Understanding how functional traits and diversity modulate plant interactions within forests is becoming a widespread research goal in ecology. We applied neighbourhood analysis to a Mediterranean biodiversity manipulation experiment (IDENT-Macomer) to assess the importance of functional traits in predicting tree diameter increments (DI) in a mixed forest. We used tree functional traits to weigh the neighbourhood competition index (NCI) and functional dispersion (FDis), which is a functional diversity metric. We found that functional traits affect Maximum height influence DI competitive performance across species within a mixed forest and that resource acquisition is based primarily on trait hierarchy. We also found that traits related to competition for light, such as maximum plant height (Hmax), are the best predictors of DI. Our results reveal that NCI is a more reliable predictor than FDis, but the combination of both effects helps to better explain differences in DI. Finally, our findings show that gathering functional trait data is a practise that should be prioritised in mixed forest management due to the predictive importance of NCI and FDis in experiments with high density and species diversity.

Understanding how functional traits and diversity modulate plant interactions within forests is becoming a widespread research goal in ecology. We applied neighbourhood analysis to a Mediterranean biodiversity manipulation experiment (IDENT-Macomer) to assess the importance of functional traits in predicting tree diameter increments (DI) in a mixed forest. We used tree functional traits to weigh the neighbourhood competition index (NCI) and functional dispersion (FDis), which is a functional diversity metric. We found that functional traits affect competitive performance across species within a mixed forest and that resource acquisition is based primarily on trait hierarchy. We also found that traits related to competition for light, such as maximum plant height (Hmax), are the best predictors of DI. Our results reveal that NCI is a more reliable predictor than FDis, but the combination of both effects helps to better explain differences in DI. Finally, our findings show that gathering functional trait data is a practise that should be prioritised in mixed forest management due to the predictive importance of NCI and FDis in experiments with high density and species diversity.