Statistical model of variable allometric growth: otolith growth in Micropogonias furnieri(Actinopterygii, Sciaenidae)

Abstract

The main objective of this study was to develop a statistical model for accurate estimates of relative growth. The method was based on identifying patterns of the residuals obtained from the Huxley's allometric equation. Three different approaches were applied: (1) growth with variable proportionality and constant allometry coefficient, (2) growth with constant proportionality and variable allometry coefficient and (3) distinct growth phases in which proportionality and allometry coefficients remained constant. The proposed statistical models were applied to the relationship of the otolith size and fish size of whitemouth croaker Micropogonias furnieri. The best fit was obtained when using approach (3). A change in the growth parameters was associated with the attainment of sexual maturity.

Age structure and growth of the rough scad, Trachurus lathami (Teleostei: Carangidae), in the Southeastern Brazilian Bight

“…Namely, OL, OH and OW in relation to TL, respectively, showed similar values. In the present results, the allometric models and residual analysis did not show noticeable changes in the pattern of otolith development, indicating a single-phase growth (Bervian et al 2006). Previously, it was evidenced a change in otolith thickness (two phases of otolith development) close to 130 mm TL (Saccardo and Katsuragawa 1995), due to the first gonadal maturation (Saccardo et al 2005), which was not evidenced here probably due to the few numbers of individuals between 100-140 mm TL.…”

Growth of the silverside Atherinella brasiliensis in a subtropical estuary with some insights concerning the weight-length relationship

“…By applying a polyphasic model according to Bervian et al (2006) the growth of A. brasiliensis could be divided into two stanzas (Fig 3a) After adjusting the polyphasic model, no residual pattern could be identified ( Fig. 3b; R²=0.00002), indicating that the polyphasic growth model accounted for all of the potentially explained weight variance.…”

“…To avoid the bias of log-transformation (Smith 1993), function adjustment was made with untransformed data by using the Solver tool of Microsoft Excel 10.0 (non linear GRG). To identify trends concerning polyphasic growth (Bervian et al 2006) the data set was grouped into size intervals (log scale, 0.0339 increment), where each size class was represented by mean length and mean weight of all selected individuals. This procedure had two main objectives: (1) to cluster several animals within a single information point, aiming to reduce the natural variability that can hide subtle trends, and (2) by using narrower size classes for small and abundant juveniles, and by increasing the size interval (on a log scale) for larger fishes to account for their lower abundance in samples, thus minimizing (but not eliminating) bias from the unbalanced sampling design.…”

Morphometry and relative growth of the Brazilian sardine, Sardinella brasiliensis (Steindachner, 1879) in the southeastern Brazilian bight

“…Subsequently, several studies have used morphometry and relative growth to describe otoliths and fish development (Yamaguti, 1979;Casselman, 1990;Hunt, 1992;Lombarte & Lleonart, 1993;Lidster et al, 1994;Kerstan, 2000;Volpedo & Echeverría, 2001;Aguirre, 2003;Strelcheck et al, 2003;Tuset et al, 2003;Monteiro et al, 2005;Megalofonou, 2006;Gomiero & Braga, 2007;Vaz-dos-Santos et al, 2007;Wilson et al, 2009;Lorenzo et al, 2011). Bervian et al (2006) proposed alternatives to Huxley's model for describing relative growth based on otoliths, showing the importance of this tool applied to fishes. Kristoffersen (2007) reviewed the theme and the growth-rate effect related to von Bertalanffy estimates, showing the importance of a well-studied otolith-fish measurements relationships.…”