Evaluation of relative (allometric) growth provides useful information to understand the development of organisms, as well as to aid in the management of fishery-exploited species. Usually, relative growth analyses use classical models such as the linear equation or the power function (allometric equation). However, these methods do not consider discontinuities in growth and may mask important biological information. As an alternative to overcome poor results and misleading interpretations, recent studies have suggested the use of more complex models, such as non-linear regressions, in conjunction with a model selection approach. Here, we tested differences in the performance of diverse models (simple linear regression, power function, and polynomial models) to assess the relative growth of the trigonal clam Tivela mactroides, an important fishing resource along the South American coast. Regressions were employed to relate parameters of the shell (length (L), width (W), height (H) and weight (SW)) among each other and with soft parts of the organism (dry weight (DW) and ash-free dry weight (ASDW)). Then, model selection was performed using the information theory and multi-model inference approach. The power function was more suitable to describe the relationships involving shell parameters and soft parts weight parameters (i.e., L vs. SW, DW, and AFDW, and SW vs. DW). However, it failed in unveiling changes in the morphometric relationships between shell parameters (i.e., L vs. W and H; W vs. H) over time, which were better described by polynomial functions. Linear models, in turn, were not selected for any relationship. Overall, our results show that more complex models (in this study polynomial functions) can unveil changes in growth related to modifications in environmental features or physiology. Therefore, we suggest that classical and more complex models should be combined in future studies of allometric growth of molluscs.
Keywords: Akaike; Allometry; Clam; Growth; Model selection.