When fitting models to data, the usual goal is to estimate parameters such that modeled dynamics match observations. In practice, estimates are always uncertain, due to stochas-ticity in population dynamics and error in fishery data. Quantifying this uncertainty has motivated extensions of each model described above to include various versions of process error, observation error, or both. As in other fields, Bayesian methods are also gaining popularity. In many cases, estimates have been found more precise when normalized to their respective biological reference point, for example, B,/Bmsy and F,/Fmsy are usually more precise than the corresponding absolute estimates, B, and Ft.
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