Chin J Plan Ecolo ›› 2012, Vol. 36 ›› Issue (1): 30-38.doi: 10.3724/SP.J.1258.2012.00030

• Research Articles • Previous Articles     Next Articles

Plant species-area relationship in a 42-hm2 research plot of coniferous and board-leaved mixed forest in Jiaohe, Jilin Province, China

JIANG Jun, ZHANG Chun-Yu, and ZHAO Xiu-Hai*   

  1. Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
  • Received:2011-04-28 Revised:2011-11-15 Online:2012-01-05 Published:2012-01-01
  • Contact: ZHAO Xiu-Hai


Aims The Species Area Relationship (SAR) is a fundamental pattern in ecology. Recent analyses have often demonstrated substantial uncertainty in selecting the best SAR model for a data set. Our objective was to understand the effects of sample design on species-area relations, in order to suggest a more appropriate SAR model for the given plot data.
Methods A long-term 42 hm2 forest plot was established in conifer and board-leaved mixed forest in Jiaohe, China. All trees with diameter at breast height (DBH) > 1 cm were tagged and their height, DBH and crown diameter recorded. We propose three SARs models (logarithmic function, power function and logistic function) to compare SARs constructed from nested design and random design. We use Akaike Information Criterion (AIC) to compare the quality fit of each SAR model given the data.
Important findings The way of constructing SARs influences the outcome. The random design showed significantly better goodness of fit of SARs model than the nested design. Among the three SAR models, Logistic function model from the random design was the best, suggesting it provided a reasonable description of the species-area relationship in the plot. This study demonstrates the significance of scale variance in species-area relationships; the effects of area on species richness are variable and can be scale dependent. However, because the species distribution patterns and spatial scale vary greatly, further work is needed to consider environmental effects and the community succession on different spatial scales.

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