Constrained partitioning of autotrophic and heterotrophic respiration reduces model uncertainties of forest ecosystem carbon fluxes but not stocks

We partitioned the soil carbon dioxide flux (R_s) into its respective autotrophic and heterotrophic components in a mature temperate-boreal forest (Howland Forest in Maine, USA). We combined automated chamber measurements of R_s with two different partitioning methods: (1) a classic root trenching experiment and (2) a radiocarbon (¹⁴C) mass balance approach. With a model-data fusion approach, we used these data to constrain a parsimonious ecosystem model (FöBAAR), and we investigated differences in modeled C fluxes and pools under both current and future climate scenarios. The trenching experiment indicated that heterotrophic respiration accounted for 53 ± 11% of total R_s. In comparison, using the ¹⁴C method, the heterotrophic contribution was 42 ± 9%. For both current and future model runs, incorporating the partitioning data as constraints substantially reduced the uncertainties of autotrophic and heterotrophic respiration fluxes. Moreover, with best fit model parameters, the two partitioning methods yielded fundamentally different estimates of the relative contributions of autotrophic and heterotrophic respiration to total R_s, especially at the annual time scale. Surprisingly, however, modeled soil C and biomass C pool size trajectories did not differ significantly between model runs based on the different methods. Instead, model differences in partitioning were compensated for by changes in C allocation, resulting in similar, but still highly uncertain, soil C pool trajectories. Our findings show that incorporating constraints on the partitioning of R_s can reduce model uncertainties of fluxes but not pools, and the results are sensitive to the partitioning method used.