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Nutrient enrichment is a major global change component that often disrupts the relationship between aboveground biodiversity and ecosystem functions by promoting species dominance, altering trophic interactions, and reducing ecosystem stability. Emerging evidence indicates that nutrient enrichment also reduces soil biodiversity and weakens the relationship between belowground biodiversity and ecosystem functions, but the underlying mechanisms remain largely unclear. Here, we explore the effects of nutrient enrichment on soil properties, soil biodiversity, and multiple ecosystem functions through a 13-year field experiment. We show that soil acidification induced by nutrient enrichment, rather than changes in mineral nutrient and carbon (C) availability, is the primary factor negatively affecting the relationship between soil diversity and ecosystem multifunctionality. Nitrogen and phosphorus additions significantly reduce soil pH, diversity of bacteria, fungi and nematodes, as well as an array of ecosystem functions related to C and nutrient cycling. Effects of nutrient enrichment on microbial diversity also have negative consequences at higher trophic levels on the diversity of microbivorous nematodes. These results indicate that nutrient-induced acidification can cascade up its impacts along the soil food webs and influence ecosystem functioning, providing novel insight into the mechanisms through which nutrient enrichment influences soil community and ecosystem properties.

Relative Sunspot Number is one of the major parameters for the study of long-term solar activity. The automatic calculation of the Relative Sunspot Number is more stable and accurate as compared to manual methods. In this paper, we propose an algorithm that can detect sunspots and divide them into groups, to automatically calculate the Relative Sunspot Number. Mathematical Morphology was adopted to detect sunspots then group them. The dataset used were the continuum images from SDO/HMI. The process was carried out on the overall HMI data available on the timespan from January 2022 to May 2023 with a time cadence of one day. The experimental results indicated that the method achieved high accuracy of 85.3\%. It was well fitted with the international Relative Sunspot Number provided by Solar Influences Data Analysis Center (SIDC) (CC=0.91). We calculated the conversion factor K value of SDO/HMI for calculating the Relative Sunspots Number(K=1.03)., Comment: 11 pages,5 figures