Microbiological status of piggery effluent from 13 piggeries in the south east Queensland region of Australia

Aims: To assist in the development of safe piggery effluent re-use guidelines by determining the level of selected pathogens and indicator organisms in the effluent ponds of 13 south-east Queensland piggeries. Methods and Results: The numbers of thermotolerant coliforms, Campylobacter jejuni/coli, Erysipelothrix rhusiopathiae, Escherichia coli, Salmonella and rotavirus were determined in 29 samples derived from the 13 piggeries. The study demonstrated that the 13 final effluent ponds contained an average of 1Æ2 · 105 colony-forming units (CFU) 100 ml)1 of thermotolerant coliforms and 1Æ03 · 105 CFU 100 ml)1 of E. coli. The Campylobacter level varied from none detectable (two of 13 piggeries) to a maximum of 930 most probable number (MPN) 100 ml)1 (two of 13 piggeries). Salmonella was detected in the final ponds of only four of the 13 piggeries and then only at a low level (highest level being 51 MPN 100 ml)1). No rotavirus and no Erysip. rhusiopathiae were detected. The average log10 reductions across the ponding systems to the final irrigation pond were 1Æ77 for thermotolerant coliforms, 1Æ71 for E. coli and 1Æ04 for Campylobacter. Conclusions: This study has provided a baseline knowledge on the levels of indicator organisms and selected pathogens in piggery effluent. Significance and Impact of the Study: The knowledge gained in this study will assist in the development of guidelines to ensure the safe and sustainable re-use of piggery effluent.

Detection of Arcobacter spp. in piggery effluent and effluent-irrigated soils in southeast Queensland

Aims: To investigate the occurrence and levels of Arcobacter spp. in pig effluent ponds and effluent-treated soil. Methods and Results: A Most Probable Number (MPN) method was developed to assess the levels of Arcobacter spp. in seven pig effluent ponds and six effluent-treated soils, immediately after effluent irrigation. Arcobacter spp. levels in the effluent ponds varied from 6.5 × 105 to 1.1 × 108 MPN 100 ml-1 and in freshly irrigated soils from 9.5 × 102 to 2.8 × 104 MPN g-1 in all piggery environments tested. Eighty-three Arcobacter isolates were subjected to an abbreviated phenotypic test scheme and examined using a multiplex polymerase chain reaction (PCR). The PCR identified 35% of these isolates as Arcobacter butzleri, 49% as Arcobacter cryaerophilus while 16% gave no band. All 13 nonreactive isolates were subjected to partial 16S rDNA sequencing and showed a high similarity (>99%) to Arcobacter cibarius. Conclusions: A. butzleri, A. cryaerophilus and A. cibarius were isolated from both piggery effluent and effluent-irrigated soil, at levels suggestive of good survival in the effluent pond. Significance and Impact of the Study: This is the first study to provide quantitative information on Arcobacter spp. levels in piggery effluent and to associate A. cibarius with pigs and piggery effluent environments.

Mechanically Ventilated Broiler Sheds: a Possible Source of Aerosolized Salmonella, Campylobacter, and Escherichia coli.

This study assessed the levels of two key pathogens, Salmonella and Campylobacter, along with the indicator organism Escherichia coli in aerosols within and outside poultry sheds. The study ranged over a 3-year period on four poultry farms and consisted of six trials across the boiler production cycle of around 55 days. Weekly testing of litter and aerosols was carried out through the cycle. A key point that emerged is that the levels of airborne bacteria are linked to the levels of these bacteria in litter. This hypothesis was demonstrated by E. coli. The typical levels of E. coli in litter were similar to 10(8) CFU g(-1) and, as a consequence, were in the range of 10(2) to 10(4) CFU m(-3) in aerosols, both inside and outside the shed. The external levels were always lower than the internal levels. Salmonella was only present intermittently in litter and at lower levels (10(3) to 10(5) most probable number [MPN] g(-1)) and consequently present only intermittently and at low levels in air inside (range of 0.65 to 4.4 MPN m(-3)) and once outside (2.3 MPN m(-3)). The Salmonella serovars isolated in litter were generally also isolated from aerosols and dust, with the Salmonella serovars Chester and Sofia being the dominant serovars across these interfaces. Campylobacter was detected late in the production cycle, in litter at levels of around 107 MPN g(-1). Campylobacter was detected only once inside the shed and then at low levels of 2.2 MPN m(-3). Thus, the public health risk from these organisms in poultry environments via the aerosol pathway is minimal.

Presence and incidence of food-borne pathogens in Australian chicken litter.

1. Litter samples were collected at the end of the production cycle from spread litter in a single shed from each of 28 farms distributed across the three Eastern seaboard States of Australia. 2. The geometric mean for Salmonella was 44 Most Probable Number (MPN)/g for the 20 positive samples. Five samples were between 100 and 1000 MPN/g and one at 105 MPN/g, indicating a range of factors are contributing to these varying loads of this organism in litter. 3. The geometric mean for Campylobacter was 30 MPN/g for the 10 positive samples, with 7 of these samples being 100 MPN/g. The low prevalence and incidence of Campylobacter were possibly due to the rapid die-off of this organism. 4. E. coli values were markedly higher than the two key pathogens (geometric mean 20 x 105 colony forming units (cfu)/g) with overall values being more or less within the same range across all samples in the trial, suggesting a uniform contribution pattern of these organisms in litter. 5. Listeria monocytogenes was absent in all samples and this organism appears not to be an issue in litter. 6. The dominant (70% of the isolates) Salmonella serovar was S. Sofia (a common serovar isolated from chickens in Australia) and was isolated across all regions. Other major serovars were S. Virchow and S. Chester (at 10%) and S. Bovismorbificans and S. Infantis (at 8%) with these serovars demonstrating a spatial distribution across the major regions tested. 7. There is potential to re-use litter in the environment depending on end use and the support of relevant application practices and guidelines.

