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We examined sagittal otoliths from 398 gray angelfish (Pomacanthus arcuatus) collected from the lower Florida Keys between September 2000 and September 2003. Fish ranged in size from 78 to 442 mm total length (TL). Males had a mean TL of 329 mm (n = 192), females had a mean TL of 308 mm (n = 166), and an additional 56 fish were unsexed (mean TL = 239 mm). Sectioned otoliths displayed clear increments that were used to estimate fish ages. Marginal increment analysis validated the formation of a single annulus per year. The relationship between TL and age was described by the von Bertalanffy growth equation Lt = 325.1[1 − exp(−0.0601(t + 0.828))] for females and Lt = 388.5[1 − exp(−0.383(t + 0.923))] for males. Females and males grew rapidly for the first 5 years of life and eventually reached asymptotic lengths of ∼325 and ∼388 mm, respectively, with a maximum estimated age of 24 years. Morphometric parameters evaluated for use in the age model for gray angelfish included fish length and weight and otolith length, width, thickness, and weight. As with fish length, otolith length and width became asymptotic between the ages of 4 and 7. Otolith weight increased throughout the life of the fish, but the rate of increase slowed with age. Only otolith thickness was linear with fish age. Stepwise forward regression resulted in the following equation: ln (age + 1) = 1.157 + 2.542 × ln(otolith thickness) indicating that otolith thickness, which explained 89% of the variation, was the best predictor of age. Additional variables did not improve the regression, nor did dividing the data into subsets based on growth rate. Once the otolith thickness–age relationship was established, the simple process of measuring otolith thickness was as effective for determining the age of gray angelfish as the far more difficult process of sectioning and reading the otoliths. Use of similar models in ageing other species, along with periodic validation to ensure that the otolith parameter–age relationship has not changed over time, could simplify age data collection for population models. This, in turn, could potentially allow fisheries to be better managed at a significantly reduced cost. |
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