Multi-model inference as criterion to determine differences in growth patterns of distinct Crassostrea gigas stocks

Production of Crassostrea gigas in hatcheries may be affected by different factors influencing spat quality; this will be reflected during its cultivation in the field. The main indicator of quality is growth. Growth modeling is a form of determining individual growth patterns in bivalves. In this study, multi-model inference (MMI) and the Akaike information criterion (AIC) were employed to identify differences in growth patterns of distinct C. gigas stocks. The experiment used spat produced in four different hatcheries (A, B, C, and D), which were cultivated under identical conditions. The stocks showed similar growth patterns but the best growth models to describe every case were different; hatchery A-von Bertalanffy (AIC = - 15.27), hatchery B-Schnute model case 3 (AIC = - 0.46), and hatcheries C and D-Schnute model case 1 (AIC = 233.4 and - 73.3, respectively). According to the models, oysters from hatchery B did not reach their maximum growth while the rest did it. Differences may be attributed to stock origin while the spat quality seems associated with production protocols. Results showed that growth patterns of C. gigas can be variable under the same cultivation conditions but the differences are difficult to detect. We demonstrated that the only way to find such differences was via MMI, and this approach should be used for any aquaculture resource.