Revisiting the Holy Grail: using plant functional traits to understand ecological processes.

Journal Article (Review;Journal Article)

One of ecology's grand challenges is developing general rules to explain and predict highly complex systems. Understanding and predicting ecological processes from species' traits has been considered a 'Holy Grail' in ecology. Plant functional traits are increasingly being used to develop mechanistic models that can predict how ecological communities will respond to abiotic and biotic perturbations and how species will affect ecosystem function and services in a rapidly changing world; however, significant challenges remain. In this review, we highlight recent work and outstanding questions in three areas: (i) selecting relevant traits; (ii) describing intraspecific trait variation and incorporating this variation into models; and (iii) scaling trait data to community- and ecosystem-level processes. Over the past decade, there have been significant advances in the characterization of plant strategies based on traits and trait relationships, and the integration of traits into multivariate indices and models of community and ecosystem function. However, the utility of trait-based approaches in ecology will benefit from efforts that demonstrate how these traits and indices influence organismal, community, and ecosystem processes across vegetation types, which may be achieved through meta-analysis and enhancement of trait databases. Additionally, intraspecific trait variation and species interactions need to be incorporated into predictive models using tools such as Bayesian hierarchical modelling. Finally, existing models linking traits to community and ecosystem processes need to be empirically tested for their applicability to be realized.

Full Text

Duke Authors

Cited Authors

  • Funk, JL; Larson, JE; Ames, GM; Butterfield, BJ; Cavender-Bares, J; Firn, J; Laughlin, DC; Sutton-Grier, AE; Williams, L; Wright, J

Published Date

  • May 2017

Published In

Volume / Issue

  • 92 / 2

Start / End Page

  • 1156 - 1173

PubMed ID

  • 27103505

Electronic International Standard Serial Number (EISSN)

  • 1469-185X

International Standard Serial Number (ISSN)

  • 1464-7931

Digital Object Identifier (DOI)

  • 10.1111/brv.12275


  • eng