Stock assessment of white teatfish (Holothuria fuscogilva) in Queensland, Australia

A stock assessment model was used to assess the population of white teatfish (Holothuria fuscogilva) in the Queensland Sea Cucumber Fishery (East Coast). The assessment used an age structured surplus production model with annual time steps. The model incorporated commercial catch data from 1996 to 2021, and commercial standardised catch rates from 1999 to 2021. The key population performance indicator was an annual estimate of exploitable biomass. The stock assessment estimated the exploitable biomass in 2021 was 76% of unfished levels in 1995

Stock assessment of Queensland east coast tiger prawns (Penaeus esculentus and Penaeus semisulcatus)

Two assessments are presented on the status of tiger prawns (Penaeus esculentus and P. semisulcatus) within the East Coast Otter Trawl Fishery: an assessment for the fishery for the northern region (18 degrees latitude to tip of Cape York) and an assessment for the central region (22 - 18 degrees latitude). A delay difference model was applied that incorporated commercial catch-effort, gear and vessel data. The data inputs into the stock assessments included total harvests and standardised catch rates. The analyses incorporated the effects of fishing power. Results indicate that the biomass in 2019 was 49% of the unfished biomass in the northern region and 50% of the unfished biomass in the central region. Biomass estimates were relative to the unfished level in 1941. The recommended effort based on the Fisheries Queensland harvest control rule was 3824 boat-days in the northern region and 1276 boat-days in the central region. The recommended fishing effort is 1.2% lower than fishing effort in 2019 for the northern region and 57% lower in the central region

Standardised catch rates for Queensland Moreton Bay bugs (Thenus spp.)

Annual harvest and catch rates of Moreton Bay bugs (Thenus spp.) are presented for two major fishing areas in Queensland. Data on commercial catch and effort was analysed to estimate catch rate in each area as a proxy for biomass. Overall, the catch rate increased between 2002 and 2013 in both areas. After 2014, the catch rate from the northern area declined while the southern area increased. The increase in the south is likely due to the targeting of Moreton Bay bugs as the market value for bugs increased

Stock assessment of white teatfish (Holothuria fuscogilva) in Queensland, Australia

A stock assessment model was used to assess the population of white teatfish (Holothuria fuscogilva) in the Queensland Sea Cucumber Fishery (East Coast). The assessment used an age structured surplus production model with annual time steps. The model incorporated commercial catch data from 1996 to 2021, and commercial standardised catch rates from 1999 to 2021. The key population performance indicator was an annual estimate of exploitable biomass. The stock assessment estimated the exploitable biomass in 2021 was 76% of unfished levels in 1995

Stock assessment of black teatfish (Holothuria whitmaei) in Queensland, Australia

A stock assessment model was used to assess the population of black teatfish (Holothuria whitmaei formerly Holothuria nobilis) in Queensland east coast waters. The assessment used an age structured surplus production model with annual time steps. The model incorporated catch data from 1878 to 2021, and survey data from Koopman and Knuckey (2021). The key population performance indicators were annual estimates of total biomass and exploitable biomass. Based on the survey results from Koopman and Knuckey (2021), the stock assessment estimated the total biomass of east coast black teatfish in 2021 was between 40% and 42% of unfished total biomass in 1877. The exploitable biomass in 2021, as estimated by the model, was 40% of unfished levels in 1877

Generalised growth models for aquatic species with an application to blacklip abalone (Haliotis rubra)

This paper presents a maximum likelihood method for estimating growth parameters for an aquatic species that incorporates growth covariates, and takes into consideration multiple tag-recapture data. Individual variability in asymptotic length, age-at-tagging, and measurement error are also considered in the model structure. Using distribution theory, the log-likelihood function is derived under a generalised framework for the von Bertalanffy and Gompertz growth models. Due to the generality of the derivation, covariate effects can be included for both models with seasonality and tagging effects investigated. Method robustness is established via comparison with the Fabens, improved Fabens, James and a non-linear mixed-effects growth models, with the maximum likelihood method performing the best. The method is illustrated further with an application to blacklip abalone ( Haliotis rubra) for which a strong growth-retarding tagging effect that persisted for several months was detected

Growth of abalone (Haliotis rubra) with implications for its productivity

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 ¨C 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 ___________________________________________________________________________________ iv was on simulated length increment data largely from adult size classes (55 ¨C 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¡Þ 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. Abstract ___________________________________________________________________________________ v 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

Stock assessment of eastern king prawn (Melicertus plebejus)

This assessment estimates the status of eastern king prawn across the Queensland and New South Wales trawl fisheries. A bioeconomic model was applied that incorporated commercial catch-effort data and economics. The stock assessment data inputs included total harvests, standardised catch rates and fishery independent density estimates. The analysis takes into account the effects of fishing power. Results indicate that spawning biomass egg production in 2019 was around 62% of the unfished level. A feature of this assessment is the use of the bioeconomic model to directly estimate the Maximum Economic Yield (MEY) for the stock. Results indicate that that the MEY is 69% of the unfished level