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生物、非生物和功能变量如何沿着压力梯度驱动孙德尔本斯红树林森林地下土壤碳储量?

How biotic, abiotic, and functional variables drive belowground soil carbon stocks along stress gradient in the Sundarbans Mangrove Forest?

机构信息

Chair of Forest Growth and Yield Science, Department of Life Science Systems, TUM School of Life Sciences, Technical University of Munich, Hans-Carl-von-Carlowitz-Platz 2, 85354 Freising, Germany; Forestry and Wood Technology Discipline, Khulna University, Khulna, 9208, Bangladesh.

Department of Forestry and Environmental Science, Shahjalal University of Science and Technology, Sylhet, Bangladesh.

出版信息

J Environ Manage. 2023 Jul 1;337:117772. doi: 10.1016/j.jenvman.2023.117772. Epub 2023 Mar 21.

DOI:10.1016/j.jenvman.2023.117772
PMID:36958279
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10109099/
Abstract

Mangrove forests, some of the most carbon-dense ecosystems on Earth, play an important role in climate change mitigation through storing carbon in the soil. However, increasing anthropogenic pressures and sea level rise are likely to alter mangrove forest structure and functions, including the major source of carbon in mangrove ecosystems - below-ground soil carbon stocks (BSCS). Although estimating soil carbon stocks has been a popular practice in the mangroves, but poorly understood the (I) the linkage between BSCS and key ecosystem drivers (i.e., biotic, abiotic, and functional) and in (II) determining the pathways of how BSCS and multiple forest variables interact along stress gradients. This lack of understanding limits our ability to predict ecosystem carbon dynamics under future changes in climate. Here, we aimed to understand how abiotic factors (such as salinity, canopy gap fraction, nutrients, and soil pH), biotic factors (e.g., structural parameters, canopy packing, and leaf area index, LAI), and forest functional variables (e.g., growth and aboveground biomass stocks, AGB) affect BSCS (i.e., soil organic carbon, SOC, and root carbon, RC) using spatiotemporal data collected from the Sundarbans Mangrove Forest (SMF) in Bangladesh. We observed that BSCS decreased significantly with increasing salinity (e.g., from 70.6 Mg C ha in the low-saline zone to 44.6 Mg C ha in the high-saline zone). In contrast, the availability of several macronutrients (such as nitrogen, phosphorous, and potassium), LAI, species diversity, AGB, and growth showed a significant positive effect on SOC and RC. Stand properties, including tree height, basal area, density, canopy packing, and structural diversity, had a non-significant but positive impact on RC, while tree height and basal area significantly influenced SOC. Pathway analysis showed that salinity affects BSCS variability directly and indirectly by regulating stand structure and restricting nutrients and forest functions, although basal area, nutrients, and LAI directly enhance RC stocks. Our results indicate that an increase in nutrient content, canopy density, species diversity, and leaf area index can enhance BSCS, as they improve forest functions and contribute to a better understanding of the underlying mechanisms.

摘要

红树林是地球上碳密度最高的生态系统之一,通过在土壤中储存碳,在减缓气候变化方面发挥着重要作用。然而,人为压力的增加和海平面上升可能会改变红树林的结构和功能,包括红树林生态系统中主要的碳源——地下土壤碳储量(BSCS)。尽管估算土壤碳储量在红树林中是一种常见做法,但人们对 BSCS 与关键生态系统驱动因素(即生物、非生物和功能)之间的联系以及(II)确定 BSCS 与多个森林变量在压力梯度下相互作用的途径知之甚少。这种缺乏了解限制了我们预测未来气候变化下生态系统碳动态的能力。在这里,我们旨在了解非生物因素(如盐度、冠层间隙分数、养分和土壤 pH)、生物因素(例如结构参数、冠层密度和叶面积指数、LAI)和森林功能变量(例如生长和地上生物量储量、AGB)如何影响 BSCS(即土壤有机碳、SOC 和根碳、RC),方法是使用从孟加拉国孙德尔本斯红树林(SMF)收集的时空数据。我们观察到,BSCS 随着盐度的增加而显著下降(例如,从低盐区的 70.6 Mg C ha 到高盐区的 44.6 Mg C ha)。相比之下,几种大量营养物(如氮、磷和钾)、LAI、物种多样性、AGB 和生长对 SOC 和 RC 有显著的正影响。林分特性,包括树高、基面积、密度、冠层密度和结构多样性,对 RC 有非显著但正的影响,而树高和基面积对 SOC 有显著影响。路径分析表明,盐度通过调节林分结构和限制养分和森林功能,直接和间接影响 BSCS 的变异性,尽管基面积、养分和 LAI 直接增加 RC 储量。我们的结果表明,增加养分含量、冠层密度、物种多样性和叶面积指数可以增强 BSCS,因为它们可以改善森林功能,并有助于更好地理解潜在机制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/68af5ff697f7/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/18dd85a19369/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/8db6a325a635/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/2fa293c72af1/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/854fa8808449/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/48d26c823eb0/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/68af5ff697f7/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/18dd85a19369/ga1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/8db6a325a635/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/2fa293c72af1/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/854fa8808449/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/48d26c823eb0/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4255/10109099/68af5ff697f7/gr5.jpg

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