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Fish and wildlife often exhibit an aggregated distribution pattern, whereas local abundance changes constantly due to movement. Estimating population density or size and survey detectability (i.e. gear efficiency in a fishery) for such elusive species is technically challenging. We extend abundance and detectability (N-mixture) methods to deal with this difficult situation, particularly for application to fish populations where gear efficiency is almost never equal to one. The method involves a mixture of statistical models (negative binomial, Poisson, and binomial functions) at two spatial scales: between-cell and within-cell. The innovation in this approach is to use more than one fishing gear with different efficiencies to simultaneously catch (sample) the same population in each cell at the same time-step. We carried out computer simulations on a range of scenarios and estimated the relevant parameters using a Bayesian technique. We then applied the method to a demersal fish species, tiger flathead, to demonstrate its utility. Simulation results indicated that the models can disentangle the confounding parameters in gear efficiency and abundance, and the accuracy generally increases as sample size increases. A joint negative binomial–Poisson model using multiple gears gives the best fit to tiger flathead catch data, while a single gear yields unrealistic results. This cross-sampling method can evaluate gear efficiency cost effectively using existing fishery catch data or survey data. More importantly, it provides a means for estimating gear efficiency for gear types (e.g. gillnets, traps, hook and line, etc.) that are extremely difficult to study using field experiments.

Modelling multiple fishing gear efficiencies and abundance for aggregated populations using fishery or survey data