Alternate Trait-Based Leaf Respiration Schemes Evaluated at Ecosystem-Scale Through Carbon Optimization Modeling and Canopy Property Data

• Published in: Journal of Advances in Modeling Earth Systems
• Main Authors: Thomas, R. Quinn, Williams, M., Cavaleri, M. A., Exbrayat, J. -F., Smallman, T. L., Street, L. E.
• Other Authors: Forest Resources and Environmental Conservation
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Geophysical Union 2019
• Subjects: Physical Sciences; Meteorology & Atmospheric Sciences; carbon; Nitrogen; scaling; ecosystem modeling; Optimization; respiration; Rain forest; Primary productivity; ARCTIC TRANSECT; AREA INDEX; Plant; Photosynthesis; Exchange; Climate; Evapotranspiration; 0401 Atmospheric Sciences; Arctic
• Online Access: http://hdl.handle.net/10919/104992; https://doi.org/10.1029/2019MS001679

Leaf maintenance respiration (Rleaf,m) is a major but poorly understood component of the terrestrial carbon cycle (C). Earth systems models (ESMs) use simple sub-models relating Rleaf,m to leaf traits, applied at canopy scale. Rleaf,m models vary depending on which leaf N traits they incorporate (e.g., mass or area based) and the form of relationship (linear or nonlinear). To simulate vegetation responses to global change, some ESMs include ecological optimization to identify canopy structures that maximize net C accumulation. However, the implications for optimization of using alternate leaf-scale empirical Rleaf,m models are undetermined. Here we combine alternate well-known empirical models of Rleaf,m with a process model of canopy photosynthesis. We quantify how net canopy exports of C vary with leaf area index (LAI) and total canopy N (TCN). Using data from tropical and arctic canopies, we show that estimates of canopy Rleaf,m vary widely among the three models. Using an optimization framework, we show that the LAI and TCN values maximizing C export depends strongly on the Rleaf,m model used. No single model could match observed arctic and tropical LAI-TCN patterns with predictions of optimal LAI-TCN. We recommend caution in using leaf-scale empirical models for components of ESMs at canopy-scale. Rleaf,m models may produce reasonable results for a specified LAI, but, due to their varied representations of Rleaf,mfoliar N sensitivity, are associated with different and potentially unrealistic optimization dynamics at canopy scale. We recommend ESMs to be evaluated using response surfaces of canopy C export in LAI-TCN space to understand and mitigate these risks. Published version