Fourth-corner generation of plant functional response groups
(2009) In Environmental and Ecological Statistics 16(4). p.561-584- Abstract
- Plant functional response groups (PFGs) are now widely established as a tool to investigate plant-environment relationships. Different statistical methods to form PFGs are used in the literature. One way is to derive emergent groups by classifying species based on correlation of biological attributes and subjecting these groups to tests of response to environmental variables. Another way is to search for associations of occurrence data, environmental variables and trait data simultaneously. The fourth-corner method is one way to assess the relationships between single traits and habitat factors. We extended this statistical method to a generally applicable procedure for the generation of plant functional response groups by developing new... (More)
- Plant functional response groups (PFGs) are now widely established as a tool to investigate plant-environment relationships. Different statistical methods to form PFGs are used in the literature. One way is to derive emergent groups by classifying species based on correlation of biological attributes and subjecting these groups to tests of response to environmental variables. Another way is to search for associations of occurrence data, environmental variables and trait data simultaneously. The fourth-corner method is one way to assess the relationships between single traits and habitat factors. We extended this statistical method to a generally applicable procedure for the generation of plant functional response groups by developing new randomization procedures for presence/absence data of plant communities. Previous PFG groupings used either predefined groups or emergent groups i.e. classifications based on correlations of biological attributes (Lavorel et al Trends Ecol Evol 12:474-478, 1997), of the global species pool and assessed their functional response. However, since not all PFGs might form emergent groups or may be known by experts, we used a permutation procedure to optimise functional grouping. We tested the method using an artificial test data set of virtual plants occurring in different disturbance treatments. Direct trait-treatment relationships as well as more complex associations are incorporated in the test data. Trait combinations responding to environmental variables could be clearly distinguished from non-responding combinations. The results are compared with the method suggested by Pillar (J Veg Sci 10:631-640) for the identification of plant functional groups. After exploring the statistical properties using an artificial data set, the method is applied to experimental data of a greenhouse experiment on the assemblage of plant communities. Four plant functional response groups are formed with regard to differences in soil fertility on the basis of the traits canopy height and spacer length. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/1518478
- author
- Lehsten, Veiko LU ; Harmand, Peter and Kleyer, Michael
- organization
- publishing date
- 2009
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Null models, Functional response groups, Functional groups, Fourth-corner method, Canopy height, Plant, Plant traits, Seed weight, Spacer length
- in
- Environmental and Ecological Statistics
- volume
- 16
- issue
- 4
- pages
- 561 - 584
- publisher
- Springer
- external identifiers
-
- wos:000271948600007
- scopus:70450250478
- ISSN
- 1352-8505
- DOI
- 10.1007/s10651-008-0098-4
- language
- English
- LU publication?
- yes
- id
- e5e4d219-0ea6-4cb5-86bf-a17b316bfee3 (old id 1518478)
- date added to LUP
- 2016-04-01 14:32:12
- date last changed
- 2022-04-22 03:46:32
@article{e5e4d219-0ea6-4cb5-86bf-a17b316bfee3,
abstract = {{Plant functional response groups (PFGs) are now widely established as a tool to investigate plant-environment relationships. Different statistical methods to form PFGs are used in the literature. One way is to derive emergent groups by classifying species based on correlation of biological attributes and subjecting these groups to tests of response to environmental variables. Another way is to search for associations of occurrence data, environmental variables and trait data simultaneously. The fourth-corner method is one way to assess the relationships between single traits and habitat factors. We extended this statistical method to a generally applicable procedure for the generation of plant functional response groups by developing new randomization procedures for presence/absence data of plant communities. Previous PFG groupings used either predefined groups or emergent groups i.e. classifications based on correlations of biological attributes (Lavorel et al Trends Ecol Evol 12:474-478, 1997), of the global species pool and assessed their functional response. However, since not all PFGs might form emergent groups or may be known by experts, we used a permutation procedure to optimise functional grouping. We tested the method using an artificial test data set of virtual plants occurring in different disturbance treatments. Direct trait-treatment relationships as well as more complex associations are incorporated in the test data. Trait combinations responding to environmental variables could be clearly distinguished from non-responding combinations. The results are compared with the method suggested by Pillar (J Veg Sci 10:631-640) for the identification of plant functional groups. After exploring the statistical properties using an artificial data set, the method is applied to experimental data of a greenhouse experiment on the assemblage of plant communities. Four plant functional response groups are formed with regard to differences in soil fertility on the basis of the traits canopy height and spacer length.}},
author = {{Lehsten, Veiko and Harmand, Peter and Kleyer, Michael}},
issn = {{1352-8505}},
keywords = {{Null models; Functional response groups; Functional groups; Fourth-corner method; Canopy height; Plant; Plant traits; Seed weight; Spacer length}},
language = {{eng}},
number = {{4}},
pages = {{561--584}},
publisher = {{Springer}},
series = {{Environmental and Ecological Statistics}},
title = {{Fourth-corner generation of plant functional response groups}},
url = {{http://dx.doi.org/10.1007/s10651-008-0098-4}},
doi = {{10.1007/s10651-008-0098-4}},
volume = {{16}},
year = {{2009}},
}