3-3). The R2 values for significant relationships ranged from 0.161 to 0.659, with the best fits achieved by regressing SGRbm and SGRI against [RNA]iiver. Stepwise multiple linear regression analyses for each individual tissue yielded a series of nine significant growth models (Table 3-4). SGRbm was the dependent variable for five models, with two models (1-2) based on liver, one model (3) based on heart, and two models (4-5) based on blood. SGRcI was the dependent variable for the final four models, with one model (6) based on liver, one model (7) based on heart, and two models (8-9) based on blood. The significant independent variables predicting growth rate in each of these equations are listed in the table in the order in which they were selected by the models. When condition index and all biochemical indices for all tissues were combined and analyzed using stepwise multiple linear regression, the resulting models were identical to models 1 and 2 (for SGRbm) and model 6 (for SGRci). The growth equation that best estimated recent growth rate was Model 2. Despite the strong coefficient of determination for several SGR models, coefficients of variation for RNA and DNA concentrations in liver, heart, and blood and for protein concentration in liver (Table 3-5) were fairly substantial, indicating a high degree of interassay variation. Discussion The purpose of this study was to evaluate the use of morphometric and biochemical indices for predicting recent growth rates in juvenile green turtles. Validation of assays with substantial predictive power for estimating growth would provide a less intensive alternative to tag and recapture programs and facilitate population monitoring in this endangered species. Nucleic acid concentrations and ratios hold promise as potential biomarkers of recent growth, as RNA content of tissues increases with feeding and growth in many marine organisms including krill (Shin et al. 2003), cephalopods (Melzner et al. 2005, Vidal et al. 2006), tuna (Carter et al. 1998),