Otolith weight as a predictor of age in Sillaginopsis panijus (Hamilton, 1822): Development of a preliminary age estimation model

Reliable age estimation is crucial for evaluating fish population dynamics and managing wild fish stocks. However, age determination in Sillaginopsis panijus remains limited due to the absence of validated age-length data and challenges associated with ring-counting methods. This study evaluated whether otolith weight (OWt) can be used to estimate the age of S. panijus and developed regression-based predictive models using assumptive age distributions derived from the von Bertalanffy Growth Function (VBGF). Thirty individuals were collected from fish markets in Dhaka, Bangladesh, and their biometric traits (total length, standard length, and body weight) were compared with otolith dimensions (length, width, and weight). Otolith weight exhibited the strongest relationship with total length (R 2 = 0.8266), supporting its use as a proxy for age. Three model types (linear, power, and exponential) were evaluated to estimate age from OWt. The linear model (Age = 0.79 + 27.78 × OWt) provided the best fit (R 2 = 0.829), followed by the power (R 2 = 0.823) and exponential models (R 2 = 0.816). These findings demonstrate the utility of otolith weight-based age estimation for S. panijus and provide a rapid, non-destructive approach for preliminary stock assessment in data-poor fisheries. Nonetheless, the applicability of the suggested model is presently confined to the observed size and age range, necessitating further confirmation through direct otolith aging over more extensive cohorts. • Otolith weight showed the strongest correlation with fish size traits in Sillaginopsis panijus. • Assumptive ages were derived using the von Bertalanffy Growth Function due to missing validated age data. • A linear model (Age = 0.79 + 27.78 × OWt) provided the best fit for estimating age from otolith weight ( R 2 = 0.829). • Otolith weight offers a rapid, non-destructive proxy for preliminary age estimation in data-limited fisheries. • The model supports early-stage stock assessment and highlights the need for future validation using direct ring counts.