A statistical approach to model soil microbiota versus heavy metals: a case study on soil samples from Foggia, Southern Italy

Heavy-metal (HM) contamination undermines soil functions and food safety, while risk appraisals often rely on chemical indices that can be unstable in the presence of extremes and only indirectly reflect biological integrity. We present an integrative framework that couples standardized contamination metrics with soil microbiome profiling to deliver stable, interpretable classifications and actionable bioindicators. Twelve peri-urban soils from Southern Italy were analysed for potentially toxic elements, including Arsenic (As), Cadmium (Cd), Chromium (Cr), Copper (Cu), Nickel (Ni), Lead (Pb), and Zinc (Zn) and profiled by shotgun metagenomics. We introduce a Standardized Ecological Risk index (SPERI) that preserves the ranking conveyed by conventional composites yet reduces outlier leverage. SPERI strongly agreed with Improved Potential Ecological Risk Index (IPERI) while stabilizing variance (R² = 0.896) and improved between-site comparability. Along the contamination gradient, community structure shifted consistently: families such as Pseudomonadaceae, Xanthomonadaceae and Rhodospirillaceae increased with risk, whereas Geodermatophilaceae and Nocardiaceae declined. Simple decision-tree models trained on family-level relative abundances reliably separated SPERI classes and repeatedly selected Zn- and Cd-enriched sites as primary split drivers, aligning microbial signals with chemical risk. By combining open, reproducible analytics with jointly chemical- and microbiome-informed endpoints, this workflow improves the interpretability and transferability of ecological risk assessment and supports targeted remediation and monitoring in contaminated agro-ecosystems.