From Plant Traits to Vegetation Structure: Chance and Selection in the Assembly of Ecological Communities

By Bill Shipley. 2009

173 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ff083c54e29fb75a71cec4cdd1321dcf08fdf6d6

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Citations in the corpus

A physiological analogy of the niche for projecting the potential distribution of plants (2012, methodology): “The TTR’s process-based method of assimilating the effects of environmental forcing factors on physiological processes thus effectively reduces the dimensionality of the species distribution model (Shipley, 2010; Kissling et al., 2012).”
The distribution of below‐ground traits is explained by intrinsic species differences and intraspecific plasticity in response to root neighbours (2013, background): “At the community level, community-aggregated traits (i.e. trait values averaged across individuals or species in a community, weighted by abundance; Shipley 2010) reflect major environmental constraints on plant traits.”
The intrinsic dimensionality of plant traits and its relevance to community assembly (2014, background): “Quantifying the variation of functional traits among and within species increases our capacity to understand ecosystem processes and community assembly (Lavorel & Garnier 2002; McGill et al. 2006; Westoby & Wright 2006; Suding et al. 2008; Shipley 2010).”
Community assembly, coexistence and the environmental filtering metaphor (2015, background): “Whittaker 1960) and functional identity (e.g. Cornwell & Ackerly 2009; Shipley 2009; Swenson et al. 2012) of species across environmental gradients, another set of patterns that are often interpreted as evidence for environmental filtering.”
Which plant traits determine abundance under long‐term shifts in soil resource availability and grazing intensity? (2012, methodology): “We also used more uninformative as well as more informative priors (Shipley 2010a).”
Climatic constraints on trait‐based forest assembly (2011, methodology): “This approach is also more general than classic species distribution modelling (Guisan & Zimmermann 2000) because it considers simultaneously the probability of occurrence of every species in the regional pool, not just the distribution of species that actually occur at a site (Shipley 2010b).”
Selecting traits that explain species–environment relationships: a generalized linear mixed model approach (2013, unknown): “Count andmultinomial data can be used as well, as we show in Appendix S1 in Supporting Information, and provide more information on the relative fitness of the species (Shipley 2010).”