Predicting soil carbon loss with warming

Journal ArticleThis is the author accepted manuscript. The final version is available from the publisher via the DOI in this record.ARISING FROM: T. W. Crowther et al. Nature 540, 104–108 (2016); doi:10.1038/nature2015

Ecosystem Carbon Stock Influenced by Plantation Practice: Implications for Planting Forests as a Measure of Climate Change Mitigation

Uncertainties remain in the potential of forest plantations to sequestrate carbon (C). We synthesized 86 experimental studies with paired-site design, using a meta-analysis approach, to quantify the differences in ecosystem C pools between plantations and their corresponding adjacent primary and secondary forests (natural forests). Totaled ecosystem C stock in plant and soil pools was 284 Mg C ha−1 in natural forests and decreased by 28% in plantations. In comparison with natural forests, plantations decreased aboveground net primary production, litterfall, and rate of soil respiration by 11, 34, and 32%, respectively. Fine root biomass, soil C concentration, and soil microbial C concentration decreased respectively by 66, 32, and 29% in plantations relative to natural forests. Soil available N, P and K concentrations were lower by 22, 20 and 26%, respectively, in plantations than in natural forests. The general pattern of decreased ecosystem C pools did not change between two different groups in relation to various factors: stand age (<25 years vs. ≥25 years), stand types (broadleaved vs. coniferous and deciduous vs. evergreen), tree species origin (native vs. exotic) of plantations, land-use history (afforestation vs. reforestation) and site preparation for plantations (unburnt vs. burnt), and study regions (tropic vs. temperate). The pattern also held true across geographic regions. Our findings argued against the replacement of natural forests by the plantations as a measure of climate change mitigation

Sources of variation in simulated ecosystem carbon storage capacity from the 5th Climate Model Intercomparison Project (CMIP5)

Ecosystem carbon (C) storage strongly regulates climate-C cycle feedback and is largely determined by both C residence time and C input from net primary productivity (NPP). However, spatial patterns of ecosystem C storage and its variation have not been well quantified in earth system models (ESMs), which is essential to predict future climate change and guide model development. We intended to evaluate spatial patterns of ecosystem C storage capacity simulated by ESMs as part of the 5th Climate Model Intercomparison Project (CMIP5) and explore the sources of multi-model variation from mean residence time (MRT) and/or C inputs. Five ESMs were evaluated, including C inputs (NPP and [gross primary productivity] GPP), outputs (autotrophic/heterotrophic respiration) and pools (vegetation, litter and soil C). ESMs reasonably simulated the NPP and NPP/GPP ratio compared with Moderate Resolution Imaging Spectroradiometer (MODIS) estimates except NorESM. However, all of the models significantly underestimated ecosystem MRT, resulting in underestimation of ecosystem C storage capacity. CCSM predicted the lowest ecosystem C storage capacity (~10 kg C m−2) with the lowest MRT values (14 yr), while MIROC-ESM estimated the highest ecosystem C storage capacity (~36 kg C m−2) with the longest MRT (44 yr). Ecosystem C storage capacity varied considerably among models, with larger variation at high latitudes and in Australia, mainly resulting from the differences in the MRTs across models. Our results indicate that additional research is needed to improve post-photosynthesis C-cycle modelling, especially at high latitudes, so that ecosystem C residence time and storage capacity can be appropriately simulated

2016 International Land Model Benchmarking (ILAMB) Workshop Report

As earth system models (ESMs) become increasingly complex, there is a growing need for comprehensive and multi-faceted evaluation of model projections. To advance understanding of terrestrial biogeochemical processes and their interactions with hydrology and climate under conditions of increasing atmospheric carbon dioxide, new analysis methods are required that use observations to constrain model predictions, inform model development, and identify needed measurements and field experiments. Better representations of biogeochemistryclimate feedbacks and ecosystem processes in these models are essential for reducing the acknowledged substantial uncertainties in 21st century climate change projections

