Growth of Abalone (Haliotis Rubra) with Implications for Its Productivity

Growth of abalone ( Haliotis rubra ) with implications for its productivity by Fay Helidoniotis, BSc (Hons) Submitted in fulfilment of the requirements for the Degree of Doctor of Philosophy in Quantitative Marine Science Institute for Marine and Antarctic Studies University of Tasmania July 2011 Statement of Originality This thesis contains no material which has been accepted for a degree or diploma by the University or any other institution. To the best of my knowledge and belief, this thesis contains no material published or written by another person except where due acknowledgement is made in the text of the thesis. Fay Helidoniotis _____________________________ Date __________________ Authority of Access Statement This thesis may be available for loan and limited copying in accordance with the Copyright Act 1968 Fay Helidoniotis _____________________________ Date __________________ i 1. STATEMENT OF CO-AUTHORSHIP The following people and institutions contributed to the publication of the work undertaken as part of this thesis: Chapter 2: Dr M. Haddon (CSIRO) supervised the research in this chapter and provided significant intellectual input and technical advice on statistical analyses and research design. Prof. C. Johnson (UTAS) provided intellectual input and discussion on the content. Chapter 3: Dr M. Haddon suggested the approach of cross model validation in addition to co-supervising and providing intellectual input and discussion. Prof. C. Johnson provided co-supervision, intellectual input and discussion that significantly improved the Chapter. Dr G. Tuck (CSIRO) co-supervised and provided a significant contribution into the intellectual input and discussion on the interpretation of results. Chapter 4: Dr M. Haddon provided statistical advice. Dr G. Tuck provided intellectual input and discussion of the content. Mr D. Tarbath (UTAS - Tasmanian Aquaculture and Fisheries Institute) is a collaborator and provided confirmation of the accuracy in the description of part of the methods. Chapter 5: Prof. C. Johnson co-supervised the research and provided significant intellectual input and technical advice on statistical analyses. Dr M. Haddon provided statistical advice and suggested significant improvements on the presentation of results. Dr G. Tuck co-supervised the research and provided intellectual input and discussion. Satellite SST data was obtained from the Remote Sensing Facility at CSIRO and Mr F. Rizwi significantly contributed to the programming used to obtain the data. Chapter 6: Dr G. Tuck conceived part of the research reported and significantly contributed to the interpretation of results. Dr M. Haddon provided the R script for the size transition matrix, as well as statistical direction and intellectual input. We the undersigned agree with the above stated “proportion of work undertaken” for each of the above published (or submitted) peer-reviewed manuscripts contributing to this thesis: Prof. Craig Johnson _____________________________________ Dr Geoff Tuck _____________________________________ Dr Malcolm Haddon _____________________________________ Date: _____________________ ii ABSTRACT The use of an incorrect growth model in fisheries management may lead to inaccurate predictions about stock productivity. In Australia, three non-nested size-based growth models are generally used to describe the growth of abalone populations: the von Bertalanffy, Gompertz and inverse logistic. The models differ in their description of growth, especially in the juvenile phase. However, while data on juveniles has the greatest discriminating power between models, in reality good data on size distributions and growth of juveniles is uncommon, and this leads to ambiguity in model selection. I use a large dataset (from the Tasmanian Aquaculture and Fisheries Institute) describing sizes and growth of juvenile and adult size classes to systematically resolve model ambiguity for blacklip abalone (Haliotis rubra ) populations in Tasmania. Modal progression analysis of bimonthly data collected over two years from the same site identified two cohorts of juveniles between 10 – 75 mm shell lengths. The best statistical model was selected using standard statistical model selection procedures, i.e. Akaike’s Information Criteria and likelihood ratio tests. Despite the large data set of 4,259 specimens, model selection remained statistically ambiguous. The Gompertz was selected as the best statistical model for one cohort and the linear model for the other. Interestingly, the biological implications of the best fitting Gompertz curve were not consistent with observations from aquaculture. The study revealed that slight differences in data quality may contribute to ambiguity in statistical model selection and that biological realism is also needed as a criterion for model selection. The robustness of different growth models to sampling error that is inconsistent between samples was explored using Monte Carlo simulation and cross model simulation. The focus Abstract ___________________________________________________________________________________ was on simulated length increment data largely from adult size classes (55 – 170 mm shell length) as these data are more commonplace than data from juveniles. Results confirm that the two main shortcomings in length increment data contributing to model misspecification were (i) poor representation of juvenile size classes (< 80 mm) and (ii) low sample size (n<150). Results indicate that when negative growth data are included in the von Bertalanffy model, K increases and L∞ decreases. In reality the true description of growth remains unknown. Given realistic length increment data, there is a reasonable probability that an incorrect growth model may be selected as the best statistical model. This is particularly important, because this study indicates there is a different magnitude of error associated with each growth model. The important overall finding is that while it is possible to make incorrect model selections using customary statistical fitting procedures, departures from biological reality are lower if the incorrect inverse logistic model is selected over the incorrect von Bertalanffy or Gompertz model. The selection of the most appropriate growth model was further tested by fitting each of the three growth models to length increment data from a total 30 wild populations. The inverse logistic was the best statistically fitting model in 23 populations. The combined results from data on the growth of juveniles, cross model simulation, and fitting to data from numerous wild populations systematically revealed that the inverse logistic model was the most robust empirical representation of blacklip abalone growth in Tasmania. With this confidence in the selected model, it was then possible to address two urgent ecological and management issues related to stock productivity; the effect of climate change on growth rates and the success of broad-scale management controls in the presence of fine-scale variability in growth rates. iv Abstract ___________________________________________________________________________________ The effect of ocean warming on the growth rates of blacklip abalone populations was explored from the analysis of length increment data from 30 populations across a range of water temperatures. Measurements based on the growth rates of juveniles did not reveal a clear negative relationship between temperature on growth. A decrease in growth rate was observed however it may not be directly attributable to temperature but may be forced by the onset of maturity, which does appear to be directly influenced by temperature. Fine-scale estimates of growth rate are an implicit aspect of evaluating the success of broad-scale management control such as Legal Minimum Length (LML) for harvesting. In reality, it is not possible to obtain fine-scale growth rates given the expense of obtaining empirical length increment data at fine spatial scales. Therefore, an alternative approach was developed that exploited the correlation between the parameters of the inverse logistic model and size at maturity. The approach generated theoretical, fine scale growth parameters and population-specific LMLs for 252 populations around Tasmania. Using population specific size limits, results revealed that 46 populations were unprotected by the current Legal Minimum Length (LML) settings, potentially exposing those populations to overexploitation. The majority of unprotected populations were located in the south west, a region that is economically valuable. An important recommendation from this thesis is that the LML of the economically valuable south-west region should be increased in order to achieve the management goals of the fishery. v ACKNOWLEDGEMENTS I am very grateful to my supervisors Craig Johnson, Malcolm Haddon and Geoff Tuck for their patience, fantastic mentoring, and for encouraging me throughout the whole process. I was fortunate to be supervised by such highly established professionals in the field. I would like to acknowledge the senior scientific officers for organizing the fieldwork that produced the dataset used in this study: Craig Mundy, Rick Officer, Warwick Nash and the technical staff of the Resource Security and Future Harvest Programme at the Tasmanian Aquaculture and Fisheries Institute, now part of the Institute for Marine and Antarctic Studies at the University of Tasmania. A big thanks to the people at the Tasmanian Abalone Council and their partners and to the Tasmanian

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