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A particle-resolved framework for quantifying microbial colonization and vector risk on environmental microplastics
Summary
Researchers propose a particle-resolved ecological framework — combining high-throughput imaging with single-particle molecular analysis — that shifts microplastic vector-risk assessment from bulk averages to per-particle colonization statistics, introducing a Colonization Prevalence Index to quantify the fraction of particles that actually carry meaningful microbial loads rather than assuming universal contamination.
Microplastics are widely regarded as vectors for microbial pathogens and antibiotic resistance genes, yet this inference is largely derived from laboratory studies and bulk environmental omics approaches that pool many particles and obscure colonization heterogeneity. Such aggregated analyses demonstrate microbial presence but do not quantify how frequently or unevenly colonization occurs across individual particles, leading to systematic overestimation of universal vector risk. Here, we propose a particle-resolved ecological framework that shifts inference from aggregate detection to population-level quantification of colonization prevalence. By integrating high-throughput imaging with single-particle molecular analyses, this framework enables resolution of right-skewed colonization distributions, in which a minority of “supercarrier” particles are expected to disproportionately contribute to microbial biomass and functional gene loads. To operationalize this shift, we introduce the Colonization Prevalence Index (CPI), a quantitative metric that measures the proportion of environmental microplastics that exceed empirically defined colonization thresholds. CPI anchors plastisphere research in statistical prevalence rather than cumulative signal strength, allowing colonization to be interpreted as an ecological probability rather than an assumed universal trait. Together, the particle-resolved framework and CPI provide a practical, scalable pathway for linking particle properties, microbial colonization patterns, and vector potential, enabling probabilistic risk assessment and more targeted mitigation strategies. By emphasizing ecological resolution over bulk averages, this approach reframes microplastic-associated microbial risk as a measurable population property that can be empirically tested across environments. • Defines the “vector illusion” as a key methodological bias in microplastic research. • Proposes a particle-resolved ecological framework replacing bulk inference. • Introduces the Colonization Prevalence Index (CPI) for quantifying vector risk. • Conceptualizes “supercarriers” as a minority driving disproportionate microbial load. • Reframes microplastic risk from assumed ubiquity to measurable ecological probability.