Abstract: 

Despite abundant work on the drivers of denitrification starting in the 1950s, mechanistic complexity and methodological challenges of direct denitrification measurements have resulted in a lack of reliable rate estimates across landscapes, and a lack of operationally valid, robust models. Denitrification is dependent on nonlinear dynamics among multiple onsite factors, such as pH, temperature, and the immediate availability of nitrate (NO3-), carbon, and oxygen (O2) , as well as distal drivers such as climate, topography, and vegetative composition and structure. Our ability to predict thresholds of denitrification functionality given perturbations such as climate change, is dependent on building models that can capture both the spatial and temporal dynamics of the process at the appropriate scales, and provide estimates of model prediction uncertainty.

Tidal systems are particularly challenging, with mixing of bidirectional inputs that vary in space and time; we lack suitable denitrification models that account for distal influences, such as tidal inundation and variability in NO3- sources. In this project our group aims to develop a probabilistic tidal salt marsh denitrification model based on empirical data and a mechanistic understanding of the denitrification process. The model functions will characterize the spatial and temporal relationships between denitrification rates and dominant NO3- sources, and the spatial patterning of O2 drawdown. We will use this model to predict the interactive effects of climate change and identify thresholds of salt marsh denitrification functioning as climate shifts.