The Effect of SST Variability on Community Growth Rates and Composition

Understanding how spatial patterns of phytoplankton species distribution may shift as a response to rising temperatures has implications for global and local ecosystem dynamics and carbon cycling. Phytoplankton biogeography is often linked to local sea surface temperatures (SSTs). However, recent modeling studies have demonstrated that currents can rapidly transport microbes across biogeographical regions with changes in SST of up to 10°C larger than local seasonal variability. As SST changes, there is a memory effect in which the phenotype most suited for the new environment must overcome the previously accumulated biomass before the optimum growth temperature of the community matches that of the SST. Here, we utilize a simple, individual-based, ecosystem model to determine the timescales over which this memory effect impacts overall community growth rates. We used idealized SST profiles to determine that the length of the memory effect depends on rate and direction of change, the mean temperature, as well as the shape of the individual’s reaction norm. Faster rates of change and decreasing temperatures are associated with longer memory effects. Skewed reaction norms and higher mean temperatures had shorter memory effects than broad reaction norms and colder temperatures. We then analyze the in situ memory effect using SSTs from surface floats in the Southern Ocean by comparing the individual model and the traditional Q₁₀ method of simulating phytoplankton growth, which is ubiquitous among the global biogeochemical models used in future scenarios. We found that the Q₁₀ method largely over-estimates community growth rate by 10-60% and is less reliable as SST variability increases.