Print PageA central paradigm in ecology is that changes in species composition and dominance are linked to species traits that influence species performance and response in different environments. However, a quantitative and predictive theory to scale from traits to communities and ecosystems has yet to emerge. We review and merge several lines of work including metabolic scaling theory, to outline a general integrated theoretical framework termed “Trait Driver Theory” (TDT). The TDT approach is based upon individuals instead of taxa and builds upon the evolutionary theory of population responses to changing environments. A central assumption is an optimum trait-to-phenotype mapping via organismal growth rate and the distribution of traits within the community. Within TDT the traits that define the scaling of growth are central to linking traits with ecosystem performance. TDT makes several mechanistic connections between phenotypes, organism performance, and the differing biotic and abiotic drivers that influence the shape and dynamics of community-level trait distributions. It predicts how the shape of the community trait distribution, as defined by the central-moments, then drives ecosystem functioning. Further, via TDT, differing ecological hypotheses can now be extended to make differing predictions for community trait structure and the functioning of ecosystems.
Results/Conclusions
Using the 140-year ecological Park Grass experiment we assessed predictions generated for TDT. Because specific leaf area (SLA, leaf area per dry mass) is a key trait underlying plant growth we first focused on quantifying community SLA frequency distributions We find broad support for several of the central hypotheses and predictions of TDT. For both fertilized and control plots, all four moments of the community SLA distribution change significantly. However, the direction and rate of change of the trait distributions for fertilized versus control plots diverged over time. In support of TDT, shifts in the community trait distribution led to changes in ecosystem productivity. The annual net primary productivity (NPP) was positively correlated with community mean and kurtosis of the SLA distribution. However, plot NPP was negatively correlated to community variance SLA. According to TDT a negative relationship between NPP and trait variance is due to an increased number of phenotypes that are displaced from the optimal trait value. These predictions are in contrast to expectations based on species richness or functional diversity. We show that empirical data are more consistent with TDT predictions indicating that TDT offers a more general framework for beginning to develop a quantitative and predictive science of biodiversity and ecosystems.
