On the identification of the most suitable traits for plant functional trait analyses

By M. Bernhardt‐Römermann, Christine Römermann, R. Nuske, A. Parth, S. Klotz, W. Schmidt, J. Stadler. 2008

129 citations recorded by the source corpus.

Subfields: Ecology, Evolution, Behavior and Systematics, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/f9682b9b40a070ce96e39998572fb36460c1931b

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In Ecology, Evolution, Behavior and Systematics

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

The n‐dimensional hypervolume (2014, background): “For example, it may be unclear what variables should and should not be included in an analysis, and how sensitive a given result may be to this choice (Petchey & Gaston, 2006; Bernhardt-Römermann et al., 2008).”
A structured and dynamic framework to advance traits-based theory and prediction in ecology. (2010, methodology): “Although lacking appropriate structure or dynamics, traditional statistical approaches remain useful in reducing the complexity of trait data sets (Bernhardt-Römermann et al. 2008).”
The intrinsic dimensionality of plant traits and its relevance to community assembly (2014, background): “Community ecologists have sought to insert traits into models to explain the distribution of species along environmental gradients (Bernhardt-R€omermann et al. 2008; Dray & Legendre 2008; Shipley 2010; Kleyer et al. 2012; Pollock, Morris & Vesk 2012; Jamil et al. 2013), to predict shifts in species…”
Substitutes for grazing in semi-natural grasslands - do mowing or mulching represent valuable alternatives to maintain vegetation structure? (2009, background): “Up to now, few studies have integrated these data in one common analysis (but compare Barbaro et al. 2000; Thuiller et al. 2006; Garnier et al. 2007; Bernhardt-Römermann et al. 2008).”
Assessing species and community functional responses to environmental gradients: which multivariate methods? (2012, methodology): “Sifting through all possible combinations of traits to identify the most responsive trait combination can also be done using RLQ (Bernhardt-Römermann et al. 2008).”