Growth responses of neotropical trees to logging gaps.

Published online
04 Aug 2010
Content type
Journal article
Journal title
Journal of Applied Ecology

Herault, B. & Ouallet, J. & Blanc, L. & Wagner, F. & Baraloto, C.
Contact email(s)

Publication language
French Guiana


Modelling growth strategies among tropical trees is an important objective in predicting the response of tree dynamics to selective logging and in gaining insights into the ecological processes that structure tree communities in managed tropical forests. We developed a disturbance index to model the effects of distance to and area of logging gaps on stem radial growth rates. This index was tested using census data of 43 neotropical tree species, representing a variety of life-history strategies and developmental stages, from a selectively logged forest at Paracou, French Guiana. Growth strategies were analyzed in light of two indicators: the inherent species growth rate (when disturbance index is null) and the species reaction (change in growth rate) to logging gaps. Across species, the predicted inherent growth rates in unlogged forest ranged from 0.25 to 6.47 mm year-1, with an average growth of 2.29 mm year-1. Ontogenetic shifts in inherent growth rate were found in 26 of the 43 species. Species growth response to logging gaps varied widely among species but was significantly positive for 27 species. The effect of ontogeny on growth response to logging was retained for 14 species, and species with inherent fast growth rate (5 mm year-1) responded less to logging gap disturbances than did species with slow inherent growth (1 mm year-1). Functional traits explained 19-42% of the variation in the inherent growth rate and in species' response across all developmental stages. Whereas maximum diameters and seed mass were strong predictors of inherent growth rate, maximum height, wood density, mode of germination and stem architecture were additionally involved in tree growth response. Synthesis and applications: This study provides a necessary framework for developing predictive post-logging growth models for the thousands of species comprising tropical forests and is sufficiently general to apply to a broad range of managed tropical forests.

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