A new data source for fisheries resource assessment: genetic estimates of the effective number of spawners. Final Report to the Fisheries Research and Development Corporation.

The development of innovative methods of stock assessment is a priority for State and Commonwealth fisheries agencies. It is driven by the need to facilitate sustainable exploitation of naturally occurring fisheries resources for the current and future economic, social and environmental well being of Australia. This project was initiated in this context and took advantage of considerable recent achievements in genomics that are shaping our comprehension of the DNA of humans and animals. The basic idea behind this project was that genetic estimates of effective population size, which can be made from empirical measurements of genetic drift, were equivalent to estimates of the successful number of spawners that is an important parameter in process of fisheries stock assessment. The broad objectives of this study were to 1. Critically evaluate a variety of mathematical methods of calculating effective spawner numbers (Ne) by a. conducting comprehensive computer simulations, and by b. analysis of empirical data collected from the Moreton Bay population of tiger prawns (P. esculentus). 2. Lay the groundwork for the application of the technology in the northern prawn fishery (NPF). 3. Produce software for the calculation of Ne, and to make it widely available. The project pulled together a range of mathematical models for estimating current effective population size from diverse sources. Some of them had been recently implemented with the latest statistical methods (eg. Bayesian framework Berthier, Beaumont et al. 2002), while others had lower profiles (eg. Pudovkin, Zaykin et al. 1996; Rousset and Raymond 1995). Computer code and later software with a user-friendly interface (NeEstimator) was produced to implement the methods. This was used as a basis for simulation experiments to evaluate the performance of the methods with an individual-based model of a prawn population. Following the guidelines suggested by computer simulations, the tiger prawn population in Moreton Bay (south-east Queensland) was sampled for genetic analysis with eight microsatellite loci in three successive spring spawning seasons in 2001, 2002 and 2003. As predicted by the simulations, the estimates had non-infinite upper confidence limits, which is a major achievement for the application of the method to a naturally-occurring, short generation, highly fecund invertebrate species. The genetic estimate of the number of successful spawners was around 1000 individuals in two consecutive years. This contrasts with about 500,000 prawns participating in spawning. It is not possible to distinguish successful from non-successful spawners so we suggest a high level of protection for the entire spawning population. We interpret the difference in numbers between successful and non-successful spawners as a large variation in the number of offspring per family that survive – a large number of families have no surviving offspring, while a few have a large number. We explored various ways in which Ne can be useful in fisheries management. It can be a surrogate for spawning population size, assuming the ratio between Ne and spawning population size has been previously calculated for that species. Alternatively, it can be a surrogate for recruitment, again assuming that the ratio between Ne and recruitment has been previously determined. The number of species that can be analysed in this way, however, is likely to be small because of species-specific life history requirements that need to be satisfied for accuracy. The most universal approach would be to integrate Ne with spawning stock-recruitment models, so that these models are more accurate when applied to fisheries populations. A pathway to achieve this was established in this project, which we predict will significantly improve fisheries sustainability in the future. Regardless of the success of integrating Ne into spawning stock-recruitment models, Ne could be used as a fisheries monitoring tool. Declines in spawning stock size or increases in natural or harvest mortality would be reflected by a decline in Ne. This would be good for data-poor fisheries and provides fishery independent information, however, we suggest a species-by-species approach. Some species may be too numerous or experiencing too much migration for the method to work. During the project two important theoretical studies of the simultaneous estimation of effective population size and migration were published (Vitalis and Couvet 2001b; Wang and Whitlock 2003). These methods, combined with collection of preliminary genetic data from the tiger prawn population in southern Gulf of Carpentaria population and a computer simulation study that evaluated the effect of differing reproductive strategies on genetic estimates, suggest that this technology could make an important contribution to the stock assessment process in the northern prawn fishery (NPF). Advances in the genomics world are rapid and already a cheaper, more reliable substitute for microsatellite loci in this technology is available. Digital data from single nucleotide polymorphisms (SNPs) are likely to super cede ‘analogue’ microsatellite data, making it cheaper and easier to apply the method to species with large population sizes.

Effect of changes in hook pattern and size on catch rate, hooking location, injury and bleeding for a number of tropical reef fish species

The Queensland Great Barrier Reef line fishery in Australia is regulated via a range of input and output controls including minimum size limits, daily catch limits and commercial catch quotas. As a result of these measures a substantial proportion of the catch is released or discarded. The fate of these released fish is uncertain, but hook-related mortality can potentially be decreased by using hooks that reduce the rates of injury, bleeding and deep hooking. There is also the potential to reduce the capture of non-target species though gear selectivity. A total of 1053 individual fish representing five target species and three non-target species were caught using six hook types including three hook patterns (non-offset circle, J and offset circle), each in two sizes (small 4/0 or 5/0 and large 8/0). Catch rates for each of the hook patterns and sizes varied between species with no consistent results for target or non-target species. When data for all of the fish species were aggregated there was a trend for larger hooks, J hooks and offset circle hooks to cause a greater number of injuries. Using larger hooks was more likely to result in bleeding, although this trend was not statistically significant. Larger hooks were also more likely to foul-hook fish or hook fish in the eye. There was a reduction in the rates of injuries and bleeding for both target and non-target species when using the smaller hook sizes. For a number of species included in our study the incidence of deep hooking decreased when using non-offset circle hooks, however, these results were not consistent for all species. Our results highlight the variability in hook performance across a range of tropical demersal finfish species. The most obvious conservation benefits for both target and non-target species arise from using smaller sized hooks and non-offset circle hooks. Fishers should be encouraged to use these hook configurations to reduce the potential for post-release mortality of released fish.

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.