MULTIVARIATE ANALYSIS OF THE ENDANGERED MEDICINAL SPECIES Cercidiphyllum japonicum COMMUNITIES IN THE SHENNONGJIA RESERVE, CENTRAL CHINA

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Naiqi Song http://orcid.org/0000-0001-5337-5218 Jin-tun Zhang http://orcid.org/0000-0002-3163-6808

Abstract

Cercidiphyllum japonicum is an endangered and nationally protected medicinal plant species and its conservation is urgent. Multivariate methodology is important in study ecological relations and conservation for endangered species. Fifty-eight 10 m ´ 20 m quadrats of Cercidiphyllum japonicum communities were randomly set up along an altitudinal gradient in the Shennongjia Nature Reserve, Central China. Data for species composition and environmental variables were measured and recorded in each quadrat. To investigate the community variation, multivariate analysis methods, two-way indicator species analysis (TWINSPAN) and canonical correspondence analysis (CCA), were conducted. Moreover, species diversity indices were used to analyze the relationships between species diversity and environmental variables. The results showed that there were eight types of Cercidiphyllum japonicum communities. Each community had its own compositional, structural and environmental characteristics. The variation in Cercidiphyllum japonicum communities was significantly related to elevation and slope; aspect and litter thickness were also related to community variations. Elevation and slope were revealed as important factors that influence community distribution and diversity. Species diversity showed a quadratic and negative correlation with elevation. The importance values of Cercidiphyllum japonicum also showed a quadratic and negative correlation with species diversity. For conservational purposes, species diversity should be maintained within a reasonable range.

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How to Cite
SONG, Naiqi; ZHANG, Jin-tun. MULTIVARIATE ANALYSIS OF THE ENDANGERED MEDICINAL SPECIES Cercidiphyllum japonicum COMMUNITIES IN THE SHENNONGJIA RESERVE, CENTRAL CHINA. CERNE, [S.l.], v. 24, n. 3, p. 180-189, oct. 2018. ISSN 2317-6342. Available at: <http://cerne.ufla.br/site/index.php/CERNE/article/view/1812>. Date accessed: 17 nov. 2018.
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