Testing for functional significance of traits: Effect of the light environment in tropical tree saplings

1. Functional traits have been examined to explain the growth rates of forest communities in different sites. However, weak or nonexistent relations are often found, especially due to the following methodological aspects: 1) lack of an environmental context (e.g., light, water, or nutrient supply), 2) use of nonfunctional traits, 3) an approach that does not contemplate phenotypic integration, and 4) neglect of intraspecific variation.
2. Here we measured relative growth rates, crown, and leaf traits in saplings of six tropical tree species growing in two light environments (Gap and Understory) to test whether contrasting light environments modulates trait–trait and trait–growth relationships. Moreover, we tested whether models that integrate traits of different dimensions of the plant (crown and leaf) improve the strength of trait–growth relations.
3. Light availability changed both trait–trait and trait–growth relationships. Overall, in Understory, crown traits (crown length and total leaf area) have a stronger effect on growth rates, while physiological traits related to nutrient acquisition (nitrogen concentration), photochemical efficiency (chlorophyll pigments and chlorophyll a fluorescence), and biochemical efficiency (potassium use efficiency) are strong in Gap. Models including multiple traits explained growth rates better in Gap (up to 62%) and Understory (up to 47%), but just in Gap the best model comprises traits that are representative of different dimensions of the plant.
4. Synthesis. We advanced the knowledge behind the light effects on tree sapling by posit that trait–trait and trait–growth relationships vary across light environments. Therefore, light availability is a key environmental factor to be considered when choosing the set of traits to be measured in functional approach studies using tropical tree saplings. In compliance with the phenotype integration hypothesis, functional traits are better predictors of growth rates when grouped in a set of traits of different dimensions of the plant that represent different functional mechanisms.