Linking spatial stock dynamics and economics: Evaluation of indicators and fishery management for the travelling eastern king prawn (Melicertus plebejus)

Reduced economic circumstances have moved management goals towards higher profit, rather than maximum sustainable yields in several Australian fisheries. The eastern king prawn is one such fishery, for which we have developed new methodology for stock dynamics, calculation of model-based and data-based reference points and management strategy evaluation. The fishery is notable for the northward movement of prawns in eastern Australian waters, from the State jurisdiction of New South Wales to that of Queensland, as they grow to spawning size, so that vessels fishing in the northern deeper waters harvest more large prawns. Bio-economic fishing data were standardized for calibrating a length-structured spatial operating model. Model simulations identified that reduced boat numbers and fishing effort could improve profitability while retaining viable fishing in each jurisdiction. Simulations also identified catch-rate levels that were effective for monitoring in simple within-year effort-control rules. However, favourable performance of catch-rate indicators was achieved only when a meaningful upper limit was placed on total allowed fishing effort. The methods and findings will allow improved measures for monitoring fisheries and inform decision makers on the uncertainty and assumptions affecting economic indicators.

Biological and economic management strategy evaluations of the eastern king prawn fishery

Stock assessment of the eastern king prawn (EKP) fishery, and the subsequent advice to management and industry, could be improved by addressing a number of issues. The recruitment dynamics of EKP in the northern (i.e., North Reef to the Swain Reefs) parts of the fishery need to be clarified. Fishers report that the size of the prawns from these areas when they recruit to the fishing grounds is resulting in suboptimal sizes/ages at first capture, and therefore localised growth overfishing. There is a need to assess alternative harvest strategies of the EKP fishery, via computer simulations, particularly seasonal and monthly or lunar-based closures to identify scenarios that improve the value of the catch, decrease costs and reduce the risk of overfishing, prior to implementing new management measures.

Micro-economic drivers of profitability in an ITQ-managed fishery: An analysis of the Queensland Coral Reef Fin-Fish Fishery

An economic survey of the commercial operators currently active in the Queensland Coral Reef Fin-Fish Fishery has been carried out, as part of a research project aimed at evaluating alternative management options for this fishery. This paper presents the background analysis used as a basis to develop the sampling design for this survey. The background analysis focuses on activity patterns of the fleet based on effort and catch information, as well as patterns of quota ownership. Based on this information, a fishing business profile describing the micro-economic structure of fishing operations is developed. This profile, in conjunction with the qualitative information gained in undertaking the economic surveys, allows preliminary understanding of the key drivers of profitability in the CRFFF, and possible impacts of external factors on fishing operations.

Stock assessment of the Queensland east coast common coral trout (Plectropomus leopardus) fishery

