Advancing mechanistic frameworks in aquatic sciences through the integration of theory and experiments

One of the biggest challenges facing oceanographers and marine biologists today is to be able to predict the responses of aquatic ecosystems to anthropogenic global change. Developing predictive mechanistic frameworks is a necessary step. Often there is a disconnect between observations and theory and models, as there is not always an effective communication between empiricists and theorists. A focused effort to overcome the traditional barriers between approaches should result in significant advancements in the field. Here we present examples of the interdisciplinary approaches to develop trait-based framework of plankton community ecology. We developed a mechanistic theory of community assembly that includes resource competition and immigration and is capable of making testable predictions. These predictions were then tested in a controlled laboratory experiment with phytoplankton communities. The theory predicted dominance of species with thermal traits matching the environment at each experimental treatment. Most of the predictions were confirmed, either quantitatively or qualitatively. At the same time, the discrepancies revealed the importance of other factors and traits not included in the model, such as nutrient utilization traits and maximum growth rates. This work demonstrates the advantages of the dynamic interplay between theory development and its rigorous testing through experiments.