• Scopus
  • Chinese Science Citation Database (CSCD)
  • A Guide to the Core Journal of China
  • CSTPCD
  • F5000 Frontrunner
  • RCCSE
Advanced search
Yao Jie, Ding Yi, Zhou Chaofan, Tian Lixin, Zang Runguo. Effects of woody plant diversity on aboveground biomass and its scale dependence in tropical natural forest in Hainan Island of southern China[J]. Journal of Beijing Forestry University, 2024, 46(12): 1-10. DOI: 10.12171/j.1000-1522.20240237
Citation: Yao Jie, Ding Yi, Zhou Chaofan, Tian Lixin, Zang Runguo. Effects of woody plant diversity on aboveground biomass and its scale dependence in tropical natural forest in Hainan Island of southern China[J]. Journal of Beijing Forestry University, 2024, 46(12): 1-10. DOI: 10.12171/j.1000-1522.20240237

Effects of woody plant diversity on aboveground biomass and its scale dependence in tropical natural forest in Hainan Island of southern China

More Information
  • Received Date: July 29, 2024
  • Revised Date: August 16, 2024
  • Accepted Date: August 29, 2024
  • Available Online: August 30, 2024
  • Objective 

    This study aimed to elucidate the effects of woody plant diversity (in terms of α-diversity and β-diversity) on aboveground biomass in tropical natural forests of Hainan Island of southern China, and to investigate the corresponding scale dependence, in order to enrich the theoretical framework of biodiversity and ecosystem function relationships.

    Method 

    Based on 30 1-ha forest dynamic sample plots established in the tropical natural forests of Hainan Island, this study utilized data from vegetation surveys, tree spatial distribution, and environmental factor measurements. By employing a spatial multi-scale design and multiple linear regression models, we explored the relationship between woody plant diversity, environmental heterogeneity, and aboveground biomass in tropical natural forests, as well as their variation across different spatial scales.

    Result 

    (1) The mean values of α-diversity and β-diversity in the 30 1-ha sample plots were (10.85 ± 3.63) and (0.30 ± 0.16), respectively. α-diversity did not show significant variation across different spatial scales. While β-diversity exhibited an increasing trend with spatial scales. (2) The effects of α-diversity and β-diversity on aboveground biomass were all significantly positive, and the strength of diversity effects showed a consistent trend with spatial scale. (3) The impact of woody plant diversity on aboveground biomass increased with spatial scale initially (from smaller scales of 400 to 3 600 m2), then it tended to flatten out(at intermediate scales of 3 600 to 6 400 m2), and finally decreased (at larger scales of 6 400 to 10 000 m2). (4) The positive effects of α-diversity on aboveground biomass were significantly stronger than that of β-diversity. (5) Environmental factors had positive effects on aboveground biomass, and the strength of these effects increased with the spatial scale.

    Conclusion 

    This study confirms a significant scale dependency in the relationship between biodiversity and ecosystem function, indicating that the main mechanism influencing the biodiversity and ecosystem functioning (BEF) relationship may vary across different spatial scales. Research limited to a single spatial scale or at a local small scale is insufficient for a comprehensive understanding of the mechanism affecting the BEF relationship. Although β-diversity has a lower explanatory power for variations in aboveground biomass compared with α-diversity, this empirical study highlights the important role of β-diversity in promoting ecosystem functions. This provides new insights and analytical perspectives for further exploration of relationship between biodiversity and ecosystem function across spatial scales. Future research should thoroughly consider the relationship between β-diversity and multiple ecosystem functions (i.e., ecosystem multifunctionality), as well as the mechanism by which β-diversity affects ecosystem multifunctionality at different spatial scales (such as local, regional, and landscape scales).