Common coral trout Plectropomus leopardus is an iconic fish of the Great Barrier Reef (GBR) and is the most important fish for the commercial fishery there. Most of the catch is exported live to Asia. This stock assessment was undertaken in response to falls in catch sizes and catch rates in recent years, in order to gauge the status of the stock. It is the first stock assessment ever conducted of coral trout on the GBR, and brings together a multitude of different data sources for the first time. The GBR is very large and was divided into a regional structure based on the Bioregions defined by expert committees appointed by the Great Barrier Reef Marine Park Authority (GBRMPA) as part of the 2004 rezoning of the GBR. The regional structure consists of six Regions, from the Far Northern Region in the north to the Swains and Capricorn–Bunker Regions in the south. Regions also closely follow the boundaries between Bioregions. Two of the northern Regions are split into Subregions on the basis of potential changes in fishing intensity between the Subregions; there are nine Subregions altogether, which include four Regions that are not split. Bioregions are split into Subbioregions along the Subregion boundaries. Finally, each Subbioregion is split into a “blue” population which is open to fishing and a “green” population which is closed to fishing. The fishery is unusual in that catch rates as an indicator of abundance of coral trout are heavily influenced by tropical cyclones. After a major cyclone, catch rates fall for two to three years, and rebound after that. This effect is well correlated with the times of occurrence of cyclones, and usually occurs in the same month that the cyclone strikes. However, statistical analyses correlating catch rates with cyclone wind energy did not provide significantly different catch rate trends. Alternative indicators of cyclone strength may explain more of the catch rate decline, and future work should investigate this. Another feature of catch rates is the phenomenon of social learning in coral trout populations, whereby when a population of coral trout is fished, individuals quickly learn not to take bait. Then the catch rate falls sharply even when the population size is still high. The social learning may take place by fish directly observing their fellows being hooked, or perhaps heeding a chemo-sensory cue emitted by fish that are hooked. As part of the assessment, analysis of data from replenishment closures of Boult Reef in the Capricorn–Bunker Region (closed 1983–86) and Bramble Reef in the Townsville Subregion (closed 1992–95) estimated a strong social learning effect. A major data source for the stock assessment was the large collection of underwater visual survey (UVS) data collected by divers who counted the coral trout that they sighted. This allowed estimation of the density of coral trout in the different Bioregions (expressed as a number of fish per hectare). Combined with mapping data of all the 3000 or so reefs making up the GBR, the UVS results provided direct estimates of the population size in each Subbioregion. A regional population dynamic model was developed to account for the intricacies of coral trout population dynamics and catch rates. Because the statistical analysis of catch rates did not attribute much of the decline to tropical cyclones, (and thereby implied “real” declines in biomass), and because in contrast the UVS data indicate relatively stable population sizes, model outputs were unduly influenced by the unlikely hypothesis that falling catch rates are real. The alternative hypothesis that UVS data are closer to the mark and declining catch rates are an artefact of spurious (e.g., cyclone impact) effects is much more probable. Judging by the population size estimates provided by the UVS data, there is no biological problem with the status of coral trout stocks. The estimate of the total number of Plectropomus leopardus on blue zones on the GBR in the mid-1980s (the time of the major UVS series) was 5.34 million legal-sized fish, or about 8400 t exploitable biomass, with an 2 additional 3350 t in green zones (using the current zoning which was introduced on 1 July 2004). For the offshore regions favoured by commercial fishers, the figure was about 4.90 million legal-sized fish in blue zones, or about 7700 t exploitable biomass. There is, however, an economic problem, as indicated by relatively low catch rates and anecdotal information provided by commercial fishers. The costs of fishing the GBR by hook and line (the only method compatible with the GBR’s high conservation status) are high, and commercial fishers are unable to operate profitably when catch rates are depressed (e.g., from a tropical cyclone). The economic problem is compounded by the effect of social learning in coral trout, whereby catch rates fall rapidly if fishers keep returning to the same fishing locations. In response, commercial fishers tend to spread out over the GBR, including the Far Northern and Swains Regions which are far from port and incur higher travel costs. The economic problem provides some logic to a reduction in the TACC. Such a reduction during good times, such as when the fishery is rebounding after a major tropical cyclone, could provide a net benefit to the fishery, as it would provide a margin of stock safety and make the fishery more economically robust by providing higher catch rates during subsequent periods of depressed catches. During hard times when catch rates are low (e.g., shortly after a major tropical cyclone), a change to the TACC would have little effect as even a reduced TACC would not come close to being filled. Quota adjustments based on catch rates should take account of long-term trends in order to mitigate variability and cyclone effects in data.

Deriving optimal fishing effort for managing Australia's Moreton Bay multispecies trawl fishery with aggregated effort data

Deriving an estimate of optimal fishing effort or even an approximate estimate is very valuable for managing fisheries with multiple target species. The most challenging task associated with this is allocating effort to individual species when only the total effort is recorded. Spatial information on the distribution of each species within a fishery can be used to justify the allocations, but often such information is not available. To determine the long-term overall effort required to achieve maximum sustainable yield (MSY) and maximum economic yield (MEY), we consider three methods for allocating effort: (i) optimal allocation, which optimally allocates effort among target species; (ii) fixed proportions, which chooses proportions based on past catch data; and (iii) economic allocation, which splits effort based on the expected catch value of each species. Determining the overall fishing effort required to achieve these management objectives is a maximizing problem subject to constraints due to economic and social considerations. We illustrated the approaches using a case study of the Moreton Bay Prawn Trawl Fishery in Queensland (Australia). The results were consistent across the three methods. Importantly, our analysis demonstrated the optimal total effort was very sensitive to daily fishing costs—the effort ranged from 9500–11 500 to 6000–7000, 4000 and 2500 boat-days, using daily cost estimates of $0, $500, $750, and $950, respectively. The zero daily cost corresponds to the MSY, while a daily cost of $750 most closely represents the actual present fishing cost. Given the recent debate on which costs should be factored into the analyses for deriving MEY, our findings highlight the importance of including an appropriate cost function for practical management advice. The approaches developed here could be applied to other multispecies fisheries where only aggregated fishing effort data are recorded, as the literature on this type of modelling is sparse.

