The relative suitability of the von Bertalanffy, Gompertz and inverse logistic models for describing growth in blacklip abalone populations (Haliotis rubra) in Tasmania, Australia
2011
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
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