論文

査読有り
2017年1月

A lognormal distribution of the lengths of terminal twigs on self-similar branches of elm trees

PROCEEDINGS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES
  • Kohei Koyama
  • ,
  • Ken Yamamoto
  • ,
  • Masayuki Ushio

284
1846
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1098/rspb.2016.2395
出版者・発行元
ROYAL SOC

Lognormal distributions and self-similarity are characteristics associated with a wide range of biological systems. The sequential breakage model has established a link between lognormal distributions and self-similarity and has been used to explain species abundance distributions. To date, however, there has been no similar evidence in studies of multicellular organismal forms. We tested the hypotheses that the distribution of the lengths of terminal stems of Japanese elm trees (Ulmus davidiana), the end products of a self-similar branching process, approaches a lognormal distribution. We measured the length of the stem segments of three elm branches and obtained the following results: (i) each occurrence of branching caused variations or errors in the lengths of the child stems relative to their parent stems; (ii) the branches showed statistical self-similarity; the observed error distributions were similar at all scales within each branch and (iii) the multiplicative effect of these errors generated variations of the lengths of terminal twigs that were well approximated by a lognormal distribution, although some statistically significant deviations from strict lognormality were observed for one branch. Our results provide the first empirical evidence that statistical self-similarity of an organismal form generates a lognormal distribution of organ sizes.

リンク情報
DOI
https://doi.org/10.1098/rspb.2016.2395
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/28053062
Web of Science
https://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=JSTA_CEL&SrcApp=J_Gate_JST&DestLinkType=FullRecord&KeyUT=WOS:000393400500010&DestApp=WOS_CPL
ID情報
  • DOI : 10.1098/rspb.2016.2395
  • ISSN : 0962-8452
  • eISSN : 1471-2954
  • PubMed ID : 28053062
  • Web of Science ID : WOS:000393400500010

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