Large-scale expansion of no-take closures within the Great Barrier Reef has not enhanced fishery production

A rare opportunity to test hypotheses about potential fishery benefits of large-scale closures was initiated in July 2004 when an additional 28.4% of the 348 000 km2 Great Barrier Reef (GBR) region of Queensland, Australia was closed to all fishing. Advice to the Australian and Queensland governments that supported this initiative predicted these additional closures would generate minimal (10%) initial reductions in both catch and landed value within the GBR area, with recovery of catches becoming apparent after three years. To test these predictions, commercial fisheries data from the GBR area and from the two adjacent (non-GBR) areas of Queensland were compared for the periods immediately before and after the closures were implemented. The observed means for total annual catch and value within the GBR declined from pre-closure (2000–2003) levels of 12 780 Mg and Australian $160 million, to initial post-closure (2005–2008) levels of 8143 Mg and $102 million; decreases of 35% and 36% respectively. Because the reference areas in the non-GBR had minimal changes in catch and value, the beyond-BACI (before, after, control, impact) analyses estimated initial net reductions within the GBR of 35% for both total catch and value. There was no evidence of recovery in total catch levels or any comparative improvement in catch rates within the GBR nine years after implementation. These results are not consistent with the advice to governments that the closures would have minimal initial impacts and rapidly generate benefits to fisheries in the GBR through increased juvenile recruitment and adult spillovers. Instead, the absence of evidence of recovery in catches to date currently supports an alternative hypothesis that where there is already effective fisheries management, the closing of areas to all fishing will generate reductions in overall catches similar to the percentage of the fished area that is closed.

Hazard Function Models to Estimate Mortality Rates Affecting Fish Populations with Application to the Sea Mullet (Mugil cephalus) Fishery on the Queensland Coast (Australia)

Fisheries management agencies around the world collect age data for the purpose of assessing the status of natural resources in their jurisdiction. Estimates of mortality rates represent a key information to assess the sustainability of fish stocks exploitation. Contrary to medical research or manufacturing where survival analysis is routinely applied to estimate failure rates, survival analysis has seldom been applied in fisheries stock assessment despite similar purposes between these fields of applied statistics. In this paper, we developed hazard functions to model the dynamic of an exploited fish population. These functions were used to estimate all parameters necessary for stock assessment (including natural and fishing mortality rates as well as gear selectivity) by maximum likelihood using age data from a sample of catch. This novel application of survival analysis to fisheries stock assessment was tested by Monte Carlo simulations to assert that it provided unbiased estimations of relevant quantities. The method was applied to the data from the Queensland (Australia) sea mullet (Mugil cephalus) commercial fishery collected between 2007 and 2014. It provided, for the first time, an estimate of natural mortality affecting this stock: 0.22±0.08 year −1 .

Queensland Spanner Crab Fishery: Commercial quota setting for June 2016 – May 2018

The Queensland (QLD) fishery for spanner crabs primarily lands live crab for export overseas, with gross landings valued around A$5 million per year. Quota setting rules are used to assess and adjust total allowable harvest (quota) around an agreed target harvest of 1631 t and capped at a maximum of 2000 t. The quota varies based on catch rate indicators from the commercial fishery and a fishery independent survey. Quota management applies only to ‘Managed Area A’ which includes waters between Rockhampton and the New South Wales (NSW) border. This report has been prepared to inform Fisheries Queensland (Department of Agriculture and Fisheries) and stakeholders of catch trends and the estimated quota of spanner crabs in Managed Area A for the forthcoming annual quota periods (1 June 2016–31 May 2018). The quota calculations followed the methodology developed by the crab fishery Scientific Advisory Group (SAG) between November 2007 and March 2008. The QLD total reported spanner crab harvest was 1170 t for the 2015 calendar year. In 2015, a total of 55 vessels were active in the QLD fishery, down from 262 vessels at the fishery’s peak activity in 1994. Recent spanner crab harvests from NSW waters average about 125 t per year, but fell to 80 t in 2014–2015. The spanner crab Managed Area A commercial standardised catch rate averaged 0.818 kg per net-lift in 2015, 22.5% below the target level of 1.043. Compared to 2014, mean catch rates in 2015 were marginally improved south of Fraser Island. The NSW–QLD survey catch rate in 2015 was 20.541 crabs per ground-line, 33% above the target level of 13.972. This represented an increase in survey catch rates of about four crabs per groundline, compared to the 2014 survey. The QLD spanner crab total allowable harvest (quota) was set at 1923 t in the 2012-13 and 2013-14 fishing years, 1777 t in 2014-15 and 1631 t in 2015-16. The results from the current analysis rules indicate that the quota for the next two fishing years be retained at the base quota of 1631 t.

