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• Analysing spatial effects of trees is critical for improving forest management. • This study explored spatial effects of trees using biomass data in Yunnan, China. • The significant spatial autocorrelations vary with stands, components, and distances. • The stands' spatial heterogeneity increased with distance and stabilised at 10 m. The trees in a given stand compete with each other for light, water and nutrients, producing spatial effects. To understand spatial effects, it is critical to characterise the spatial distributions and patterns of the aboveground biomass (AGB) of individual trees and the biomass of their components (stem, bark, branches and foliage). This study investigated the spatial effects by examining the AGB of individual trees and the biomass of their components in the context of typical sub-tropical tree species in Yunnan, China, including a Pinus kesiya var. langbianensis natural forest (PN), a Pinus kesiya var. langbianensis plantation (PP), and a Eucalyptus spp. plantation (EP), using datasets for three clear-cutting plots. Ripley's L function was employed to identify the spatial distributions and patterns of the AGB of individual trees and the biomass of their components. Then, Global and Local Moran's I indices were utilised to analyse spatial autocorrelation, with Intra-group variance being calculated to quantify spatial heterogeneity. This led to following findings. Firstly, with increasing distance, the spatial heterogeneity of the three stands increased before stabilising at a distance of 10 m. Secondly, excluding bark, all components of the tree biomass in the PN stand exhibited significant dispersion. In contrast, all the components in the PP stand showed significant dispersion. The spatial distributions and patterns of trees and their biomass in the EP stand indicated the coexistence of clustering and dispersion. Moreover, the significant spatial autocorrelations of tree AGB and components biomass could be noticed but depending on the stands, components and distances. These findings provide theoretical support for sustainable forest management based on biomass and carbon stocks. [ABSTRACT FROM AUTHOR]

• Evaluated the spatio-temporal distribution of ecological risk under different scenarios on the QTP from 2000 to 2030. • Using the Sharpe ratio of future uncertainty to assess ecological risk. • Spatial heterogeneity in the ecosystem service values on the Qinghai-Tibet Plateau under future scenarios. • Discussing the different impacts of uncertainty in integrated scenario-based risk assessment of the Qinghai-Tibet Plateau. As one of the world's critical ecological regions – often called the "Third Pole" – the Qinghai-Tibet Plateau (QTP) has outstanding ecosystem service values (ESV). However, its variable climate and fragile environmental conditions make it vulnerable to disturbance by human activities. Based on land use data from 2000, 2010, and 2020, we developed three simulated scenarios of land use distribution on the QTP: business as usual (BAU), farmland protection (FP), and ecological protection (EP). Finally, we employed the Sharpe Index to evaluate ecological risk across the QTP. The results showed that from 2000 to 2020, land use change was mainly in the form of conversion of grassland to unused land, totaling an area of 63,717 km2. The ecosystem service value showed an increasing trend, from US$752.6 billion to US$783.0 from 2010 to 2020. The simulation results from the three scenarios revealed significant differences. Farmland area shows a declining trend in both the EP and BAU scenarios, while exhibiting an increasing trend in the FP scenario. Forest area on the plateau experiences a decreasing trend in the EP and BAU scenarios, but shows an increase in the FP scenario, predominantly in the southeastern region, including Sichuan and Yunnan provinces. The ecological risk assessment results indicate that the eastern region has higher ecological risk compared to the western region, highlighting the western region as a priority area for risk control. The expansion of built-up land and unused land has had significant impacts on regional ecological risk. Integrating risk assessment outcomes with land use planning to identify focal areas can provide guidance for the sustainable development of this globally important ecoregion. [ABSTRACT FROM AUTHOR]

[Display omitted] • Anthropogenic impact has warming effect on the LSWT. • When human activities cause a 1℃ increase in LSWT, the overall average increase in LSWT is 0.24℃. • The impact of human social policies on the surface water temperature of lakes will continue. In the past 40 years, the surface water temperature of lakes worldwide has generally shown an upward trend, with a significant spatial heterogeneity. Previous studies generally attributed the change in lake surface water temperature to direct impact of climate change. However, few studies have explored the potential impact of additional anthropogenic factors, resulting in an incomplete understanding of the anthropogenic influence on lake surface water temperatures. We thus propose a new method to quantify the anthropogenic impact on the surface water temperature of lakes. We selected 11 lakes characterized by significant variations in the intensity of watershed urbanization development on the Yunnan-Guizhou Plateau in China for an empirical study, and discussed the trend of LSWT (lake surface water temperature) changes under the anthropogenic impact, as well as the potential link between these changes and human social policies. The research results show that (1) The mean annual rate of change in LSWT due to anthropogenic impact fluctuates of 0.06℃ per year. (2) LSWT is sensitive to changes in anthropogenic activities; a 1℃ increase in LSWT due to anthropogenic factors typically results in a mean temperature variation of 0.24℃. (3) During the three years of the COVID-19 pandemic, LSWT on the Yunnan-Guizhou Plateau was significantly affected by anthropogenic activities. [ABSTRACT FROM AUTHOR]

