Abstract
AbstractQuestionIn vegetation science, the compositional dissimilarity among two or more groups of plots is usually tested with dissimilarity‐based multivariate analysis of variance (db‐MANOVA), whereas the compositional characterization of the different groups is performed by means of indicator species analysis. Although db‐MANOVA and indicator species analysis are apparently very far from each other, the question we address here is: can we put both approaches under the same methodological umbrella?MethodsWe will show that for a specific class of dissimilarity measures, the partitioning of variation used in one‐factor db‐MANOVA can be additively decomposed into species‐level values allowing us to identify the species that contribute most to the compositional differences among the groups.ResultsThe proposed method, for which we provide a simple R function, is illustrated with one small data set on alpine vegetation sampled along a successional gradient.ConclusionThe species that contribute most to the compositional differences among the groups are preferentially concentrated in particular groups of plots. Therefore, they can be appropriately called indicator species. This connects multivariate analysis of variance with indicator species analysis.
Highlights
Db-MANOVA and indicator species analysis are apparently very far from each other, the question we address here is: can we put both approaches under the same methodological umbrella? Methods: We will show that for a specific class of dissimilarity measures, the partitioning of variation used in one-factor db-MANOVA can be additively decomposed into species-level values allowing us to identify the species that contribute most to the compositional differences among the
‘Which species contribute most to the compositional differences among the groups?’ These questions are generally answered with different tools: the compositional dissimilarity among groups of plots is usually tested with dissimilarity-based multivariate analysis of variance
Caccianiga et al (2006) showed that the establishment of the first pioneer species is associated with random dispersal mechanisms that drive the colonization of the glacial deposits by early-successional ruderal forbs, such as Cerastium uniflorum, Oxyria digyna, or Tussilago farfara, whereas the late successional stages are preferentially colonized by stress-tolerant graminoids, such as Carex curvula, or Carex sempervirens
Summary
For a set of plots that are grouped according to some external criteria, such as selected environmental variables or different experimental treatments, two relevant questions usually asked by community ecologists are: ‘Are these groups compositionally different from each other?’ and ‘Which species contribute most to the compositional differences among the groups?’ These questions are generally answered with different tools: the compositional dissimilarity among groups of plots is usually tested with dissimilarity-based multivariate analysis of variance (Pillar &Orlóci 1996; Anderson 2001), whereas the compositional characterization of the different groups is performed by means of indicator species analysis (Dufrêne & Legendre 1997; Chytrý et al.2002).Dissimilarity-based multivariate analysis of variance (or shorter db-MANOVA) is applicable to any type of compositional data, irrespective of the number of species sampled and the way they are sampled (i.e. presence/absence scores, number of individuals, species cover, etc.) provided that a meaningful measure is used to adequately represent the dissimilarity between pairs of plots (or relevés, communities, assemblages, quadrats, sites, etc.).k 1, 2,..., K be Nk such that N K k 1 kN and d ij be the compositional dissimilarity between plot i and plot j. For a set of plots that are grouped according to some external criteria, such as selected environmental variables or different experimental treatments, two relevant questions usually asked by community ecologists are: ‘Are these groups compositionally different from each other?’ and ‘Which species contribute most to the compositional differences among the groups?’ These questions are generally answered with different tools: the compositional dissimilarity among groups of plots is usually tested with dissimilarity-based multivariate analysis of variance Orlóci 1996; Anderson 2001), whereas the compositional characterization of the different groups is performed by means of indicator species analysis (Dufrêne & Legendre 1997; Chytrý et al.2002). Once the compositional differences among the groups of plots have been verified, the identification of diagnostic species for the different groups is a relevant step for their ecological characterization (Dufrêne & Legendre 1997). According to Chytrý et al (2002), diagnostic species “include species which preferably occur in a single vegetation unit (character species) or in a few vegetation units (differential species)”
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
