Benchmarking models with data: ecosystem carbon and nutrient budget along an elevation gradient in a subtropical forest ecosystem

The ability to simulate vegetation dynamics and their feedback with nutrient cycling to affect ecosystem productivity underpins our prediction of the land carbon sink under climate change. Predictive models are now capable of simulating complex ecosystem processes, including the recent advancement in simulating vegetation dynamics and ecosystem phosphorus cycling, but there is a general lack of empirical evidence to form a systematic evaluation of the model predictions, especially how functional diversity affect ecosystem nutrient cycling and its consequence for productivity. Here, we developed a dataset based on 9 permanent plots (20 x 20 m) along an elevation gradient (300 – 1200m a.s.l.) in a subtropical forested mountain in eastern China. We measured vegetation growth, estimated forest structure and species composition, and compiled ecosystem-scale carbon (C), nitrogen (N) and phosphorus (P) budgets based on concentration, pool and flux data collected from dominant canopy trees, understorey herbaceous plants, and soil organic and inorganic components in these forested plots. Our aims are three-fold: 1) to understand how C, N and P are distributed along the plant-microbe-soil continuum; 2) to disentangle how different growth and nutrient use strategies of plant and soil microbes affect ecosystem productivity and regulate the rate nutrient cycling; and 3) to benchmark predictive models in simulating ecosystem vegetation dynamics and their interaction with C, N, and P cycle processes. Our research will contribute towards better understanding of the functional diversity and productivity relationship, and will contribute towards an improved predictive capacity to simulate vegetation dynamics and the land carbon sink under climate change.