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Beyond carbon flux partitioning: Carbon allocation and nonstructural carbon dynamics inferred from continuous fluxes.

Carbon (C) allocation and non-structural carbon (NSC) dynamics play essential roles in plant growth and survival under stress and disturbance. However, quantitative understanding of these processes remains limited. Here we propose a framework, where we connect commonly measured carbon cycle components (eddy covariance fluxes of canopy CO2 exchange, soil CO2 efflux, and allometry-based biomass and net primary production) by a simple mass balance model, to derive ecosystem-level NSC dynamics (NSCi ), C translocation (dCi ) to and the biomass production efficiency (BPEi ) in above- and below-ground plant (i = agp and bgp) compartments. We applied this framework to two long-term monitored loblolly pine (Pinus taeda) plantations of different ages in North Carolina and characterized the variations of NSC and allocation in years under normal and drought conditions. The results indicated that the young stand did not have net NSC flux at the annual scale whereas the mature stand stored a near-constant proportion of new assimilates as NSC every year under normal conditions, being comparable in magnitude to new structural growth. Roots consumed NSC in drought and stored a significant amount of NSC post-drought. The above- and below-ground dCi and BPEi varied more from year to year in the young stand and approached a relatively stable pattern in the mature stand. The belowground BPEbgp differed the most between the young and mature stands, and was most responsive to drought. With the internal C dynamics quantified, this framework may also improve the biomass production estimation that reveals the variations resulting from droughts. Overall, these quantified ecosystem-scale dynamics were consistent with existing evidence from tree-based manipulative experiments and measurements, and demonstrated that combining the continuous fluxes as proposed here can provide additional information about plant internal C dynamics. Given that it is based on broadly available flux data, the proposed framework is promising to improve the allocation algorithms in ecosystem C cycle models, and offers new insights into observed variability in soil-plant-climate interactions.