Abstract

Tree vigor is often used as a covariate when tree mortality is predicted from tree growth in tropical forest dynamic models, but it is rarely explicitly accounted for in a coherent modeling framework. We quantify tree vigor at the individual tree level, based on the difference between expected and observed growth. The available methods to join nonlinear tree growth and mortality processes are not commonly used by forest ecologists so that we develop an inference methodology based on an MCMC approach, allowing us to sample the parameters of the growth and mortality model according to their posterior distribution using the joint model likelihood. We apply our framework to a set of data on the 20-year dynamics of a forest in Paracou, French Guiana, taking advantage of functional trait-based growth and mortality models already developed independently. Our results showed that growth and mortality are intimately linked and that the vigor estimator is an essential predictor of mortality, highlighting that trees growing more than expected have a far lower probability of dying. Our joint model methodology is sufficiently generic to be used to join two longitudinal and punctual linked processes and thus may be applied to a wide range of growth and mortality models. In the context of global changes, such joint models are urgently needed in tropical forests to analyze, and then predict, the effects of the ongoing changes on the tree dynamics in hyperdiverse tropical forests.

Highlights

  • The biological processes responsible for tree mortality involve a combination of environmental stresses, but early warning signs can be detected by looking at the behavior of tree growth (Pedersen 1998; Dobbertin 2005)

  • Ecology and Evolution published by John Wiley & Sons Ltd

  • We present a flexible methodology for building a growth-mortality joint model using the tree vigor

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Summary

Introduction

The biological processes responsible for tree mortality involve a combination of environmental stresses, but early warning signs can be detected by looking at the behavior of tree growth (Pedersen 1998; Dobbertin 2005). Trees exhibiting the highest growth rates have a better chance to stay alive, while trees with lower than expected growth rates are more likely to die before their expected size at maturity (Chao et al 2008). This phenomenon is often called tree vigor, a good starting point from which to build coupled models of growth and mortality that explicitly take into account the biological link between these two processes.

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