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.

A novel breeding programme for improved growth in barramundi Lates calcarifer (Bloch) using foundation stock from progeny-tested parents

Rapid genetic gains for growth in barramundi ( Lates calcarifer) appear achievable by starting a breeding programme using foundation stock from progeny tested broodstock. The potential gains of this novel breeding design were investigated using biologically feasible scenarios tested with computer simulation models. The design involves the production of a large number of full-sib families using artificial mating which are compared in common growout conditions. The estimated breeding values of their paternal parents are calculated using a binomial probit analysis to assess their suitability as foundation broodstock. The programme can theoretically yield faster rates of genetic gain compared to other breeding programmes for aquaculture species. Assuming a heritability of 0.25 for growth, foundation broodstock evaluated in two years had breeding values for faster growth ranging from 21% to 51% depending on the genetic diversity of stock under evaluation. As a comparison it will take between nine and twenty-two years to identify broodstock with similar breeding values in a contemporary barramundi breeding programme.

Recrudescent Infection Supports Hendra Virus Persistence in Australian Flying-Fox Populations

Zoonoses from wildlife threaten global public health. Hendra virus is one of several zoonotic viral diseases that have recently emerged from Pteropus species fruit-bats (flying-foxes). Most hypotheses regarding persistence of Hendra virus within flying-fox populations emphasize horizontal transmission within local populations (colonies) via urine and other secretions, and transmission among colonies via migration. As an alternative hypothesis, we explore the role of recrudescence in persistence of Hendra virus in flying-fox populations via computer simulation using a model that integrates published information on the ecology of flying-foxes, and the ecology and epidemiology of Hendra virus. Simulated infection patterns agree with infection patterns observed in the field and suggest that Hendra virus could be maintained in an isolated flying-fox population indefinitely via periodic recrudescence in a manner indistinguishable from maintenance via periodic immigration of infected individuals. Further, post-recrudescence pulses of infectious flying-foxes provide a plausible basis for the observed seasonal clustering of equine cases. Correct understanding of the infection dynamics of Hendra virus in flying-foxes is fundamental to effectively managing risk of infection in horses and humans. Given the lack of clear empirical evidence on how the virus is maintained within populations, the role of recrudescence merits increased attention.

Methods to determine resistance to anthelmintics when continuing larval development occurs

The in vivo faecal egg count reduction test (FECRT) is the most commonly used test to detect anthelmintic resistance (AR) in gastrointestinal nematodes (GIN) of ruminants in pasture based systems. However, there are several variations on the method, some more appropriate than others in specific circumstances. While in some cases labour and time can be saved by just collecting post-drench faecal worm egg counts (FEC) of treatment groups with controls, or pre- and post-drench FEC of a treatment group with no controls, there are circumstances when pre- and post-drench FEC of an untreated control group as well as from the treatment groups are necessary. Computer simulation techniques were used to determine the most appropriate of several methods for calculating AR when there is continuing larval development during the testing period, as often occurs when anthelmintic treatments against genera of GIN with high biotic potential or high re-infection rates, such as Haemonchus contortus of sheep and Cooperia punctata of cattle, are less than 100% efficacious. Three field FECRT experimental designs were investigated: (I) post-drench FEC of treatment and controls groups, (II) pre- and post-drench FEC of a treatment group only and (III) pre- and post-drench FEC of treatment and control groups. To investigate the performance of methods of indicating AR for each of these designs, simulated animal FEC were generated from negative binominal distributions with subsequent sampling from the binomial distributions to account for drench effect, with varying parameters for worm burden, larval development and drench resistance. Calculations of percent reductions and confidence limits were based on those of the Standing Committee for Agriculture (SCA) guidelines. For the two field methods with pre-drench FEC, confidence limits were also determined from cumulative inverse Beta distributions of FEC, for eggs per gram (epg) and the number of eggs counted at detection levels of 50 and 25. Two rules for determining AR: (1) %reduction (%R) < 95% and lower confidence limit <90%; and (2) upper confidence limit <95%, were also assessed. For each combination of worm burden, larval development and drench resistance parameters, 1000 simulations were run to determine the number of times the theoretical percent reduction fell within the estimated confidence limits and the number of times resistance would have been declared. When continuing larval development occurs during the testing period of the FECRT, the simulations showed AR should be calculated from pre- and post-drench worm egg counts of an untreated control group as well as from the treatment group. If the widely used resistance rule 1 is used to assess resistance, rule 2 should also be applied, especially when %R is in the range 90 to 95% and resistance is suspected.

Herbicide resistance modelling: Past, present and future

Computer simulation modelling is an essential aid in building an integrated understanding of how different factors interact to affect the evolutionary and population dynamics of herbicide resistance, and thus in helping to predict and manage how agricultural systems will be affected. In this review, we first discuss why computer simulation modelling is such an important tool and framework for dealing with herbicide resistance. We then explain what questions related to herbicide resistance have been addressed to date using simulation modelling, and discuss the modelling approaches that have been used, focusing first on the earlier, more general approaches, and then on some newer, more innovative approaches. We then consider how these approaches could be further developed in the future, by drawing on modelling techniques that are already employed in other areas, such as individual-based and spatially explicit modelling approaches, as well as the possibility of better representing genetics, competition and economics, and finally the questions and issues of importance to herbicide resistance research and management that could be addressed using these new approaches are discussed. We conclude that it is necessary to proceed with caution when increasing the complexity of models by adding new details, but, with appropriate care, more detailed models will make it possible to integrate more current knowledge in order better to understand, predict and ultimately manage the evolution of herbicide resistance. © 2014 Society of Chemical Industry.