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Revealing the structural signatures of plastic pollution: The taxonomy-inspired plastic litter indices
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Scientists tested new ways to measure beach plastic pollution that look beyond just counting trash, tracking which companies' products show up, how hazardous items are, and how litter clusters together. This matters because better tracking tools could help identify which brands and products contribute most to ocean plastic, potentially guiding policies that reduce plastic waste before it breaks down into microplastics that enter our food and water.
Coastal plastic pollution is routinely quantified using abundance- and mass-based metrics. These metrics are effective for trend detection, regulatory reporting and international comparison, but by design they describe the magnitude of contamination rather than its internal organisation, its commercial attribution, or the functional behaviour of the items involved. Here we operationalise and internally validate the Taxonomy-Inspired Plastic Litter Indices (TIPLI), a modular set of diagnostic metrics intended to complement conventional monitoring. Twenty-seven indices spanning structural diversity, corporate attribution, functional traits, geo-environmental dynamics, hazard weighting and network organisation were computed for harmonised, item-level coastal litter datasets from six sites in Colombia, Morocco, Brazil, Italy, Panama and Spain (Canary Islands). Richness-based indices were strongly effort-dependent (Spearman rho with sample size 0.89-0.94) and estimated sample coverage was low at product level (0.005-0.698), so raw richness contrasts are not directly comparable among sites. The first ordination axis derived from the full index set was itself correlated with assemblage size (rho = -0.94, p = 0.005). After rarefaction to common effort, product richness converged to 19.2-22.0 products per 22 items, whereas abundance-weighted diversity retained a two-group separation. In contrast, corporate concentration, functional-trait, hazard and geo-environmental indices were insensitive to effort and were estimated without bias by non-parametric bootstrapping (coefficients of variation 1.1-32.4%). The descriptive configurations reported here are therefore presented as exploratory hypotheses rather than validated classes.
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