Biotic responses buffer warming-induced soil organic carbon loss in Arctic tundra.
Junyi LiangJiangyang XiaZheng ShiLifen JiangShuang MaXingjie LuMarguerite E MauritzSusan M NataliElaine PegoraroChristopher Ryan PentonCésar PlazaVerity G SalmonGerardo CelisJames R ColeKonstantinos T KonstantinidisJames M TiedjeJizhong ZhouEdward A G SchuurYiqi LuoPublished in: Global change biology (2018)
Climate warming can result in both abiotic (e.g., permafrost thaw) and biotic (e.g., microbial functional genes) changes in Arctic tundra. Recent research has incorporated dynamic permafrost thaw in Earth system models (ESMs) and indicates that Arctic tundra could be a significant future carbon (C) source due to the enhanced decomposition of thawed deep soil C. However, warming-induced biotic changes may influence biologically related parameters and the consequent projections in ESMs. How model parameters associated with biotic responses will change under warming and to what extent these changes affect projected C budgets have not been carefully examined. In this study, we synthesized six data sets over 5 years from a soil warming experiment at the Eight Mile Lake, Alaska, into the Terrestrial ECOsystem (TECO) model with a probabilistic inversion approach. The TECO model used multiple soil layers to track dynamics of thawed soil under different treatments. Our results show that warming increased light use efficiency of vegetation photosynthesis but decreased baseline (i.e., environment-corrected) turnover rates of SOC in both the fast and slow pools in comparison with those under control. Moreover, the parameter changes generally amplified over time, suggesting processes of gradual physiological acclimation and functional gene shifts of both plants and microbes. The TECO model predicted that field warming from 2009 to 2013 resulted in cumulative C losses of 224 or 87 g/m2 , respectively, without or with changes in those parameters. Thus, warming-induced parameter changes reduced predicted soil C loss by 61%. Our study suggests that it is critical to incorporate biotic changes in ESMs to improve the model performance in predicting C dynamics in permafrost regions.
Keyphrases
- climate change
- high glucose
- diabetic rats
- drug induced
- genome wide
- magnetic resonance imaging
- gene expression
- plant growth
- big data
- oxidative stress
- risk assessment
- pregnant women
- microbial community
- endothelial cells
- artificial intelligence
- genome wide identification
- transcription factor
- body composition
- current status
- deep learning