A multi‐trait approach reveals the structure and the relative importance of intra‐ vs. interspecific variability in plant traits

By Cécile H. Albert, Wilfried Thuiller, N. Yoccoz, R. Douzet, S. Aubert, S. Lavorel. 2010

552 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Evolution, Behavior and Systematics, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/33112ebbcede923ea0fb91f71fd5270ed081b27f

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In Ecological Modeling

In Ecology, Evolution, Behavior and Systematics

In Nature and Landscape Conservation

Citations in the corpus

Leaf dry matter content is better at predicting above-ground net primary production than specific leaf area (2017, background): “2009; Laughlin et al. 2012) often ending up as residual variance rather than predicting 433 local coupling between trait values and environmental (Albert et al. 2010; Jung et al. 2010).”
The n‐dimensional hypervolume (2014, background): “…& Jiang, 2009), for instance to quantify the multivariate space of a community or a regional pool (Ricklefs & O’Rourke, 1975; Foote, 1997), to measure morphology (Raup & Michelson, 1965) or to test functional ecology hypotheses (Albert et al., 2010; Baraloto et al., 2012; Boucher et al., 2013).”
Spatial scale and intraspecific trait variability mediate assembly rules in alpine grasslands (2017, background): “However, recent works question this assumption and show that ITV can significantly affect patterns of community functional diversity (Albert et al. 2010, 2012; Siefert 2012).”
New handbook for standardised measurement of plant functional traits worldwide (2013, methodology): “Other more powerful techniques can also be used, such as mixed models (Albert et al. 2010; Messier et al. 2010; Moreira et al. 2012).”
Functional traits of individual trees reveal ecological constraints on community assembly in tropical rain forests (2011, result): “For studies of community assembly, however, we concur with Albert et al. (2010a), that individual-level data is most appropriate. Even in ecosystems such as grasslands where individual genets may be diffi cult to distinguish, it may be possible to weight species-mean trait values by their relative a”
Climate and soil nutrients differentially drive multidimensional fine root traits in ectomycorrhizal‐dominated alpine coniferous forests (2020, methodology): “…we performed a resampling procedure to generate balanced data subsets concerning fine root trait values of 29 species (i.e. with an equal number of populations [n = 1] per species; Albert et al., 2010), and then PCAs and Pearson's correlation analyses were run again over the balanced data subset.”
Do plant traits retrieved from a database accurately predict on‐site measurements? (2013, background): “For the leaf trait LDMC, showing a relatively low variability within species (Garnier et al. 2001; Roche, Diaz-Burlinson & Gachet 2004; Albert et al. 2010b), database values seem to be better proxies than for CH and SLA, which also follows from the ranking of these traits by standard deviation in…”