The rapid development of experimental and observational networks along with the push for more open-access data is expanding the amount of data available to plant ecophysiologists. These data boons have greatly expanded the spatiotemporal breadth of plant traits. However, the data is heterogeneous, making it difficult to derive ecological mechanisms. Previous research has often used sophisticated statistical methods to parse these data, but these approaches can be subject to misinterpretation of the driving mechanisms. Here, I promote the use of ecophysiological theory as a means to help determine mechanisms from large-scale hetereogeneous datasets. I present examples of this approach using global trait datasets and a coordinated research network, the Nutrient Network. Specifically, I demonstrate the use of a theoretical model as a null model to test mechanisms across broad scales.
Results/Conclusions
I first present the example of the structure of theoretical null model. The model utilizes an optimization-based approach to predict leaf and whole-plant traits. The optimization is based on least-cost theory, which posits that, optimally, plants will operate to maximize photosynthetic carbon assimilation, while minimizing water and nutrient use. In a comparison to global plant trait data, I find that leaf traits (photosynthesis, leaf nitrogen) tend to respond more strongly to aboveground abiotic conditions, while whole-plant traits (leaf area index, biomass) tend to respond more strongly to belowground abiotic conditions. Similar results hold when examining responses at the Nutrient Network. I show that using the theoretical model allows for greater mechanistic understanding of these responses and argue that such models can be used to improve the reliability of predictions of future ecosystem processes.