The key role of soil quality improvement in achieving sustainable agricultural development based on highly intensive use of farmland is increasingly being recognized, as is the ponderance of suitable evaluation of the soil quality. The overarching goal of this study was to determine an accurate assessment framework by the comparison of the scoring function (linear and non-linear) and integration method (area and weighted additive), which integrally evaluates the soil quality of an eleven-year field fertilization experiment (including CK, no fertilizer; CF, conventional fertilization; SF, formulated fertilization; SFO, SF with organic fertilizer). Thirty-three properties, including eighteen physiochemical-related and fifteen biological-related properties, associated with soil functions were measured as potential soil quality indicators, and the soil multifunctionality (SMF) was applied to validate the soil quality indices (SQIs). Principal component analysis and relationship analysis were used with indicators sensitive to management to determine a minimum data set (MDS). The results showed that the electrical conductivity, large macroaggregate-associated total nitrogen, small macroaggregate-associated organic carbon, carbon fixation, and enzyme activities of phenol oxidase and cellulase were chosen as the MDS. All the SQIs were significantly correlated with the SMF (p < 0.05). The fertilization strategies affected most indicators in different ways, and the index developed using the non-linear function and weighted additive integration method (SQI-NL) had the best sensibility and discriminability. The SQI value with the SQI-NL-MDS method was higher following the fertilization treatments than that of no fertilizer (p < 0.05), and the treatment of the organic fertilizer had the highest SQI value (0.66). Soil quality evaluation in long-term fertilized farmland suggested that the soil quality constraints between treatments of synthetic and organic fertilizer are related to the soil functions of nutrient cycling and sustain biological activity due to their higher contribution rates to the SQI in the organic fertilizer treatment, which provides insights into ways to reduce the gap in soil quality. The framework method can provide an accurate quantitative tool for the evaluation of soil quality from the target indicators by bridging management objectives and field-level actions.

Soil metal contamination poses potential risks to ecosystem health, yet its vertical distribution and impacts on microbial communities in protected wildlife habitats remain poorly understood. This study investigated the vertical variation (0–80 cm) in seven metals or metalloids (Zn, Cr, Cu, Co, Ni, As, and Pb) and their correlations with soil bacterial communities in the Chinese Changxing Alligator Nature Reserve, Zhejiang Province, China. Soil samples were collected from two functional zones—the Reserved Breeding Zone (FY) and Reintroduction Zone (FG)—at four depth intervals. The results revealed distinct vertical stratification for both soil elements (metals and metalloids) and bacterial communities. Soil element concentrations exhibited depth-dependent variations, with surface layers showing higher accumulation of Zn, Ni, Cu, Cr and Pb, while As was enriched in deeper horizons. Bacterial alpha diversity declined with depth, and community composition shifted markedly: Pseudomonadota and Actinomycetota dominated surface soils, whereas Acidobacteriota and Chloroflexota increased in deeper layers. Network analysis revealed that microbial co-occurrence network complexity decreased with soil depth, indicating greater stability and environmental adaptability of surface soil microbial communities. Pearson correlation and Mantel analyses revealed significant depth-related variation in soil element concentrations (Zn, Co, Ni, Cu, Pb, Cr, and As), and these patterns were closely associated with shifts in soil bacterial community structure. This study provides systematic evidence of vertical differentiation in soil element–microbe interactions within reserve soil profiles, offering a scientific basis for habitat quality assessment, ecological risk warning, and Chinese alligator habitat conservation and management.