|Title||Heterotrophic respiration in disturbed forests: A review with examples from North America|
|Publication Type||Journal Article|
|Year of Publication||2011|
|Authors||Harmon, Mark E., Bond-Lamberty Ben, TANG JIANWU, and Vargas Rodrigo|
|Journal||Journal of Geophysical Research|
|Keywords||Black Spruce chronosequence, carbon dioxide flux, climate change, coarse woody debris, leaf litter decomposition, long term carbon, net primary production, old growth forests, soil organic matter, temperature sensitivity|
Heterotrophic respiration (R(H)) is a major process releasing carbon to the atmosphere and is essential to understanding carbon dynamics in terrestrial ecosystems. Here we review what is known about this flux as related to forest disturbance using examples from North America. The global R(H) flux from soils has been estimated at 53-57 Pg C yr(-1), but this does not include contributions from other sources (i.e., dead wood, heart-rots). Disturbance-related inputs likely account for 20-50% of all R(H) losses in forests, and disturbances lead to a reorganization of ecosystem carbon pools that influences how RH changes over succession. Multiple controls on R(H) related to climate, the material being decomposed, and the decomposers involved have been identified, but how each potentially interacts with disturbance remains an open question. An emerging paradigm of carbon dynamics suggests the possibility of multiple periods of carbon sinks and sources following disturbance; a large contributing factor is the possibility that postdisturbance R(H) does not always follow the monotonic decline assumed in the classic theory. Without a better understanding and modeling of R(H) and its controlling factors, it will be difficult to estimate, forecast, understand, and manage carbon balances of regions in which disturbance frequency and severity are changing. Meeting this challenge will require (1) improved field data on processes and stores, (2) an improved understanding of the physiological and environmental controls of R(H), and (3) a more formal analysis of how model structure influences the R(H) responses that can be predicted.