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New method for extracting plant indicators based on their adaptive responses to management practices: application to semi‐natural and artificial grassland data
Author(s) -
Koyanagi Tomoyo,
Kusumoto Yoshinobu,
Hiradate Syuntaro,
Morita Sayaka,
Yokogawa Masashi,
Takahashi Yoshitaka,
Sato Chiyoshi
Publication year - 2013
Publication title -
applied vegetation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.096
H-Index - 64
eISSN - 1654-109X
pISSN - 1402-2001
DOI - 10.1111/j.1654-109x.2012.01204.x
Subject(s) - grassland , forb , trait , vegetation (pathology) , biological dispersal , multidimensional scaling , ecology , grazing , canopy , environmental science , species richness , natural (archaeology) , geography , biology , statistics , medicine , population , demography , mathematics , pathology , sociology , computer science , programming language , archaeology
Questions What is the best way to identify plant indicators for monitoring the responses of semi‐natural grassland communities to various management practices (i.e. burning, burning and mowing, burning and grazing)? Can a multi‐trait plant functional group ( PFG ) approach extract indicator species more effectively than a single‐trait approach? Location Semi‐natural and artificial grasslands in the northern and eastern parts of the somma of Mt. Aso, Kumamoto Prefecture, Japan. Methods We analysed a data set from 203 semi‐natural and artificial grassland vegetation plots (each 1 m × 1 m), and compared species and functional compositions among the management types and between the northern and eastern locations using non‐metric multidimensional scaling ( NMDS ). Potential indicator species were extracted by indicator species analysis ( INSPAN ) and compared with the results of single‐trait and multi‐trait PFG approaches, which classify plant species into groups based on individual or combinations of traits (growth form, life span, dispersal mode, flowering season and plant height). Results Only the multi‐trait PFG approach successfully clustered the vegetation plots for each management type. Semi‐natural burned‐and‐grazed grasslands in the eastern and northern regions were characterized by a specific PFG among 21 PFG s, whereas the same three PFG s characterized the burned‐and‐mowed semi‐natural grassland of the two regions. The extracted PFG s represent combinations of vegetative, dispersal and reproductive traits (e.g. short, wind‐ and animal‐dispersed species for grazed semi‐natural grasslands; tall, summer‐flowering forbs for mowed semi‐natural grasslands). As compared to indicator species extracted by the multi‐trait PFG approach, INSPAN extracted additional indicator species, but some of these were inappropriate. Conclusions We identified PFG s representing each management type and characterized species adaptations to different disturbance regimes, such as mowing and grazing. Among the species extracted by INSPAN , only those classified into the representative PFG s can serve as indicators for characterizing managed semi‐natural grasslands. Our findings indicate that using a multi‐trait PFG approach can provide reasonable plant indicators for monitoring grassland communities throughout the regions when combined with a species‐based INSPAN approach. This proposed method would contribute to the conservation of plant biodiversity in threatened semi‐natural grazed and mowed grasslands.