LIME: a new model for assessing the status of data-poor fisheries

Formal stock assessments are conducted for many large and valuable fisheries, but these typically require reliable catch data, estimates of trends in fish numbers, and age data from caught fish. In data-poor fisheries, these kinds of data are not available, resulting in difficulties in assessing whether they are overfished or sustainably fished. Now a new model called LIME has been developed that accounts for variability in recruitment (the number of baby fish produced each year), and can assess status from samples of the lengths of fish in each year, together with whatever additional information is available. Applying LIME to simulated data (representing “truth”) shows that it can estimate fishery status, especially for short-lived species, provided good information is available for growth rate parameters, and there are multiple years of length data. The LIME model was developed as part of Merrill Rudd’s PhD dissertation at SAFS, together with James Thorson of NOAA, and is published in the Canadian Journal of Aquatic and Fishery Sciences.

Performance of the LIME model under different conditions where model parameters are too low, just right, and too high (columns); for species that are short-lived, medium-lived, or long-lived (rows); and for two variants of catch time series (red, blue). Relative errors close to zero indicate the model has good performance for that scenario.
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