Plant size and resprouting ability: trading tolerance and avoidance of damage?
Article first published online: 29 JUN 2006
Journal of Ecology
Volume 94, Issue 5, pages 1027–1034, September 2006
How to Cite
VESK, P. A. (2006), Plant size and resprouting ability: trading tolerance and avoidance of damage?. Journal of Ecology, 94: 1027–1034. doi: 10.1111/j.1365-2745.2006.01154.x
- Issue published online: 29 JUN 2006
- Article first published online: 29 JUN 2006
- Received 23 February 2005 revision accepted 10 May 2006 Handling Editor: Peter Bellingham
- Bayesian statistics;
- damage avoidance;
- damage tolerance;
- disturbance response;
- ecological strategies;
- growth form;
- plant functional types;
- research synthesis;
- vegetation dynamics
- 1Resprouting has been much studied in mature plants and seedlings, but with little attention to how resprouting ability may change with ontogeny in cross-species datasets. Because damage exposure and avoidance through large size may differ between growth forms, size-stage, growth form and disturbance type may interact.
- 2A Bayesian mixed-effects model was used to analyse a wide-ranging literature dataset of resprouting responses following clipping or burning. There were distinct ontogenetic patterns of resprouting ability between growth forms. In ground-dwelling plants, seedling resprouting ability was high and was maintained as plants grew. In trees, survivorship increased with size-stage. However, for disturbances resulting in stem-kill, resprouting ability of trees declined with increasing size.
- 3The finding of distinct ontogenetic patterns of sprouting ability between growth forms demonstrates how species trade-off damage avoidance with resprouting ability to achieve persistence, and provides an example of the life-history trade-off of avoidance and tolerance of damage.
- 4Large random effects associated with between-species variation indicated that although such ontogenetic patterns can be detected, each growth form contains species employing diverse strategies. Bayesian modelling enables flexible and powerful analyses of literature datasets, to reveal diverse strategies often obscured by classical analyses exploring only main effects.