Dataset Information

Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event.

ABSTRACT: Moso bamboo has large potential to alleviate global warming through carbon sequestration. Since soil respiration (R _s ) is a major source of CO₂ emissions, we analyzed the dynamics of soil respiration (R _s ) and its relation to environmental factors in a Moso bamboo (Phllostachys heterocycla cv. pubescens) forest to identify the relative importance of biotic and abiotic drivers of respiration. Annual average R _s was 44.07 t CO₂ ha^-1 a^-1. R _s correlated significantly with soil temperature (P < 0.01), which explained 69.7% of the variation in R _s at a diurnal scale. Soil moisture was correlated significantly with R _s on a daily scale except not during winter, indicating it affected R _s . A model including both soil temperature and soil moisture explained 93.6% of seasonal variations in R _s . The relationship between R _s and soil temperature during a day showed a clear hysteresis. R _s was significantly and positively (P < 0.01) related to gross ecosystem productivity and leaf area index, demonstrating the significance of biotic factors as crucial drivers of R _s .

SUBMITTER: Liu Y

PROVIDER: S-EPMC6215440 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event.

Liu Yuli Y Zhou Guomo G Du Huaqiang H Berninger Frank F Mao Fangjie F Li Xuejian X Chen Liang L Cui Lu L Li Yangguang Y Zhu Di'en D

PeerJ 20181031

Moso bamboo has large potential to alleviate global warming through carbon sequestration. Since soil respiration (R s ) is a major source of CO2 emissions, we analyzed the dynamics of soil respiration (R s ) and its relation to environmental factors in a Moso bamboo (Phllostachys heterocycla cv. pubescens) forest to identify the relative importance of biotic and abiotic drivers of respiration. Annual average R s</ ...[more]

PMID: 30402345

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Project description:Leaf stomatal and anatomical traits strongly influence plant productivity. Understanding the environmental adaptation mechanisms of leaf stomatal and anatomical traits and their relationship with ecosystem productivity is essential to better understand and predict the long-term adaptation strategies to climate change of moso bamboo forests. Here, we selected 6 sites within the moso bamboo distribution area, measured 3 leaf stomatal traits and 10 leaf anatomical traits of unmanaged moso bamboo stands. We explored the spatial variation characteristics of these traits and their response to environmental changes, assessed the relationships among these traits at regional scales through network analysis, and tested the direct and indirect effects of environmental, leaf stomatal and anatomical traits on gross primary productivity (GPP) of bamboo stands using structural equation modeling (SEM). The results showed that both climate and soil factors significantly affected leaf stomatal and anatomical traits of moso bamboo. Solar radiation (SR) and mean annual precipitation (MAP) out of the climatic factors were the key drivers of variation in leaf stomatal and anatomical traits, respectively. Soil moisture and nutrients out of the soil properties significantly affected both leaf stomatal and anatomical traits of moso bamboo. Network analysis further indicated that there was a significant correlation between leaf stomata and anatomical traits. Stomatal size (SS) showed the highest centrality value at the regional scale, indicating that it plays a key role in adjusting the adaptation of plants to external environmental conditions. SEM analysis showed that environment did not directly but indirectly affect GPP via stomatal performance. The environment explained 53.3% and 39.2% of the variation in leaf stomatal and anatomical traits, respectively, and leaf stomatal traits explained 20.8% of the regional variation in GPP. Our results demonstrate a direct effect of leaf stomatal traits rather than leaf anatomical traits on bamboo ecosystem productivity, which provides new insights into model predictions of bamboo forests under global climate change.

Project description:Data analysis and model-data comparisons in the environmental sciences require diagnostic measures that quantify time series dynamics and structure, and are robust to noise in observational data. This paper investigates the temporal dynamics of environmental time series using measures quantifying their information content and complexity. The measures are used to classify natural processes on one hand, and to compare models with observations on the other. The present analysis focuses on the global carbon cycle as an area of research in which model-data integration and comparisons are key to improving our understanding of natural phenomena. We investigate the dynamics of observed and simulated time series of Gross Primary Productivity (GPP), a key variable in terrestrial ecosystems that quantifies ecosystem carbon uptake. However, the dynamics, patterns and magnitudes of GPP time series, both observed and simulated, vary substantially on different temporal and spatial scales. We demonstrate here that information content and complexity, or Information Theory Quantifiers (ITQ) for short, serve as robust and efficient data-analytical and model benchmarking tools for evaluating the temporal structure and dynamical properties of simulated or observed time series at various spatial scales. At continental scale, we compare GPP time series simulated with two models and an observations-based product. This analysis reveals qualitative differences between model evaluation based on ITQ compared to traditional model performance metrics, indicating that good model performance in terms of absolute or relative error does not imply that the dynamics of the observations is captured well. Furthermore, we show, using an ensemble of site-scale measurements obtained from the FLUXNET archive in the Mediterranean, that model-data or model-model mismatches as indicated by ITQ can be attributed to and interpreted as differences in the temporal structure of the respective ecological time series. At global scale, our understanding of C fluxes relies on the use of consistently applied land models. Here, we use ITQ to evaluate model structure: The measures are largely insensitive to climatic scenarios, land use and atmospheric gas concentrations used to drive them, but clearly separate the structure of 13 different land models taken from the CMIP5 archive and an observations-based product. In conclusion, diagnostic measures of this kind provide data-analytical tools that distinguish different types of natural processes based solely on their dynamics, and are thus highly suitable for environmental science applications such as model structural diagnostics.

Dataset Information

Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event.

Publications

Soil respiration of a Moso bamboo forest significantly affected by gross ecosystem productivity and leaf area index in an extreme drought event.

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