OOS 31-2 - Partitioning mortality into growth-dependent and growth-independent hazards across 203 tropical tree species

Friday, August 16, 2019: 8:20 AM
M103, Kentucky International Convention Center

ABSTRACT WITHDRAWN

Daniel S. Falster, Evolution & Ecology Research Centre, University of New South Wales, Australia, James Camac, University of Melbourne, Australia, Richard Condit, Smithsonian Tropical Research Institute, Panamá City, Panama, Mark Westoby, Macquarie University and S Joseph Wright, Smithsonian Tropical Research Institute, Panama, Panama
Daniel S. Falster, University of New South Wales; James Camac, University of Melbourne; Richard Condit, Smithsonian Tropical Research Institute; Mark Westoby, Macquarie University; S Joseph Wright, Smithsonian Tropical Research Institute

Background/Question/Methods:

Mortality variation across species is thought to be influenced by different factors relative to variation within species. The unified model provided here separates mortality rates into growth-dependent and growth- independent hazards. This model creates the opportunity to simultaneously estimate these hazards both across and within species. Moreover, it provides the ability to examine how species traits affect growth-dependent and growth-independent hazards. We derive this unified mortality model using cross-validated Bayesian methods coupled with mortality data collected over three census intervals for 203 tropical rainforest tree species at Barro Colorado Island (BCI), Panama.

Results/Conclusions:

We found that growth- independent mortality tended to be higher in species with lower wood density, higher light requirements, and smaller maximum diameter at breast height (dbh). Mortality due to marginal carbon budget as measured by near-zero growth rate tended to be higher in species with lower wood density and higher light demand. The total mortality variation attributable to differences among species was large relative to variation explained by these traits, emphasizing that much remains to be understood. This additive hazards model strengthens our capacity to parse and understand individual-level mortality in highly diverse tropical forests and hence to predict its consequences.