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

Tree death drives population dynamics, nutrient cycling, and evolution within plant communities. 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 a...

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Bibliographic Details
Published in:Proceedings of the National Academy of Sciences - PNAS 2018-12, Vol.115 (49), p.12459-12464
Main Authors: Camac, James S., Condit, Richard, FitzJohn, Richard G., McCalman, Lachlan, Steinberg, Daniel, Westoby, Mark, Wright, S. Joseph, Falster, Daniel S.
Format: Article
Language:English
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Summary:Tree death drives population dynamics, nutrient cycling, and evolution within plant communities. 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 growthindependent 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. 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.
ISSN:0027-8424
1091-6490
DOI:10.1073/pnas.1721040115