  • [1]
    Hooper D U, Chapin III F S, Ewel J J, et al. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge[J]. Ecological Monographs, 2005, 75(1): 3−35. doi: 10.1890/04-0922
    [2]
    Schnabel F, Liu X, Kunz M, et al. Species richness stabilizes productivity via asynchrony and drought-tolerance diversity in a large-scale tree biodiversity experiment[J]. Science Advances, 2021, 7(51): eabk1643. doi: 10.1126/sciadv.abk1643
    [3]
    Mori A S, Isbell F, Seidl R. β-diversity, community assembly, and ecosystem functioning[J]. Trends in Ecology and Evolution, 2018, 33(7): 549−564. doi: 10.1016/j.tree.2018.04.012
    [4]
    Liang J, Crowther T W, Picard N, et al. Positive biodiversity-productivity relationship predominant in global forests[J]. Science, 2016, 354: aaf8957. doi: 10.1126/science.aaf8957
    [5]
    Ouyang S, Xiang W, Wang X, et al. Effects of stand age, richness and density on productivity in subtropical forests in China[J]. Journal of Ecology, 2019, 107(5): 2266−2277. doi: 10.1111/1365-2745.13194
    [6]
    Weiher E, Freund D, Bunton T, et al. Advances, challenges and a developing synthesis of ecological community assembly theory[J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2011, 366: 2403−2413. doi: 10.1098/rstb.2011.0056
    [7]
    Leibold M A, Chase J M, Ernest S M. Community assembly and the functioning of ecosystems: how metacommunity processes alter ecosystems attributes[J]. Ecology, 2017, 98(4): 909−919. doi: 10.1002/ecy.1697
    [8]
    Gonzalez A, Germain R M, Srivastava D S, et al. Scaling-up biodiversity-ecosystem functioning research[J]. Ecology Letters, 2020, 23(4): 757−776. doi: 10.1111/ele.13456
    [9]
    Isbell F, Cowles J, Dee L E, et al. Quantifying effects of biodiversity on ecosystem functioning across times and places[J]. Ecology Letters, 2018, 21(6): 763−778. doi: 10.1111/ele.12928
    [10]
    Thompson P L, Isbell F, Loreau M, et al. The strength of the biodiversity–ecosystem function relationship depends on spatial scale[J]. Proceedings of the Royal Society B, 2018, 285(1880): 20180038. doi: 10.1098/rspb.2018.0038
    [11]
    Liang J, Zhou M, Tobin P C, et al. Biodiversity influences plant productivity through niche–efficiency[J]. Proceedings of the National Academy of Sciences, 2015, 112(18): 5738−5743. doi: 10.1073/pnas.1409853112
    [12]
    Winfree R, Reilly J R, Bartomeus I, et al. Species turnover promotes the importance of bee diversity for crop pollination at regional scales[J]. Science, 2018, 359: 791−793. doi: 10.1126/science.aao2117
    [13]
    Reu J C, Catano C P, Spasojevic M J, et al. Beta diversity as a driver of forest biomass across spatial scales[J]. Ecology, 2022, 103(10): e3774. doi: 10.1002/ecy.3774
    [14]
    Wang S, Loreau M. Biodiversity and ecosystem stability across scales in metacommunities[J]. Ecology Letters, 2016, 19(5): 510−518. doi: 10.1111/ele.12582
    [15]
    王凯, 王聪, 冯晓明, 等. 生物多样性与生态系统多功能性的关系研究进展[J]. 生态学报, 2022, 42(1): 11−23.

    Wang K, Wang C, Feng X M, et al. Research progress on the relationship between biodiversity and ecosystem multifunctionality[J]. Acta Ecologica Sinica, 2022, 42(1): 11−23.
    [16]
    Omidipour R, Tahmasebi P, Faizabadi M F, et al. Does β diversity predict ecosystem productivity better than species diversity?[J]. Ecological Indicators, 2021, 122: 107212.
    [17]
    Feizabadi M F, Tahmasebi P, Broujeni E A, et al. Functional diversity, functional composition and functional β diversity drive aboveground biomass across different bioclimatic rangelands[J]. Basic and Applied Ecology, 2021, 52: 68−81. doi: 10.1016/j.baae.2021.01.007
    [18]
    陈圣宾, 欧阳志云, 徐卫华, 等. Beta多样性研究进展[J]. 生物多样性, 2010, 18(4): 323.