• Accurately mapping forest AGB for large areas using images is challenging due to topographic complexity. • Random forest (RF) methods were compared for mapping forest AGB using Sentinel-2 images for Chuxiong. • Quantile RF coupled with the smallest mean error of each quantile led to the most accurate estimates. • Texture measures with windows of 3 × 3 and 5 × 5 pixels were more sensitive than other spectral variables. • Red edge 1 (B5) and Near-infrared bands derived EVIs were more sensitive than other vegetation indices. A simpler, unbiased, and comprehensive random forest (RF) model is needed to improve the accuracy of aboveground biomass (AGB) estimation. In this study, data were obtained from 128 sample plots of Pinus yunnanensis forest located in Chuxiong prefecture, Yunnan province, China. Sentinel-2 imagery data were applied to extract the important predictors of forest AGB, which were screened using the Boruta algorithm. We compared the fitting performance of two modified random forest models − regularized random forest (RRF) and quantile random forest (QRF) − with the random forest model. Moreover, we combined the smallest mean error of each quantile model as the best QRF (QRFb). The result showed: (1) Window sizes of 3 × 3 pixels and 5 × 5 pixels demonstrated greater sensitivity and suitability for estimating AGB than the 7 × 7 pixels window size. Enhanced vegetation indices derived from Red Edge 1 (B5) and Near-Infrared bands (B8A) were strongly correlated with AGB, indicating the heightened sensitivity of B5 and B8A bands to biomass and their potential in AGB estimation. (2) The RRF model outperformed both the standard RF and QRF in fitting performance, with an R2 of 0.56 and RMSE 57.14 Mg/ha. (3) The QRFb model exhibited the highest R2 of 0.88 and lowest RMSE of 29.56 Mg/ha, significantly reducing overestimation and underestimation issues. The modified RF regression supplies new insights into improving forest AGB estimation, which will be helpful for future research addressing carbon cycling. [ABSTRACT FROM AUTHOR]

• Incorporate the ecological security pattern into the constraints of future land use simulation. • Integrate future building land expansion with the ecological security pattern to identify ecological early warning areas. • Considering land use types, propose ecological restoration strategies for ecological restoration and early warning areas. The scientific determination of crucial regions for ecological land restoration and reasonable early warnings for ecologically weak areas are vital for implementing land ecological restoration. Five major plateau lake basins in central Yunnan (China) are selected as the study area in this research. An ecological security pattern (ESP) is constructed using the source-resistance surface-corridor-pinch point framework to effectively identify crucial areas for ecological restoration. Finally, the land use/cover change (LUCC) in the study region in 2030 is simulated in terms of the natural development prospect (NDP), with priority given to urban development (PUD) and priority given to ecological protection (PEP) scenarios, and identify areas where future urban expansion poses a threat to current ecological source areas and corridors as early warning regions. The results indicate the following: (1) There are 15 ecological source regions in the Five Lakes Basin, mainly forestland and waters, with a total area of 787.54 km2. The ecological corridor spans 245.72 km, with a total area of 439.90 km2. (2) The key ecological restoration areas include 72.87 km2 of ecological pinch points, 70.46 km2 of barrier points of ecological, and 71 breakpoints. (3) The ecological land under the NDP scenario and the PUD scenario were damaged to varying degrees. The ecological land dominated by forestland and water area under the PEP scenario is effectively protected. (4) The seven ecological protection space early warning points are mainly distributed in the eastern part of Dianchi Lake and the northern part of Fuxian Lake, which have good economic development and better habitat quality. The comprehensive analysis identified key ecological restoration areas and identified future ecological stresses due to land use change in the Five-Lakes Basin. Restoration strategies are proposed based on current land use and future scenarios to enhance the ESP in central Yunnan's Five Lakes Basin. [ABSTRACT FROM AUTHOR]

[Display omitted] • Macadamia-based agroforestry systems influence microbial necromass C and its contribution to SOC. • Soil fungal β diversity controlled and significant negative effect over microbial necromass C and its contribution to SOC. • Woody aboveground biomass indirectly affected microbial necromass C via soil fungal β diversity. Agroforestry management practices governed soil organic carbon (SOC) and microbial community structure in terrestrial ecosystems, yet the effect of these practices on microbial necromass carbon (C) remain poorly understood in a subtropical region. Here, we compared the differences in fungal, bacterial, and microbial necromass C (indicated by amino sugars) and their contributions to SOC among different forest types including macadamia monoculture plantation (MMP) and macadamia-based agroforestry systems intercropping with dasheen (MDAS), konjac (MKAS), and maize (MMAS) in the Lincang city, southwest Yunnan province, China, and assessed the impact of aboveground biomass, soil fertility, and soil microbial community structure on microbial necromass C and its contribution to SOC. Compared with monoculture plantation, macadamia-based agroforestry decreased SOC, and changed significantly soil microbial α diversity, β diversity, and microbial necromass C and its contribution to SOC (P < 0.01). Microbial necromass C and its contribution to SOC were no significant difference between MMP and MMAS. MDAS and MKAS had lower microbial necromass C than MMP (35.11 % and 63.62 %, respectively). SOC increased with increasing soil fungal and microbial necromass C among the agroforestry systems. Soil fertility was the most important factor affecting fungal (41.45 %), bacterial (33.84 %), and microbial necromass C (40.63 %). Importantly, soil fertility and fungal β diversity directly negatively affected soil fungal and microbial necromass C and their contributions to SOC. Furthermore, woody aboveground biomass affected microbial necromass C directly or indirectly via soil fungal β diversity. Overall, our findings suggest that soil fungal β diversity drove microbial necromass C accumulation and its contribution to SOC in a macadamia-based agroforestry systems, which reveal that sowing crop may be key considerations in mitigating climate change. [ABSTRACT FROM AUTHOR]