A heuristic classification of woody plants based on contrasting shade and drought strategies. (31st January 2019)

• Record Type: Journal Article
• Authors: Wei, Liang; Xu, Chonggang; Jansen, Steven; Zhou, Hang; Christoffersen, Bradley O; Pockman, William T; Middleton, Richard S; Marshall, John D; McDowell, Nate G
• Editors: Whitehead, David
• Is Part Of: Tree physiology. Volume 39:Number 5(2019)
• Page Start: 767
• Page End: 781
• Publication Date: 2019-01-31
• Subjects: drought responses -- embolism resistance -- plant functional traits -- shade tolerance -- wood density; Trees -- Physiology -- Periodicals; 582.16
• DOI: 10.1093/treephys/tpy146
• Languages: English
• ISSNs: 0829-318X

Abstract

Abstract: Woody plants vary in their adaptations to drought and shade. For a better prediction of vegetation responses to drought and shade within dynamic global vegetation models, it is critical to group species into functional types with similar adaptations. One of the key challenges is that the adaptations are generally determined by a large number of plant traits that may not be available for a large number of species. In this study, we present two heuristic woody plant groups that were separated using cluster analysis in a three-dimensional trait–environment space based on three key metrics for each species: mean xylem embolism resistance, shade tolerance and habitat aridity. The two heuristic groups separate these species into tolerators and avoiders. The tolerators either rely on their high embolism resistance to tolerate drought in arid habitats (e.g., Juniperus and Prunus ) or rely on high shade tolerance to withstand shaded conditions in wet habitats (e.g., Picea, Abies and Acer ). In contrast, all avoiders have low embolism resistance and low shade tolerance. In arid habitats, avoiders tend to minimize catastrophic embolism (e.g., most Pinus species) while in wet habitats, they may survive despite low shade tolerance (e.g., Betula, Populus, Alnus and Salix ). Because our approach links traits to the environmental conditions, we expect it could be a promising framework for predicting changes in species composition, and therefore ecosystem function, under changing environmental conditions.