Is there an easy method to reduce bias in fish growth parameter estimates resulting from size-selective sampling?


Describing the growth of fish is important for population modeling, particularly when evaluating policy options for fisheries management.  The most common way to describe the growth of fish is with a von Bertalanffy growth curve, but estimates of growth parameters can be bias when specimens are collected with size-selective sampling gears.  Age-length samples have been corrected for size selective sampling, but these methods typically required multiple years of data, tagging information, or independent estimates fishgrowth

of size selectivity of the sampling gears and are, therefore, not feasible for some studies.  A few years ago I was discussing with some colleagues the variety of ways that researchers have modified the fitting of von Bertalanffy growth models and we devised a study that would investigate if any of these methods were more robust to size-selective sampling than others. The methods we evaluated were, 1) fixing to at zero, 2) deleting data associated with ages that are not fully vulnerable to sampling, 3) deleting less than fully vulnerable ages and fixing to at zero, and 4) fixing L at the maximum value observed in the data.  Ultimately we found that none of these easy fitting modifications  were robust to size selective sampling across different shapes of size selectivity (e.g. asymptotic, dome shaped).  However some procedures performed better under certain selectivity shapes.  The following is the paper we wrote about this.

Full text pdf can be downloaded by clicking here.

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