    Chen S B, Ouyang Z Y, Xu W H, et al. A review of beta diversity studies[J]. Biodiversity Science, 2010, 18(4): 323.
    [19]
    van der Plas F, Manning P, Soliveres S, et al. Biotic homogenization can decrease landscape-scale forest multifunctionality[J]. Proceedings of the National Academy of Sciences, 2016, 113(13): 3557−3562. doi: 10.1073/pnas.1517903113
    [20]
    Hautier Y, Isbell F, Borer E T, et al. Local loss and spatial homogenization of plant diversity reduce ecosystem multifunctionality[J]. Nature Ecology and Evolution, 2018, 2(1): 50−56.
    [21]
    Leprieur F, Tedesco P A, Hugueny B, et al. Partitioning global patterns of freshwater fish beta diversity reveals contrasting signatures of past climate changes[J]. Ecology Letters, 2011, 14(4): 325−334. doi: 10.1111/j.1461-0248.2011.01589.x
    [22]
    Pedro M S, Rammer W, Seidl R. A disturbance-induced increase in tree species diversity facilitates forest productivity[J]. Landscape Ecology, 2016, 31: 989−1004. doi: 10.1007/s10980-015-0317-y
    [23]
    刘雅莉, 吴俣, 顾盼, 等. 生物多样性–生产力关系国际研究进展与展望[J]. 生态学报, 2023, 43(18): 1−15.

    Liu Y L, Wu Y, Gu P, et al. Progress and future direction of biodiversity-productivity relationship research[J]. Acta Ecologica Sinica, 2023, 43(18): 1−15.
    [24]
    姚杰, 丁易, 黄继红, 等. 海南霸王岭热带山地雨林 6 hm2 森林动态监测样地物种组成与群落特征[J]. 陆地生态系统与保护学报, 2023, 3(3): 1−12.

    Yao J, Ding Y, Huang J H, et al. Species composition and community characteristics of a 6 ha forest dynamics plot in tropical mountain rainforest, Bawangling, Hainan Island, South China[J]. Terrestrial Ecosystem and Conservation, 2023, 3(3): 1−12.
    [25]
    Harms K E, Condit R, Hubbell S P, et al. Habitat associations of trees and shrubs in a 50-ha neotropical forest plot[J]. Journal of Ecology, 2001, 89(6): 947−959. doi: 10.1111/j.1365-2745.2001.00615.x
    [26]
    Yamakura T, Kanzaki M, Itoh A, et al. Topography of a large-scale research plot established within a tropical rain forest at Lambir, Sarawak[J]. Tropics, 1995, 5(1/2): 41−56. doi: 10.3759/tropics.5.41
    [27]
    Chave J, Réjou-Méchain M, Búrquez A, et al. Improved allometric models to estimate the aboveground biomass of tropical trees[J]. Global Change Biology, 2014, 20(10): 3177−3190. doi: 10.1111/gcb.12629
    [28]
    Bu W, Zang R, Ding Y. Field observed relationships between biodiversity and ecosystem functioning during secondary succession in a tropical lowland rainforest[J]. Acta Oecologica, 2014, 55: 1−7. doi: 10.1016/j.actao.2013.10.002
    [29]
    Luo W, Liang J, Cazzolla G R, et al. Parameterization of biodiversity–productivity relationship and its scale dependency using georeferenced tree-level data[J]. Journal of Ecology, 2019, 107(3): 1106−1119. doi: 10.1111/1365-2745.13129
    [30]
    Yao J, Huang J, Zang R. Alpha and beta diversity jointly drive the aboveground biomass in temperate and tropical forests[J]. Ecology and Evolution, 2023, 13(9): e10487. doi: 10.1002/ece3.10487
    [31]
    Ali A. Biodiversity–ecosystem functioning research: brief history, major trends and perspectives[J]. Biological Conservation, 2023, 285: 110210. doi: 10.1016/j.biocon.2023.110210
    [32]
    van der Plas F, Hennecke J, Chase J M, et al. Universal beta-diversity–functioning relationships are neither observed nor expected[J]. Trends in Ecology & Evolution, 2023, 38(6): 532−544.
    [33]
    Mokany K, Thomson J J, Lynch A J J, et al. Linking changes in community composition and function under climate change[J]. Ecological Applications, 2015, 25(8): 2132−2141. doi: 10.1890/14-2384.1
    [34]
    Pasari J R, Levi T, Zavaleta E S, et al. Several scales of biodiversity affect ecosystem multifunctionality[J]. Proceedings of the National Academy of Sciences, 2013, 110(25): 10219−10222. doi: 10.1073/pnas.1220333110
  • Related Articles

