Abstract

Age-structured stock assessment models for data rich species like Gulf of Mexico gray snapper, Lutjanus griseus, require significant time commitments to process and age samples. Next generation ageing methods, such as Fourier-transform near infrared spectroscopy provide a means to drastically reduce the time required for production ageing because only a relatively small portion of samples would require sectioning and enumerating annuli. The majority of samples would be processed via near infrared scans of whole otoliths, which require <1 minute per otolith, and ages are estimated with species-specific predictive models. We used the Stock Synthesis assessment modeling framework to evaluate the sensitivity of gray snapper assessment model parameters to age data inputs estimated from models derived with FT-NIRS-predicted ages. Assessment outputs were insensitive to FT-NIRS-predicted ages but sample sizes were very low. Greater sample sizes or resampling schemes are necessary to better evaluate the effects of imprecision on simulated assessment outputs.

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