ABSTRACTAim An important issue regarding biodiversity concerns its influence on ecosystem functioning. Experimental work has led to the proposal of mechanisms such as niche complementarity. However, few attempts have been made to confirm these in natural systems, especially in forests. Furthermore, one of the most interesting unresolved questions is whether the effects of complementarity on ecosystem functioning (EF) decrease in favour of competitive exclusions over an increasing productivity gradient. Using records from permanent forest plots, we asked the following questions. (1) Is tree productivity positively related to diversity? (2) Does the effect of diversity increase in less productive forests? (3) What metric of diversity (e.g. functional or phylogenetic diversity) better relates to tree productivity?
Location Temperate, mixed and boreal forests of eastern Canada.
Methods Over 12,000 permanent forest plots, from temperate to boreal forests, were used to test our hypotheses in two steps. (1) Stepwise regressions were used to identify the best explanatory variables for tree productivity. (2) The selected climatic and environmental variables, as well as density and biodiversity indices, were included in a structural equation model where links (paths) between covarying variables are made explicit, making structural equation modelling the best tool to explore such complicated causal networks.
Results This is the first large‐scale demonstration of a strong, positive and significant effect of biodiversity on tree productivity with control for climatic and environmental conditions. Important differences were noted between the two forest biomes investigated.
Main conclusions We show for the first time that complementarity may be less important in temperate forests growing in a more stable and productive environment where competitive exclusion is the most probable outcome of species interactions, whereas in the more stressful environment of boreal forests, beneficial interactions between species may be more important. The present work is also a framework for the analysis of large datasets in biodiversity–ecosystem functioning (B‐EF) research.
