Quantifying the effect of biomass magnitude on apparent methylmercury toxicity in marine phytoplankton

Methylmercury (MeHg) is a potent neurotoxin whose ecological impacts begin at the base of marine food webs, yet laboratory toxicity assays often rely on phytoplankton biomass levels far exceeding those found in natural waters. This discrepancy obscures true per-cell sensitivity and complicates cross-species comparisons. Here, we quantify the influence of initial biomass on apparent MeHg toxicity across five ecologically and taxonomically diverse marine phytoplankton-Thalassiosira pseudonana, Cricosphaera carterae, Emiliania huxleyi, Synechococcus elongatus, and Phaeodactylum tricornutum. Dose-response experiments were conducted across a gradient of initial optical densities (OD₇₅₀), and EC₅₀ values were estimated using nonlinear regression. Log₁₀-transformed EC₅₀ values exhibited strong linear relationships with OD₇₅₀ for most species, demonstrating a consistent biomass buffering effect in which higher cell densities reduce apparent MeHg toxicity. Extrapolation of regression models to OD = 0 yielded intrinsic, biomass-normalized EC₅₀ values that converged more tightly across taxa than raw EC₅₀ measurements, revealing a shared physiological sensitivity range once biomass artifacts were removed. Cricosphaera carterae was the most sensitive species (EC₅₀ = 0.0015 μg/L), while Synechococcus elongatus was the most tolerant (EC₅₀ = 43.86 μg/L). These biomass-adjusted EC₅₀ values were used to construct a species sensitivity distribution (SSD), from which an HC₅ of 2.25 x 10^-3 μg/L was estimated. Following the EPA acute-criteria relationship (FAV = 2 × HC₅), a Final Acute Value (FAV) of 4.50 x 10^-3 μg/L was derived. These findings demonstrate that biomass strongly modulates apparent MeHg toxicity and that biomass-adjusted EC₅₀ values serve as useful upper-bound estimates of per-cell sensitivity. This framework provides a reproducible method for normalizing toxicity data across taxa and improves the interpretation of contaminant sensitivity under low-biomass conditions without directly predicting ecological risk in situ.