Life history traits predict the response to increased light among 33 tropical rainforest tree species

Rainforest restoration is an important application in today’s multipurpose management of secondary forest. However, our knowledge of tree species’ traits and responses to treatment is insufficient for foresters to make good decisions for sustainable management. The aim of our study was to see whether it is possible to predict tree species’ responses to increased light based on species’ traits, and to relate these responses to a possible pioneer–climax continuum of life history traits, also among species with presumed climax properties. We examined 33 taxa (including 19 from the dipterocarp family) replicated 20 times and randomly planted in lines over a 3ha area in the interior of Sabah, Borneo. Four years after establishment we performed a canopy reduction treatment to increase the light conditions up to levels present in tree gaps in the forest. We created a PLS (Partial Least Square Regressions) model with the two predicted variables HGR (height growth response) and Q3 HGR (the 75 percentile of a species’ HGR, interpreted as the potential HGR). The model captured 47% of the variation for the predicted variables. We found significant tree species’ responses in height growth to the increased light. High specific leaf area, strong early height growth, high foliar N content, high leaved stem length and large crown were linked to fast growth, while high wood density and high foliar K content were associated with slow growth. We also found a trade-off between growth response and survival among the species. We conclude that climax tree species have specific life history adaptations along a pioneer–climax continuum, which can be predicted from species’ traits. The importance of easily observed or extracted traits such as initial growth rate, specific leaf area and wood density for predicting growth suggests the possibility of fast screening of species with unknown characteristics, which could be of great value in practical forest management.