The relative importance of climate, stand variables and liana abundance for carbon storage in tropical forests

Publicado en Global Ecology and Biogeography, v. 24(8),:939-949 

Durán, S.M., Sánchez-Azofeifa, G.A., Rios, R.S. and Gianoli, E.

Año de publicación 2015

Earth and Atmospheric Sciences Department, University of Alberta, Edmonton, AB Canada T6G 2E9, Departamento de Biología, Universidad de la Serena, Casilla 554, La Serena, Chile, Departamento de Botánica, Universidad de Concepción, Casilla 160-C, Concepción, Chile




Proyecto CRN3025


  • Aim

To develop an integrative framework to evaluate variation in aboveground carbon storage (AGC). A model that can be applied to understand and predict how global‐change drivers influence tropical carbon sinks.

  • Location

Old‐growth tropical forests world‐wide.

  • Methods

Using structural equation modelling (SEM), we propose an a priori model to evaluate the direct and indirect effects of climate, stand variables (basal area, tree diameter and wood density at plot level) and liana abundance on AGC. Our model indicated that stand variables increased AGC while liana abundance decreased AGC indirectly via negative effects on stand variables. We used a multigroup SEM to test the generality of our framework using a standardized dataset of 145 plots (0.1 ha) in dry, moist and wet tropical forests.

  • Results

Our model explained over 85% variation in AGC and showed a positive and consistent relationship between stand variables and AGC across forests types. The effects of climate on AGC were indirect rather than direct, with negative effects of temperature in all forests. Liana abundance reduced tree diameter and basal area in moist forests, but did not affect AGC in wet or dry forests.

  • Main conclusions

Our results suggest that climate affects AGC indirectly, via its direct influence on stand variables and liana abundance. The effects of lianas on AGC result from reductions in stand variables and are as important as climate for moist forests, which harbour the greatest tropical carbon pools. Our model was consistent across forest types. This highlights the usefulness of an integrative framework to improve predictions of the effects of drivers of global change on tropical carbon sinks.