U.S. Department of Agriculture: Forest Service -- National Agroforestry Center


Date of this Version



Renninger, H. J., N. Carlo, K. L. Clark, and K. V. R. Schäfer (2014), Modeling respiration from snags and coarse woody debris before and after an invasive gypsy moth disturbance, J. Geophys. Res. Biogeosci., 119, 630–644, doi:10.1002/2013JG002542.


U.S. government work.


Although snags and coarse woody debris are a small component of ecosystem respiration, disturbances can significantly increase the mass and respiration from these carbon (C) pools. The objectives of this study were to (1) measure respiration rates of snags and coarse woody debris throughout the year in a forest previously defoliated by gypsy moths, (2) develop models for dead stem respiration rates, (3) model stand-level respiration rates of dead stems using forest inventory and analysis data sets and environmental variables predisturbance and postdisturbance, and (4) compare total dead stem respiration rates with total ecosystem respiration and net ecosystem exchange. Respiration rates were measured on selected Pinus and Quercus snags and coarse woody debris each month for 1 year in a northeastern U.S. temperate forest. Multiple linear regression using environmental and biometric variables including wood temperature, diameter, density, species, and decay class was used to model respiration rates of dead stems. The mass of snags and coarse woody debris increased more than fivefold after disturbance and respiration rates increased more than threefold. The contribution of dead stems to total ecosystem respiration more than tripled from 0.85% to almost 3% and respiration from dead stems alone was approximately equal to the net ecosystem exchange of the forest in 2011 (fourth year postdisturbance). This study highlights the importance of dead stem C pools and fluxes particularly during disturbance and recovery cycles. With climate change increasing the ranges of many forest pests and pathogens, these data become particularly important for accurately modeling future C cycling.