The relative suitability of the von Bertalanffy, Gompertz and inverse logistic models for describing growth in blacklip abalone populations (Haliotis rubra) in Tasmania, Australia

Abstract

Three candidate, non-nested, growth models (von Bertalanffy, Gompertz and inverse logistic) were fitted to multiple samples of tag-recapture data (n = 27 samples) to determine the best statistical model for blacklip abalone (Haliotis rubra) populations in Tasmania, Australia. Wild populations of blacklip abalone were sampled for growth data using tag-recapture methods. The best statistical model was identified for each sample using Akaike’s Information Criteria and Akaike weights to measure the relative statistical fit. Using these criteria, the best fitting model was the inverse logistic for 21 of the 27 samples, both the von Bertalanffy and the Gompertz models were the best fitting model in three samples each. When the inverse logistic was the best fitting model it was the best unambiguously, as indicated by the high Akaike weight values (generally wi > 0.8; 0.65–1.0). In contrast, when either the von Bertalanffy or the Gompertz growth models were statistically optimal, the highest Akaike weights ranged between 0.15 and 0.44 across both models. We conclude that the use of either the von Bertalanffy or Gompertz growth models in the assessment of Tasmanian blacklip abalone would be statistically sub-optimal and may mislead assessments of Tasmanian abalone stocks. The inverse logistic model can be considered as a good candidate growth model for other fished invertebrate stocks