IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i9p2602-d228633.html
   My bibliography  Save this article

Belowground Bud Bank Distribution and Aboveground Community Characteristics along Different Moisture Gradients of Alpine Meadow in the Zoige Plateau, China

Author

Listed:
  • Xinjing Ding

    (Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 320 Donggang West Road, Lanzhou 730000, China
    University of Chinese Academy of Sciences, 19A Yuquan Road, Beijing 100049, China)

  • Peixi Su

    (Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 320 Donggang West Road, Lanzhou 730000, China)

  • Zijuan Zhou

    (Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 320 Donggang West Road, Lanzhou 730000, China)

  • Rui Shi

    (Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, 320 Donggang West Road, Lanzhou 730000, China)

Abstract

The belowground bud bank plays an important role in plant communities succession and maintenance. In order to understand the response of the bud bank to the sod layer moisture, we investigated the bud bank distribution, size, and composition of six different water gradient alpine meadows through excavating in the Zoige Plateau. The results showed: (1) The alpine meadow plant belowground buds were mainly distributed in the 0–10 cm sod layer, accounting for 74.2%–100% of the total. The total bud density of the swamp wetland and degraded meadow was the highest (16567.9 bud/m 3 ) and the lowest (4839.5 bud/m 3 ). (2) A decrease of the moisture plant diversity showed a trend of increasing first and then decreasing. Among six alpine meadows the swamp meadow plant diversity was the highest, and species richness, Simpson, Shannon–Wiener, and Pielou were 10.333, 0.871, 0.944, and 0.931, respectively. (3) The moisture was significantly positively correlated with the total belowground buds and short rhizome bud density. There were significant positive correlations with sod layer moisture and tiller bulb bud density. This study indicates that the moisture affected bud bank distribution and composition in the plant community, and the results provide important information for predicting plant community succession in the alpine meadow with future changes in precipitation patterns.

Suggested Citation

  • Xinjing Ding & Peixi Su & Zijuan Zhou & Rui Shi, 2019. "Belowground Bud Bank Distribution and Aboveground Community Characteristics along Different Moisture Gradients of Alpine Meadow in the Zoige Plateau, China," Sustainability, MDPI, vol. 11(9), pages 1-13, May.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:9:p:2602-:d:228633
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/9/2602/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/11/9/2602/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brian J. Enquist & James H. Brown & Geoffrey B. West, 1998. "Allometric Scaling of Plant Energetics and Population Density," Working Papers 98-11-104, Santa Fe Institute.
    2. Brian J. Enquist & James H. Brown & Geoffrey B. West, 1998. "Allometric scaling of plant energetics and population density," Nature, Nature, vol. 395(6698), pages 163-165, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chen, Yanguang, 2014. "An allometric scaling relation based on logistic growth of cities," Chaos, Solitons & Fractals, Elsevier, vol. 65(C), pages 65-77.
    2. Barnes, Belinda & Mokany, Karel & Roderick, Michael, 2007. "Allocation within a generic scaling framework," Ecological Modelling, Elsevier, vol. 201(2), pages 223-232.
    3. Sorrell, Steve, 2015. "Reducing energy demand: A review of issues, challenges and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 74-82.
    4. Wolpert, David & Harper, Kyle, 2024. "The computational power of a human society: a new model of social evolution," SocArXiv qj83z, Center for Open Science.
    5. Tao, Yong & Lin, Li & Wang, Hanjie & Hou, Chen, 2023. "Superlinear growth and the fossil fuel energy sustainability dilemma: Evidence from six continents," Structural Change and Economic Dynamics, Elsevier, vol. 66(C), pages 39-51.
    6. David H. Wolpert & Kyle Harper, 2024. "The computational power of a human society: a new model of social evolution," Papers 2408.08861, arXiv.org.
    7. Chen, Yanguang, 2017. "Multi-scaling allometric analysis for urban and regional development," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 673-689.
    8. Hendriks, A. Jan, 2007. "The power of size: A meta-analysis reveals consistency of allometric regressions," Ecological Modelling, Elsevier, vol. 205(1), pages 196-208.
    9. Peters, Ronny & Olagoke, Adewole & Berger, Uta, 2018. "A new mechanistic theory of self-thinning: Adaptive behaviour of plants explains the shape and slope of self-thinning trajectories," Ecological Modelling, Elsevier, vol. 390(C), pages 1-9.
    10. He, Ji-Huan, 2007. "Shrinkage of body size of small insects: A possible link to global warming?," Chaos, Solitons & Fractals, Elsevier, vol. 34(3), pages 727-729.
    11. Wiegand, Kerstin & Saltz, David & Ward, David & Levin, Simon A., 2008. "The role of size inequality in self-thinning: A pattern-oriented simulation model for arid savannas," Ecological Modelling, Elsevier, vol. 210(4), pages 431-445.
    12. Song, Dong-Ming & Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2009. "Statistical properties of world investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2450-2460.
    13. Lu, Zhihao & Yin, Di & Chen, Peng & Wang, Hongzhen & Yang, Yuhang & Huang, Guangtuan & Cai, Lankun & Zhang, Lehua, 2020. "Power-generating trees: Direct bioelectricity production from plants with microbial fuel cells," Applied Energy, Elsevier, vol. 268(C).
    14. Hunt, Allen G. & Faybishenko, Boris & Powell, Thomas L., 2020. "A new phenomenological model to describe root-soil interactions based on percolation theory," Ecological Modelling, Elsevier, vol. 433(C).
    15. Louis J. Irving, 2015. "Carbon Assimilation, Biomass Partitioning and Productivity in Grasses," Agriculture, MDPI, vol. 5(4), pages 1-19, November.
    16. Jiang Zhang & Lingfei Wu, 2013. "Allometry and Dissipation of Ecological Flow Networks," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-8, September.
    17. Ogawa, Kazuharu, 2009. "Mathematical analysis of change in forest carbon use efficiency with stand development: A case study on Abies veitchii Lindl," Ecological Modelling, Elsevier, vol. 220(11), pages 1419-1424.
    18. Laurent Augusto & Antra Boča, 2022. "Tree functional traits, forest biomass, and tree species diversity interact with site properties to drive forest soil carbon," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    19. Ma, Ping & Han, Xiao-Hui & Lin, Yue & Moore, John & Guo, Yao-Xin & Yue, Ming, 2019. "Exploring the relative importance of biotic and abiotic factors that alter the self-thinning rule: Insights from individual-based modelling and machine-learning," Ecological Modelling, Elsevier, vol. 397(C), pages 16-24.
    20. Harris, Lora A. & Brush, Mark J., 2012. "Bridging the gap between empirical and mechanistic models of aquatic primary production with the metabolic theory of ecology: An example from estuarine ecosystems," Ecological Modelling, Elsevier, vol. 233(C), pages 83-89.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:9:p:2602-:d:228633. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.