Allelopathic Potential of Invasive Plantago virginica on Four Lawn Species

Plantago virginica L. has invaded many lawn ecosystems in the Eastern part of China. The invasion has incurred an economic cost to remove them. In order to prevent the invasion, it is critical to understand the invasive mechanisms of this species. However, few studies have been conducted on the allelopathic mechanisms of its invasion. In this study, we examined allelopathic effects of P. virginica on germination of seeds and growth of seedlings of four widely used lawn species. We found extensive allelopathic potential of P. virginica on other lawn species, which varied with species and developmental stage. While most effects of the extracts of P. virginica were inhibitory, some variables in some species were promoted by the addition of the extracts. The extracts of P. virginica significantly inhibited seed germination of Agrostis matsumurae. While the overall differences in seed germination rate of Poa annua were significant among treatments, difference between control and any of the treatments was not significant. The height of seedlings of A. matsumurae and Cynodon dactylon was significantly lower under the treatments of adding extracts of P. virginica. In contrast, growth of seedlings of Festuca elata and P. annua did not show significant differences among treatments. The root length of A. matsumurae, C. dactylon and P. annua was suppressed by the extracts of P. virginica whereas root length of F. elata was not affected. Aboveground biomass of A. matsumurae and F. elata was significantly higher than control, except for F. elata at the concentration of 50mg/mL, whereas aboveground biomass of C. dactylon and P. annua was reduced at higher concentrations of the extracts. Except for A. matsumurae, root biomass of the other three lawn species declined under the treatments with the extracts of P. virginica. Our results revealed that P. virginica had allelopathic potential on four lawn species and supported the theory of “novel weapons hypothesis”. Invasion by P. virginica in lawn can be moderated by selecting those species that are not affected or promotionally affected by it.Yeshttp://www.plosone.org/static/editorial#pee

Co-Regulations of Spartina alterniflora Invasion and Exogenous Nitrogen Loading on Soil N2O Efflux in Subtropical Mangrove Mesocosms

We thank Zhonglei Wang, Cunxin Ning, Hui Chen, Qian Huang, Fang Liu and Jian Zhou for their assistance with the greenhouse experiments and gas sampling. We are also grateful to Weimin Song, Rashid Rafique, Junyi Liang, Zheng Shi and Jianyang Xia for editing the manuscript.Both plant invasion and nitrogen (N) enrichment should have significant impact on mangrove ecosystems in coastal regions around the world. However, how N2O efflux in mangrove wetlands responds to these environmental changes has not been well studied. Here, we conducted a mesocosm experiment with native mangrove species Kandelia obovata, invasive salt marsh species Spartina alterniflora, and their mixture in a simulated tide rotation system with or without nitrogen addition. In the treatments without N addition, the N2O effluxes were relatively low and there were no significant variations among the three vegetation types. A pulse loading of exogenous ammonium nitrogen increased N2O effluxes from soils but the stimulatory effect gradually diminished over time, suggesting that frequent measurements are necessary to accurately understand the behavior of N-induced response of N2O emissions. With the N addition, the N2O effluxes from the invasive S. alterniflora were lower than that from native K. obovata mesocosms. This result may be attributed to higher growth of S. alterniflora consuming most of the available nitrogen in soils, and thus inhibiting N2O production. We concluded that N loading significantly increased N2O effluxes, while the invasion of S. alterniflora reduced N2O effluxes response to N loading in this simulated mangrove ecosystem. Thus, both plant invasion and excessive N loading can co-regulate soil N2O emissions from mangrove wetlands, which should be considered when projecting future N2O effluxes from this type of coastal wetland.Yeshttp://www.plosone.org/static/editorial#pee

Improving Estimations of Spatial Distribution of Soil Respiration Using the Bayesian Maximum Entropy Algorithm and Soil Temperature as Auxiliary Data

