Physiological traits are frequently used as indicators of tree productivity. Aquilaria species growing in a research planting were studied to investigate relationships between leaf-productivity traits and tree growth. Twenty-eight trees were selected to measure isotopic composition of carbon (δ13C) and nitrogen (δ15N) and monitor six leaf attributes. Trees were sampled randomly within each of four diametric classes (at 150 mm above ground level) ensuring the variability in growth of the whole population was represented. A model averaging technique based on the Akaike's information criterion was computed to identify whether leaf traits could assist in diameter prediction. Regression analysis was performed to test for relationships between carbon isotope values and diameter and leaf traits. Approximately one new leaf per week was produced by a shoot. The rate of leaf expansion was estimated as 1.45 mm day−1. The range of δ13C values in leaves of Aquilaria species was from −25.5‰ to −31‰, with an average of −28.4 ‰ (±1.5‰ SD). A moderate negative correlation (R2 = 0.357) between diameter and δ13C in leaf dry matter indicated that individuals with high intercellular CO2 concentrations (low δ13C) and associated low water-use efficiency sustained rapid growth. Analysis of the 95% confidence of best-ranked regression models indicated that the predictors that could best explain growth in Aquilaria species were δ13C, δ15N, petiole length, number of new leaves produced per week and specific leaf area. The model constructed with these variables explained 55% (R2 = 0.55) of the variability in stem diameter. This demonstrates that leaf traits can assist in the early selection of high-productivity trees in Aquilaria species.