title: Why species tell more about traits than traits about species: predictive analysis.
abstract: Trait analysis aims to understand relationships between traits, species diversity, and the environment. Current methods could benefit from a model-based probabilistic framework that accommodates covariance between traits and quantifies contributions from inherent trait syndromes, species interactions, and responses to the environment. I develop a model-based approach that separates these effects on trait diversity. Application to USDA Forest Inventory and Analysis (FIA) data in the eastern United States demonstrates an apparent paradox, that the analysis of species better explains and predicts traits than does direct analysis of the traits themselves; trait data contain less, not more, information than species on environmental responses. Whereas variation in some traits is dominated by inherent syndromes (tendency for certain traits to be associated with others within an individual and species), others are strongly controlled by variation in species diversity. There is substantial variation in environmental control on trait patterns, between traits and regionally. In terms of environmental response traits do not aggregate into defined plant functional types, as would be desirable for models.
authors.affiliation: Nicholas School of the Environment, Duke University, Durham, North Carolina, 27709, USA.|Department of Statistical Science, Duke University, Durham, North Carolina, 27709, USA.
authors.forename: James S
authors.initials: JS
authors.lastname: Clark
mesh_terms: D044822:Biodiversity; D004463:Ecology; D065928:Forests; D010641:Phenotype; D010944:Plants
keywords: climate effects; forests; joint species distribution; predictive trait analysis; species diversity; trait hyper-volume