Queensland Spanner Crab Fishery : Commercial quota setting for June 2015 – May 2016

The Australian fishery for spanner crabs is the largest in the world, with the larger Queensland (QLD) sector’s landings primarily exported live overseas and GVP valued ~A$5 million per year. Spanner crabs are unique in that they may live up to 15 years, significantly more than blue swimmer crabs (Portunus armatus) and mud crabs (Scylla serrata), the two other important crab species caught in Queensland. Spanner crabs are caught using a flat net called a dilly, on which the crabs becoming entangled via the swimming legs. Quota setting rules are used to assess and adjust total allowable harvest (quota) around an agreed target harvest of 1631 t and capped at a maximum of 2000 t. The quota varies based on catch rate indicators from the commercial fishery and a fishery-independent survey from the previous two years, compared to target reference points. Quota management applies only to ‘Managed Area A’ which includes waters between Rockhampton and the New South Wales (NSW) border. This report has been prepared to inform Fisheries Queensland (Department of Agriculture and Fisheries) and stakeholders of catch trends and the estimated quota of spanner crabs in Managed Area A for the forthcoming quota period (1 June 2015–31 May 2016). The quota calculations followed the methodology developed by the crab fishery Scientific Advisory Group (SAG) between November 2007 and March 2008. The total reported spanner crab harvest was 917 t for the 2014 calendar year, almost all of which was taken from Managed Area A. In 2014, a total of 59 vessels were active in the QLD fishery, the lowest number since the peak in 1994 of 262 vessels. Recent spanner crab harvests from NSW waters have been about 125 t per year. The spanner crab Managed Area A commercial standardised catch rate averaged 0.739 kg per net-lift in 2014, 24% below the target level of 1.043. Mean catch rates declined in the commercial fishery in 2014, although the magnitude of the decreases was highest in the area north of Fraser Island. The NSW–QLD survey catch rate in 2014 was 16.849 crabs per ground-line, 22% above the target level of 13.972. This represented a decrease in survey catch rates of 0.366 crabs per ground-line, compared to the 2013 survey. The Queensland spanner crab total allowable harvest (quota) was set at 1923 t in 2012 and 2013. In 2014, the quota was calculated at the base level of 1631 t. However, given that the 2012 fisheryindependent survey was not undertaken for financial reasons, stakeholders proposed that the total allowable commercial catch (TACC) be decreased to 1777 t; a level that was halfway between the 2012/13 quota of 1923 t and the recommended base quota of 1631 t. The results from the current analysis indicate that the quota for the 2015-2016 financial year be decreased from 1777 t to the base quota of 1631 t.

Variability of sif and cod of stochastic structural systems

Stochastic structural systems having a stochastic distribution of material properties and stochastic external loadings in space are analysed when a crack of deterministic size is present. The material properties and external loadings are considered to constitute independent, two-dimensional, univariate, real, homogeneous stochastic fields. The stochastic fields are characterized by their means, variances, autocorrelation functions or the equivalent power spectral density functions, and scale fluctuations. The Young's modulus and Poisson's ratio are treated to be stochastic quantities. The external loading is treated to be a stochastic field in space. The energy release rate is derived using the method of virtual crack extension. The deterministic relationship is derived to represent the sensitivities of energy release rate with respect to both virtual crack extension and real system parameter fluctuations. Taylor series expansion is used and truncation is made to the first order. This leads to the determination of second-order properties of the output quantities to the first order. Using the linear perturbations about the mean values of the output quantities, the statistical information about the energy release rates, SIF and crack opening displacements are obtained. Both plane stress and plane strain cases are considered. The general expressions for the SIF in all the three fracture modes are derived and a more detailed analysis is conducted for a mode I situation. A numerical example is given.