Temperature is a critical determinant of organismal behavior, performance, and survival, making it one of the most widely used predictors of how biological systems will respond to environmental change. While the potential impacts of warming have received considerable attention in the past, only recently have the importance of perhaps more biologically meaningful variables such as thermal extremes and variability been widely recognized. Indeed, these changes in temperature will undoubtedly influence performance of all life on earth, thus predictions of how species will respond to these changes requires understanding how the not only the mean, but also variance, and seasonal fluctuations in temperature will affect individual performance.
To explore the independent and interactive effects of mean body temperature, temperature variability, and seasonal fluctuations in temperature on fitness, we used a model based on Dynamic Energy Budget (DEB) theory. First, we independently altered the statistical moments of the distribution of body temperatures and added the resulting datasets to seasonal signals with increasingly larger amplitudes. Using the resulting time series as inputs to the DEB model, we then simulated growth, condition, reproductive potential, and survival of the mussel Mytilus californianus. Additionally, we ran the simulations under three food conditions to explore the interactions between the distribution of temperature and food availability.
Results/Conclusions
Using a DEB approach allowed us to incorporate the effects of temperature on metabolic rates as well as the mechanisms by which organisms acquire and assimilate energy from food, effectively combining the impacts of temperature and food on the overall energy budget of individual organisms. First, we show that predictions of fitness based on mean temperature alone differ substantially from those incorporating temperature variance, due to the non-linear relationship between temperature and performance. Furthermore, we found appreciable differences in predicted fitness between simulations incorporating seasonal fluctuations in temperature versus those with mean and variance alone. Specifically, maximum fitness declined and the thermal tolerance range contracted with increasing daily variance. Increased seasonal fluctuations magnified this effect. Our results suggest that a compensatory mechanism may exist through which organisms offset this fitness cost associated with thermal stress.