest in vegetable land and the lowest in the wasteland. Proteobacteria is the dominant phylum (20.69%–32.70%), and Actinobacteria is the dominant class (7.99%–16.95%). The species richness of fungi in woodland was the highest, while was the lowest in cultivated land. The dominant phylum of fungi in vegetable land, woodland, and cultivated land is Mucoromycota, 29.39%, 41.36%, and 22.67%, respectively. Ascomycota (42.16%) is the dominant phylum in wasteland. Sordariomyetes of Ascomycota is the dominant class in wasteland and cultivated land. Mortierellomycetes and Glomeromycetes of Mucoromycota are the dominant class in vegetable land and woodland. The results of the Spearman analysis revealed that the dominant groups in the bacterial and fungal communities had significant correlations with soil pH, clay, and sand (p < 0.01). The RDA results showed that soil clay, pH, and moisture were the key environmental factors affecting the diversity of soil microbial communities. Fungal diversity is more affected by different land use patterns than bacteria. These results provided a theoretical basis for the changes in soil microbial community composition and diversity in river basins.

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.

Vegetation is the most vulnerable component of terrestrial ecosystems to climate change. In recent decades, there has been a significant warming and humidification trend in the Tibetan Plateau. It is crucial to study and analyze the impact of these changes on the ecosystem and their future trends for protecting the Tibetan Plateau’s ecosystem. This study collected and analyzed climate (temperature, precipitation) data and vegetation index data (the normalized difference vegetation index (NDVI) and the leaf area index (LAI)), as well as data from significance tests combined with Mann–Kendall tests and Sen’s slope estimation. The effects of temperature and precipitation factors on vegetation indices were revealed, leading to a multiple regression model predicting NDVI and LAI value changes under climate change from 2021 to 2100. The results indicate a general increase in temperature and precipitation levels across the Tibetan Plateau between 2000 and 2020. The climate experienced a clear pattern of warming and moist conditions, with the southeast region experiencing warmer and wetter conditions, and the northwest region experiencing drier and colder conditions. The trends of the LAI and NDVI values of the Tibetan Plateau indicated a general increase, with a gradual decline from the southeast to the northwest. Precipitation and temperature were differentially correlated with the NDVI and LAI values across various regions of the plateau. Between 2021 and 2100, the Tibetan Plateau is expected to experience year-on-year increases in both precipitation and temperature levels. However, the increase in precipitation was found to be less significant than that of the climate and, comparatively, smoother. There is a certain correlation between the NDVI and LAI values, and the changes in temperature and precipitation. The variations of both are more influenced by temperature than precipitation, with an overall increasing trend observed over the years, which is also quite evident. This study could serve as a scientific foundation and a point of reference for monitoring vegetation changes over a long period of time on the plateau, as well as for the planning and execution of ecological development in the Tibetan Plateau.

Aerosols affect the gross primary productivity (GPP) of plants by absorbing and scattering solar radiation. However, it is still an open question whether and to what extent the effects of aerosol on the diffuse fraction (D_f) can enhance GPP globally. We quantified the aerosol diffuse fertilization effect (DFE) and incorporated it into a light use efficiency (LUE) model, EC-LUE. The new model is driven by aerosol optical depth (AOD) data and is referred to as AOD-LUE. The eddy correlation variance (EC) of the FLUXNET2015 dataset was used to calibrate and validate the model. The results showed that the newly developed AOD-LUE model improved the performance in simulating GPP across all ecosystem types (R² from 0.6 to 0.68), with the highest performance for mixed forest (average R² from 0.71 to 0.77) and evergreen broadleaf forest (average R² from 0.34 to 0.45). The maximum LUE of diffuse photosynthetic active radiation (PAR) (3.61 g C m⁻² MJ⁻¹) was larger than that of direct PAR (1.68 g C m⁻² MJ⁻¹) through parameter optimization, indicating that the aerosol DFE seriously affects the estimation of GPP, and the separation of diffuse PAR and direct PAR in the GPP model is necessary. In addition, we used AOD-LUE to quantify the impact of aerosol on GPP. Specifically, aerosols impaired GPP in closed shrub (CSH) by 6.45% but enhanced the GPP of grassland (GRA) and deciduous broadleaf forest (DBF) by 3.19% and 2.63%, respectively. Our study stresses the importance of understanding aerosol-radiation interactions and incorporating aerosol effects into regional and global GPP models.