Evaluating an exponential respiration model to alternative models for soil respiration components in a Canadian wildfire chronosequence (FireResp, v1.0)

• Main Authors: Zobitz, John, Aaltonen, Heidi, Zhou, Xuan, Beninger, Frank, Pumpanen, Jukka, Köster, Kajar
• Format: Text
• Language: English
• Published: 2021
• Subjects: Canada; Northwest Territories; Yukon
• Online Access: https://doi.org/10.5194/gmd-2021-185; https://gmd.copernicus.org/preprints/gmd-2021-185/

Forest fires modify soil organic carbon and suppress soil respiration for many decades since the initial disturbance. The associated changes in soil autotrophic and heterotrophic respiration from the time of the forest fire however, is less well characterized. We analyzed models of soil autotrophic and heterotrophic respiration with a novel dataset across a fire chronosequence in the Yukon and Northwest Territories of Canada. The dataset consisted of soil incubation experiments and field measurements of soil respiration and soil carbon stocks. The models ranged from a Q 10 (exponential) model of respiration to models of heterotrophic respiration using Michaelis-Menten kinetics parameterized with soil microbe carbon. For model evaluation we applied model selection metrics (Akaike Information Criterion) and compared predicted patterns in soil respiration components across the chronosequence. Parameters estimated with data from the 5 cm soil depth had better model-data comparisons than parameters estimated with data from the 10 cm soil depth. The model-data fit was improved by including parameters estimated from soil incubation experiments. Models that incorporated microbial carbon with Michaelis-Menten kinetics reproduced patterns in autotrophic and heterotrophic soil respiration components across the chronosequence. Autotrophic respiration was associated with aboveground tree biomass at more recently burned sites, but this association was less robust at older sites in the chronosequence. Our results provide support for more structured soil respiration models than standard Q 10 exponential models.