Microplastics are an increasing contaminant in coastal marine environments, yet their effects on primary producers and the consequences for food-web stability remain poorly quantified. This study combines laboratory bioassays with computational modelling to investigate how microplastic exposure affects the green macroalga Ulva lactuca and how these disruptions propagate to higher trophic levels, offering a replicable framework for ecosystem-level risk assessment.
Algae were cultivated at eight microplastic concentrations (0–580 ppm) for 28 days, with cellular and structural traits quantified via digital image analysis. Cell density declined significantly and monotonically with increasing concentration (F(7, 23) = 74.2, p < 0.001, η² = 0.96 across 24 digitized image-analysis replicates), falling by 46%, while net dry biomass stabilized above 120 ppm, revealing a stress-induced cellular-to-mass decoupling.
These empirical relationships directly parameterized a modified Rosenzweig-MacArthur food-chain model, where the laboratory-derived cell density decay determined the algal growth rate r(M) and the cell-to-biomass ratio defined trophic conversion efficiency E1(M). The model identifies a critical bifurcation threshold at 35.6 ppm, beyond which apex predators undergo a catastrophic bottom-up collapse due to energetic starvation.
To map the systemic vulnerabilities of this framework, a comprehensive sensitivity analysis was conducted via a Tornado chart. This analysis isolated the key biological parameters driving the system’s volatility, demonstrating how minor shifts in lower-trophic efficiencies can accelerate or buffer ecosystem-wide collapse.
These findings demonstrate that conventional biomass monitoring severely underestimates microplastic stress, and that our integrated framework provides a scalable, early-warning diagnostic tool for global coastal water management.