    [1]Zhao Yabing, Peng Daoli, Guo Famiao, Wang Yin, Huang Jingxian. Estimating forest growing stock volume based on feature selection and machine learning[J]. Journal of Beijing Forestry University. DOI: 10.12171/j.1000-1522.20240328
    [2]Guo Jialun, Zhong Haomin, Zhao Junbo, Chen Yao. Deresination rate prediction of Masson pine wood based on support vector regression (SVR)[J]. Journal of Beijing Forestry University, 2025, 47(3): 151-161. DOI: 10.12171/j.1000-1522.20240359
    [3]Zhou Lai, Cheng Xiaofang, Zhang Mengtao. Model construction of Larix principis-rupprechtii canopy volume and surface area based on BP neural network[J]. Journal of Beijing Forestry University, 2024, 46(8): 94-100. DOI: 10.12171/j.1000-1522.20230166
    [4]Feng Xinyan, Jia Xin, Huang Jinze, Gao Shengjie, Yuan Min, Liu Tiantian, Jin Chuan. Application of ANN-BiLSTM model to long-term gap-filling of carbon flux data in temperate desert shrub[J]. Journal of Beijing Forestry University, 2023, 45(9): 62-72. DOI: 10.12171/j.1000-1522.20220510
    [5]Zhang Hanyue, Feng Zhongke, Huang Guosheng, Yang Xueqing, Feng Zemin. Research on the growth rate model of Populus spp. considering environmental factors[J]. Journal of Beijing Forestry University, 2022, 44(11): 50-59. DOI: 10.12171/j.1000-1522.20210201
    [6]Zhang Mengku, Jiang Lichun. Prediction of bark thickness for Larix gmelinii based on machine learning[J]. Journal of Beijing Forestry University, 2022, 44(6): 54-62. DOI: 10.12171/j.1000-1522.20210097
    [7]Zhao Jing, Chen Ran, Hao Huichao, Shao Zhuang. Application progress and prospect of machine learning technology in landscape architecture[J]. Journal of Beijing Forestry University, 2021, 43(11): 137-156. DOI: 10.12171/j.1000-1522.20200313
    [8]Lei Xiangdong. Applications of machine learning algorithms in forest growth and yield prediction[J]. Journal of Beijing Forestry University, 2019, 41(12): 23-36. DOI: 10.12171/j.1000-1522.20190356
    [9]ZHANG Wen-yi, JING Tian-zhong, YAN Shan-chun. Studies on prediction models of Dendrolimus superans occurrence area based on machine learning[J]. Journal of Beijing Forestry University, 2017, 39(1): 85-93. DOI: 10.13332/j.1000-1522.20160205
    [10]LIN Zhuo, WU Cheng-zhen, HONG Wei, HONG Tao. Yield model of Cunninghamia lanceolata plantation based on back propagation neural network and support vector machine.[J]. Journal of Beijing Forestry University, 2015, 37(1): 42-54. DOI: 10.13332/j.cnki.jbfu.2015.01.008
  • Cited by

    Periodical cited type(7)

    1. 孙欣,尹紫良,赵琬婧,张治军,王清波,蔡体久,孙晓新. 灌木扩张压力下三江平原沼泽植物群落多样性变化及其土壤控制因子. 应用生态学报. 2024(04): 1016-1024 .
    2. 李元,杨海鑫,齐昊宇,喻其林,郭东罡,张全喜. 运城盐湖湿地土壤重金属污染特征与风险评价. 生态毒理学报. 2024(03): 396-406 .
    3. 管祥楠,董士伟,刘玉,张欣欣,潘瑜春,卢闯. 土壤重金属含量变化的影响因素多目标识别方法. 环境科学. 2024(08): 4791-4801 .
    4. 高明华. 长寿湖国家湿地公园退耕地土壤细菌群落多样性研究. 呼伦贝尔学院学报. 2023(03): 85-91 .
    5. 裘奕斐,王静,徐敏. 江苏滨海县近岸海域海水、沉积物和生物体重金属分布及健康风险评价. 南京师大学报(自然科学版). 2021(01): 71-78 .
    6. 李琦,赵琬婧,王瑜,原卉,王清波,刘成林,李海兴,孙晓新. 三江平原沼泽湿地典型湿地植物对重金属的富集效应. 湿地科学与管理. 2021(02): 9-13 .
    7. 刘德浩,廖文莉,陈智涛,阳艳萍,吴宝宏,舒夏竺,邓仿东. 潼湖湿地土壤重金属污染现状及生态风险评价. 林业与环境科学. 2021(05): 61-68 .

    Other cited types(0)

Catalog

    Article views (368) PDF downloads (110) Cited by(7)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return