Alternate trait-based leaf respiration schemes evaluated at ecosystem-scale through carbon optimization modeling and canopy property data

R. Q. Thomas, Mathew Williams, M. A. Cavaleri, Jean-Francois Exbrayat, T. Luke Smallman, Lorna Street

Research output: Contribution to journalArticlepeer-review

Abstract

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 non‐linear). 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,m–foliar N sensitivity, are associated with different and potentially unrealistic optimization dynamics at canopy scale. We recommend ESMs be evaluated using response surfaces of canopy C export in LAI‐TCN space to understand and mitigate these risks.
Original languageEnglish
JournalJournal of Advances in Modelling Earth Systems (JAMES)
Early online date10 Dec 2019
DOIs
Publication statusE-pub ahead of print - 10 Dec 2019

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