Print PageOrganisms, particularly ectotherms, commonly exhibit different growth rates at different temperatures. These patterns are described as thermal tolerance curves, which depend on physiological and metabolic properties. Differences in thermal tolerance, combined with variation in environmental temperatures, can allow different strategies to coexist. Using an eco-evolutionary modeling approach known as adaptive dynamics, we developed a model to predict the optimal thermal trait values of organisms competing in a specified temperature environment. With this model, we then examined how the value and diversity of optimal traits depended on the influence of the environment (mean, range and skewness of temperature variations) and a hypothesized tradeoff between maximum growth rate and niche width. As a case study, we used data on sea surface temperature variation (from NOAA) to describe realistic temperature regimes. Using this environmental data for our adaptive dynamics model, we were able to predict optimal thermal traits and then compare them to the empirically measured traits of marine phytoplankton from these environments. We are also able to evaluate evidence for the hypothesized tradeoff using this data set.
Results/Conclusions
General modeling results indicate that the thermal tolerance strategies evolve to match the mean of the temperatures that they experience. Adaptation to temperature range is weaker and depends on the specification of low or negative growth rates at unsuitable temperatures. These properties are rarely measured. We also find that stronger tradeoffs between thermal niche width and maximum growth rate result in narrower niches, leading to an increase in the diversity of coexisting thermal strategies. The shape of the tradeoff also influences how closely optimal growth temperatures match mean environmental temperatures. Theoretical predictions of thermal traits based on realistic tradeoff values show remarkably strong agreement with measured trait values across more than one hundred environments and phytoplankton strains. This work represents one of very few applications of adaptive dynamics models to empirical data. Furthermore, we have established and verified a model that connects a general description of temperature environments with predictions of organismal traits and fitness. Based on this, we can now make predictions about the consequences of altered or novel thermal regimes due to climate change on organismal fitness, community composition, and limits to adaptation. This general framework should apply additionally to all marine ectotherms, and potentially other environments.
