Despite a growing interest in identifying tipping points in response to environmental change, our understanding of the ecological mechanisms underlying non-linear ecosystem dynamics is limited. Ecosystems governed by strong species interactions can provide important insight into how non-linear relationships between organisms and their environment propagate through ecosystems, and the potential for environmentally mediated species interactions to drive or protect against sudden ecosystem shifts. Here, we experimentally determine the functional relationships (i.e., the shapes of the relationships between predictor and response variables) of a seagrass assemblage with well-defined species interactions to ocean acidification (enrichment of CO2) in isolation and in combination with nutrient loading. We demonstrate that the effect of ocean acidification on grazer biomass (Phyllaplysia taylori and Idotea resecata) was quadratic, with the peak of grazer biomass at mid-pH levels. Algal grazing was negatively affected by nutrients, potentially due to low grazer affinity for macroalgae (Ulva intestinalis), as recruitment of both macroalgae and diatoms were favored in elevated nutrient conditions. This led to an exponential increase in macroalgal and epiphyte biomass with ocean acidification, regardless of nutrient concentration. When left unchecked algae can cause declines in seagrass productivity and persistence through shading and competition. Despite quadratic and exponential functional relationships to stressors that could cause a non-linear decrease in seagrass biomass, productivity of our model seagrass – the eelgrass (Zostera marina)- remained highly resilient to increasing acidification. These results suggest that important species interactions governing ecosystem dynamics may shift with environmental change, and ecosystem state may be decoupled from ecological responses at lower levels of organization.

In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2022) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2023-05-15.