Divergent changes in particulate and mineral-associated organic carbon upon permafrost thaw

Acknowledgements This work was supported by the National Natural Science Foundation of China (31988102, 31825006, 91837312, and 32101332), the Second Tibetan Plateau Scientific Expedition and Research (STEP) program (2019QZKK0106 and 2019QZKK0302), and the Fundamental Research Foundation of Chinese Academy of Forestry (CAFYBB2020MA008).Peer reviewedPublisher PD

Decadal soil carbon accumulation across Tibetan permafrost regions

Acknowledgements We thank the members of Peking University Sampling Teams (2001–2004) and IBCAS Sampling Teams (2013–2014) for assistance in field data collection. We also thank the Forestry Bureau of Qinghai Province and the Forestry Bureau of Tibet Autonomous Region for their permission and assistance during the sampling process. This study was financially supported by the National Natural Science Foundation of China (31670482 and 31322011), National Basic Research Program of China on Global Change (2014CB954001 and 2015CB954201), Chinese Academy of Sciences-Peking University Pioneer Cooperation Team, and the Thousand Young Talents Program.Peer reviewedPostprintPostprin

Response to Comment on 'Soil carbon persistence governed by plant input and mineral protection at regional and global scales'

We demonstrated that ignoring the non-linear relationship between topsoil Delta C-14 and plant carbon (C) input in Wu et al.'s analysis was the fundamental reason for the discrepancy between their analysis and ours. By considering such a non-linear relationship, plant C input still predominantly governs the topsoil C turnover

Predicting alien herb invasion with machine learning models: biogeographical and life-history traits both matter

Identifying the variables associated with invasiveness is a core task for developing risk assessment models to predict invasion potential. However, quantitative models with both biogeographical and life-history variables for invasion risk assessment in China are limited. We hypothesized that (1) compared to statistical algorithms, some machine learning models could offer a promising quantitative approach with high accuracy for potential invader prediction; (2) native range distribution size, origins and life-history traits co-determine an alien plant's performance in the latter invasion stage. In this study, we used four machine learning models [classification and regression tree (CART), multivariate adaptive regression spline (MARS), random forest (RF) and multiple additive regression tree (MART)] and two traditional statistical algorithms [logistic regression (LR) and linear discriminant analysis (LDA)] to assess the relative importance of biogeographical and trait variables in the naturalized-invasion stage of 150 invasive and 87 non-invasive herb plants in China. Our results showed that good performance was the case for all predictive models (AUROC ranges from 0.68 to 0.87), which had overall mean performance value ranging from 0.66 to 0.82. Compared with traditional statistical algorithms, MART and RF models have a consistently higher accuracy, indicating that these two models could be used as alternative quantitative approaches for risk assessment. Additionally, both biogeographical (native range distribution size) and life-history traits (seed weight) were screened out by the models, suggesting their high correlation with plant invasiveness and important roles in risk assessment

Conspecific plasticity and invasion: invasive populations of Chinese tallow (Triadica sebifera) have performance advantage over native populations only in low soil salinity.

Global climate change may increase biological invasions in part because invasive species may have greater phenotypic plasticity than native species. This may be especially important for abiotic stresses such as salt inundation related to increased hurricane activity or sea level rise. If invasive species indeed have greater plasticity, this may reflect genetic differences between populations in the native and introduced ranges. Here, we examined plasticity of functional and fitness-related traits of Chinese tallow (Triadica sebifera) populations from the introduced and native ranges that were grown along a gradient of soil salinity (control: 0 ppt; Low: 5 ppt; Medium: 10 ppt; High: 15 ppt) in a greenhouse. We used both norm reaction and plasticity index (PIv) to estimate the conspecific phenotypic plasticity variation between invasive and native populations. Overall, invasive populations had higher phenotypic plasticity of height growth rate (HGR), aboveground biomass, stem biomass and specific leaf area (SLA). The plasticity Index (PIv) of height growth rate (HGR) and SLA each were higher for plants from invasive populations. Absolute performance was always comparable or greater for plants from invasive populations versus native populations with the greatest differences at low stress levels. Our results were consistent with the "Master-of-some" pattern for invasive plants in which the fitness of introduced populations was greater in more benign conditions. This suggests that the greater conspecific phenotypic plasticity of invasive populations compared to native populations may increase invasion success in benign conditions but would not provide a potential interspecific competitive advantage in higher salinity soils that may occur with global climate change in coastal areas

Temperature sensitivity of permafrost carbon release mediated by mineral and microbial properties

Temperature sensitivity (Q(10)) of permafrost carbon (C) release upon thaw is a vital parameter for projecting permafrost C dynamics under climate warming. However, it remains unclear how mineral protection interacts with microbial properties and intrinsic recalcitrance to affect permafrost C fate. Here, we sampled permafrost soils across a 1000-km transect on the Tibetan Plateau and conducted two laboratory incubations over 400- and 28-day durations to explore patterns and drivers of permafrost C release and its temperature response after thaw. We find that mineral protection and microbial properties are two types of crucial predictors of permafrost C dynamics upon thaw. Both high C release and Q(10) are associated with weak organo-mineral associations but high microbial abundances and activities, whereas high microbial diversity corresponds to low Q(10). The attenuating effects of mineral protection and the dual roles of microbial properties would make the permafrost C-climate feedback more complex than previously thought

Mineral and Climatic Controls Over Soil Organic Matter Stability Across the Tibetan Alpine Permafrost Region

Permafrost thaw could accelerate microbial decomposition, lead to greenhouse gases emissions into the atmosphere, and thus trigger a positive feedback to climate warming. As a key parameter reflecting the resistance of soil organic matter (SOM) to be decomposed by microorganisms, SOM stability may determine the strength of permafrost carbon (C)-climate feedback. Adequate understanding of patterns and drivers of SOM stability can thus contribute to predicting permafrost C cycle and its feedback to climate change. However, due to limited observations, it remains unknown whether biochemical selectivity or physico-chemical protection dominates SOM stability in permafrost regions. By combining large-scale soil sampling, thermal analysis and random forest model, we quantified SOM stability in the top 10 cm using thermogravimetry and differential scanning calorimetry, and explored its spatial patterns across the Tibetan alpine permafrost region. We then constructed structural equation model to evaluate the relative importance of climatic variables, edaphic properties, substrate quality, and mineral variables in regulating SOM stability over a broad geographic scale. Our results indicated that SOM stability exhibited an increasing tendency from the southeastern to northwestern plateau. The stronger SOM stability was associated with higher mineral-organic associations and more arid conditions. By contrast, substrate quality had limited effects on SOM stability. Overall, these results provide large-scale evidence for the physico-chemical protection hypothesis, highlighting the importance of considering mineral variables in Earth system models to better predict soil C dynamics across permafrost regions