Estimating carbon carrying capacity in natural forest ecosystems across heterogeneous landscapes: addressing sources of error
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Evaluating contributions of forest ecosystems to climate change mitigation requires well-calibrated carbon cycle models with quantified baseline carbon stocks. An appropriate baseline for carbon accounting of natural forests at landscape scales is carbon carrying capacity (CCC); defined as the mass of carbon stored in an ecosystem under prevailing environmental conditions and natural disturbance regimes but excluding anthropogenic disturbance. Carbon models require empirical measurements for input and calibration, such as net primary production (NPP) and total ecosystem carbon stock (equivalent to CCC at equilibrium). We sought to improve model calibration by addressing three sources of errors that cause uncertainty in carbon accounting across heterogeneous landscapes: (1) data-model representation, (2) data-object representation, (3) up-scaling. We derived spatially explicit empirical models based on environmental variables across landscape scales to estimate NPP (based on a synthesis of global site data of NPP and gross primary productivity, n=27), and CCC (based on site data of carbon stocks in natural eucalypt forests of southeast Australia, n=284). The models significantly improved predictions, each accounting for 51% of the variance. Our methods to reduce uncertainty in baseline carbon stocks, such as using appropriate calibration data from sites with minimal human disturbance, measurements of large trees and incorporating environmental variability across the landscape, have generic application to other regions and ecosystem types. These analyses resulted in forest CCC in southeast Australia (mean total biomass of 360 t C ha-1, with cool moist temperate forests up to 1000 t C ha-1) that are larger than estimates from other national and international (average biome 202 t C ha-1) carbon accounting systems. Reducing uncertainty in estimates of carbon stocks in natural forests is important to allow accurate accounting for losses of carbon due to human activities and sequestration of carbon by forest growth.
Global Change Biology
Environmental Sciences not elsewhere classified