Evaluating the time to detect biological effects of ocean acidification and warming: an example using simulations of purple sea urchin settlement

Ocean acidification (OA) and ocean warming driven by climate change are important stressors for marine species and systems, but documenting and detecting their long-term impacts on biological responses outside of laboratory settings are challenging due to natural variability caused by complex processes and interactions. We used settlement of purple sea urchins Strongylocentrotus purpuratus in the Southern California Bight (USA) over 6 yr as an example data set to parameterize a simulation model for exploring the time needed to detect environmental effects on a biological response. A generalized linear model was used to describe an index of urchin settlement as functions of pH, sea surface temperature (SST), sea surface salinity (SSS), and spatio-temporal factors, demonstrating that settlement was negatively associated with pH (i.e. lower settlement at higher pH) and positively associated with SST and SSS. Monte Carlo simulations were developed from this base model under a variety of alternative scenarios to estimate the time to detect: (1) annual trends in pH and SST time series, (2) pH and SST effects on urchin settlement, and (3) annual trends in urchin settlement. Time to detect pH and SST effects was predominantly influenced by the underlying strength of the relationships and the model uncertainty. Time to detect annual trends in settlement was more sensitive to the severity of long-term OA and warming trends, which had cumulative (at times opposing) effects. This study highlights the variable time scales (2-60+ yr) that may be necessary to detect biological responses to OA and ocean warming and the sensitivity to different assumptions of the study system.