Integrated age and length structured stock assessment models require age and/or length composition data from catch or surveys to inform selectivity ogives and year class strengths. We develop model-based approaches using categorical and ordinal models that can include spatial and temporal variability and help account for missing data and/or non-representative data to generate estimates of age and length compositions. The results show that these approaches can improve the estimates of age compositions when compared with those using empirical methods.
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