Estimating abundance of pelagic fishes using gillnet catch data in data-limited fisheries: a Bayesian approach

Abstract

We describe a Bayesian modelling approach to estimate abundance and biomass of pelagic fishes from gillnet catches in data-limited situations. By making a number of simple assumptions, we use fish sustained swimming speed to calculate the effective area fished by a gillnet in a specified soak time to estimate abundance (fish·km–2) from the number of fish caught. We used catch data from various sampling methods in northern Australia and elicited anecdotal information from experts to build a size distribution of the true population to compensate for size classes that were unlikely to be represented in the catch because of size selectivity of the gear. Our final abundance estimates for various-sized scombrids (0.04–4.17 fish·km–2) and istiophorids (0.004–0.005 fish·km–2) were similar to what has been estimated for similar species in more data-rich situations in tropical regions of the Pacific Ocean. The model is particularly useful in data-limited situations in which abundance or biomass estimates are required for pelagic fish species of low economic importance. These data are often required for ecosystem models (e.g., Ecopath) that are increasingly being considered as potential tools for ecosystem-based fisheries management.