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Plant cover estimation based on the beta distribution in grassland vegetation
Author(s) -
Chen Jun,
Shiyomi Masae,
Bonham Charles D.,
Yasuda Taisuke,
Hori Yoshimichi,
Yamamura Yasuo
Publication year - 2008
Publication title -
ecological research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.628
H-Index - 68
eISSN - 1440-1703
pISSN - 0912-3814
DOI - 10.1007/s11284-007-0443-3
Subject(s) - quadrat , grassland , mathematics , statistics , vegetation (pathology) , grid , cover (algebra) , ecology , geometry , biology , transect , medicine , mechanical engineering , pathology , engineering
Cover is the most frequently used measure of abundance in vegetation surveys of grasslands, and various qualitative and semi‐quantitative methods have been developed for visual estimation of this metric. Field survey is usually made with a point‐grid plate. The frequency distributions of cover derived from point‐grid counts follow a beta distribution. Combining point‐grid counts from a field survey and the beta distribution for a statistical analysis, we developed an effort‐saving cover‐measurement method. Cover is measured with a transparent plastic plate on which, for example, 10 × 10 = 100 points are arranged in a lattice with 1‐cm grid spacing (thus, one point count represents 1 cm 2 of cover). N quadrats are set out at randomly dispersed sites in a grassland, and, in each, the plastic plate is used for making counts. The number of grid points located above a given species is counted in every quadrat until the number of counted points reaches a given value c , which is determined in advance. If the number of counted points reaches c in a quadrat, the count is stopped and the quadrat is classified in the category “> c ”. In quadrats where c is not attained, full point counts above the species bodies are made. Let g be the number of observed quadrats whose cover is ≤ c . Using these g cover measurements and the number of quadrats ( N − g ) with cover > c , we can quantitatively estimate cover for each species and the spatial pattern index value based on the maximum likelihood method. In trial counts using this method, the time savings varied between 5% and 41%, depending on the shape of the cover frequency distribution. The mean cover value estimates agreed well with conventional measures without a stopping point (i.e., based on full counts of all points in each quadrat).