MISC

2009年7月

Stand-scale spatial patterns of soil microbial biomass in natural cold-temperate beech forests along an elevation gradient

SOIL BIOLOGY & BIOCHEMISTRY
  • Xin Zhao
  • ,
  • Quan Wang
  • ,
  • Yoshitaka Kakubari

41
7
開始ページ
1466
終了ページ
1474
記述言語
英語
掲載種別
DOI
10.1016/j.soilbio.2009.03.028
出版者・発行元
PERGAMON-ELSEVIER SCIENCE LTD

This study focuses on spatial heterogeneity in the soil microbial biomass (SMB) of typical climax beech (Fagus crenata) at the stand scale in forest ecosystems of the cold-temperate mountain zones of Japan. Three beech-dominated sites were selected along an altitudinal gradient and grid sampling was used to collect soil samples at each site. The highest average SMB density was observed at the site 1500 m a.s.l. (44.9 gC m(-2)), the lowest was recorded at the site 700 m a.s.l. (18.9 gC m(-2)); the average SMB density at the 550 m site (36.5 gC m(-2)) was close to the overall median of all three sites. Geostatistics, which is specifically designed to take spatial autocorrelation into account, was then used to analyze the data collected. All sites generally exhibited stand-scale spatial autocorrelation at a lag distance of 10-18 m in addition to the small-scale spatial dependence noted at <3.5 m at the 550 m site. Correlation analysis with an emphasis on spatial dependency showed SMB to be significantly correlated with bulk density at the 550 and 1500 m sites, dissolved organic carbon (DOC) at the 700 and 1500 m sites, and nitrogen (N) at the 550 and 700 m sites. However, no soil parameter showed a significant correlation with SMB at every site, and some variables were also differently correlated (negative or positive) with SMB at different sites. This suggests that the factors controlling the spatial distribution of SMB are very complex and responsive to local in situ conditions. SMB regression models were generated from both the ordinary least-squares (OLS) and generalized least-squares (GLS) models. GLS performance was only superior to OLS when cross-variograms were accurately fitted. Geostatistics is preferable, however, since these techniques take the spatial non-stationarity of samples into account. In addition, the sampling numbers for given minimum detectable differences (MDD(s)) are provided for each site for future SMB monitoring. (C) 2009 Elsevier Ltd. All rights reserved.

リンク情報
DOI
https://doi.org/10.1016/j.soilbio.2009.03.028
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000267775300012&DestApp=WOS_CPL
ID情報
  • DOI : 10.1016/j.soilbio.2009.03.028
  • ISSN : 0038-0717
  • Web of Science ID : WOS:000267775300012

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