This study was supported by the NSF China Programs (Grant No. 31300539 and 31570629) and the Public Welfare Technology Application Research Program of Zhejiang province (Grant No. 2015C31004).Soil respiration inherently shows strong spatial variability. It is difficult to obtain an accurate characterization of soil respiration with an insufficient number of monitoring points. However, it is expensive and cumbersome to deploy many sensors. To solve this problem, we proposed employing the Bayesian Maximum Entropy (BME) algorithm, using soil temperature as auxiliary information, to study the spatial distribution of soil respiration. The BME algorithm used the soft data (auxiliary information) effectively to improve the estimation accuracy of the spatiotemporal distribution of soil respiration. Based on the functional relationship between soil temperature and soil respiration, the BME algorithm satisfactorily integrated soil temperature data into said spatial distribution. As a means of comparison, we also applied the Ordinary Kriging (OK) and Co-Kriging (Co-OK) methods. The results indicated that the root mean squared errors (RMSEs) and absolute values of bias for both Day 1 and Day 2 were the lowest for the BME method, thus demonstrating its higher estimation accuracy. Further, we compared the performance of the BME algorithm coupled with auxiliary information, namely soil temperature data, and the OK method without auxiliary information in the same study area for 9, 21, and 37 sampled points. The results showed that the RMSEs for the BME algorithm (0.972 and 1.193) were less than those for the OK method (1.146 and 1.539) when the number of sampled points was 9 and 37, respectively. This indicates that the former method using auxiliary information could reduce the required number of sampling points for studying spatial distribution of soil respiration. Thus, the BME algorithm, coupled with soil temperature data, can not only improve the accuracy of soil respiration spatial interpolation but can also reduce the number of sampling points.Yeshttp://www.plosone.org/static/editorial#pee

Thermal optimality of net ecosystem exchange of carbon dioxide and underlying mechanisms

It is well established that individual organisms can acclimate and adapt to temperature to optimize their functioning. However, thermal optimization of ecosystems, as an assemblage of organisms, has not been examined at broad spatial and temporal scales. Here, we compiled data from 169 globally distributed sites of eddy covariance and quantified the temperature response functions of net ecosystem exchange (NEE), an ecosystem-level property, to determine whether NEE shows thermal optimality and to explore the underlying mechanisms. We found that the temperature response of NEE followed a peak curve, with the optimum temperature (corresponding to the maximum magnitude of NEE) being positively correlated with annual mean temperature over years and across sites. Shifts of the optimum temperature of NEE were mostly a result of temperature acclimation of gross primary productivity (upward shift of optimum temperature) rather than changes in the temperature sensitivity of ecosystem respiration. Ecosystem-level thermal optimality is a newly revealed ecosystem property, presumably reflecting associated evolutionary adaptation of organisms within ecosystems, and has the potential to significantly regulate ecosystemclimate change feedbacks. The thermal optimality of NEE has implications for understanding fundamental properties of ecosystems in changing environments and benchmarking global models.This is the publisher’s final pdf. The published article is copyrighted by New Phytologist Trust and can be found at: http://www.newphytologist.org/Keywords: Climate change, Temperature acclimation, Optimum temperature, Thermal optimality, Temperature adaptatio

Global changes alter plant multi-element stoichiometric coupling

Plant stoichiometric coupling among all elements is fundamental to maintaining growth‐related ecosystem functions. However, our understanding of nutrient balance in response to global changes remains greatly limited to plant carbon : nitrogen : phosphorus (C : N : P) coupling. Here we evaluated nine element stoichiometric variations with one meta‐analysis of 112 global change experiments conducted across global terrestrial ecosystems and one synthesis over 1900 species observations along natural environment gradients across China. We found that experimentally increased soil N and P respectively enhanced plant N : potassium (K), N : calcium (Ca) and N : magnesium (Mg), and P : K, P : Ca and P : Mg, and natural increases in soil N and P resulted in qualitatively similar responses. The ratios of N and P to base cations decreased both under experimental warming and with naturally increasing temperature. With decreasing precipitation, these ratios increased in experiments but decreased under natural environments. Based on these results, we propose a new stoichiometric framework in which all plant element contents and their coupling are not only affected by soil nutrient availability, but also by plant nutrient demand to maintain diverse functions under climate change. This study offers new insights into understanding plant stoichiometric variations across a full set of mineral elements under global changes