Nitrogen (N), while the most abundant element in the atmosphere, is an essential soil nutrient that limits plant growth. Leguminous plants naturally possess the ability to fix atmospheric nitrogen through symbiotic relationships with rhizobia in their root nodules. However, the widespread use of synthetic N fertilizers in modern agriculture has led to N enrichment in soils, causing complex and profound effects on legumes. Amid ongoing debates about how leguminous plants respond to N enrichment, the present study compiles 2174 data points from 162 peer-reviewed articles to analyze the impacts and underlying mechanisms of N enrichment on legumes. The findings reveal that N enrichment significantly increases total legume biomass by 30.9% and N content in plant tissues by 13.2% globally. However, N enrichment also leads to notable reductions, including a 5.8% decrease in root-to-shoot ratio, a 21.2% decline in nodule number, a 29.3% reduction in nodule weight, and a 27.1% decrease in the percentage of plant N derived from N₂ fixation (%Ndfa). Legume growth traits and N₂-fixing capability in response to N enrichment are primarily regulated by climatic factors, such as mean annual temperature (MAT) and mean annual precipitation (MAP), as well as the aridity index (AI) and N fertilizer application rates. Correlation analyses show that plant biomass is positively correlated with MAT, and tissue N content also exhibits a positive correlation with MAT. In contrast, nodule numbers and tissue N content are negatively correlated with N fertilizer application rates, whereas %Ndfa shows a positive correlation with AI and MAP. Under low N addition, the increase in total biomass in response to N enrichment is twice as large as that observed under high N addition. Furthermore, regions at lower elevations with abundant hydrothermal resources are especially favorable for total biomass accumulation, indicating that the responses of legumes to N enrichment are habitat-specific. These results provide scientific evidence for the mechanisms underlying legume responses to N enrichment and offer valuable insights and theoretical references for the conservation and management of legumes in the context of global climate change.

The study of litter can provide an important reference for understanding patterns of forest nutrient cycling and sustainable management. Here, we measured litterfall (leaves, branches, etc.) from a wet, evergreen, broad-leaved forest in Ailao Mountains of southwestern China on a monthly basis for 11 years (2005–2015). We measured the total biomass of litter fall as well as its components, and estimated the amount of C, N, P, K, S, Ca, and Mg in the amount of litterfall. We found that: The total litter of evergreen, broadleaved forest in Ailao Mountains from 2005 to 2015 was 7.70–9.46 t/ha, and the output of litterfall differed between years. This provides a safeguard for the soil fertility and biodiversity of the area. The total amount of litterfall and its components showed obvious seasonal variation, with most showing a bimodal pattern (peak from March to May and October to November). The majority of litterfall came from leaves, and the total amount as well as its components were correlated with meteorological factors (wind speed, temperate and precipitation) as well as extreme weather events. We found that among years, the nutrient concentration was sorted as C > Ca > N > K > Mg > S > P. The nutrient concentration in the fallen litter and the amount of nutrients returned showed a decreasing trend, but the decreasing rate was slowed through time. Nutrient cycling was influenced by meteorological factors, such as temperature, precipitation, and wind speed, but the nutrient utilization efficiency is high, the circulation capacity is strong, and the turnover time is short. Our results showed that although there was nutrient loss in this evergreen, broad-leaved forest, the presence of forest litterfall can effectively curb potential ecological problems in the area.

In the context of global climate change and the increasing severity of the urban heat island effect, it is particularly important to study the spatial variation mechanism of urban land surface temperature (LST). The LST data provided by ECOSTRESS offer a new perspective for deepening our understanding of the diurnal cycle and spatial variation of urban LST. In this study, based on a block scale, Tianjin is divided into nine block types, and a multi-scale geographic regression weighting (MGWR) model is used to comprehensively explore the relative contributions of urban 2D and 3D landscape indicators of different block types to the spatial changes in diurnal urban LST cycles. The results indicate that ① the thermal effect during the daytime is mainly influenced by the building density, while at night, it is more influenced by the building height and the heat retention effect; ② the building indicator and the water-body indicator had the most significant effect on surface temperature at different observation times; ③ the influence of urban morphology on land surface temperature shows significant spatial non-stationarity across different block types. This study enhances the understanding of the mechanisms driving urban heat island formation and provides a scientific basis for urban authorities to develop more effective urban planning and heat island mitigation strategies.