Whitefly Transmission and Efficient ssDNA Accumulation of Bean Golden Mosaic Geminivirus Require Functional Coat Protein

Bean golden mosaic geminivirus (BGMV) has a bipartite genome composed of two circular ssDNA components (DNA-A and DNA-B) and is transmitted by the whitefly, Bemisia tabaci. DNA-A encodes the viral replication proteins and the coat protein. To determine the role of BGMV coat protein systemic infection and whitefly transmission, two deletions and a restriction fragment inversion were introduced into the BGMV coat protein gene. All three coat protein mutants produced systemic infections when coinoculated with DNA-B onto Phaseolus vulgaris using electric discharge particle acceleration "particle gun." However, they were not sap transmissible and coat protein was not detected in mutant-infected plants. In addition, none of the mutants were transmitted by whiteflies. With all three mutants, ssDNA accumulation of DNA-A and DNA-B was reduced 25- to 50-fold and 3- to 10-fold, respectively, as compared to that of wild-type DNA. No effect on dsDNA-A accumulation was detected and there was 2- to 5-fold increase in dsDNA-B accumulation. Recombinants between the mutated DNA-A and DNA-B forms were identified when the inoculated coat protein mutant was linearized in the common region.

Hierarchical Bayesian models for estimating the extent of plant pest invasions

Plant biosecurity requires statistical tools to interpret field surveillance data in order to manage pest incursions that threaten crop production and trade. Ultimately, management decisions need to be based on the probability that an area is infested or free of a pest. Current informal approaches to delimiting pest extent rely upon expert ecological interpretation of presence / absence data over space and time. Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis. Markov chain Monte Carlo simulation is used to estimate the probable extent of pests, given the observation and process model conditioned by surveillance data. Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. The model suggests that typical early detection programs provide a moderate reduction in the probability of an area being infested but a dramatic reduction in the expected area of incursions at a given time. Estimates of spiralling whitefly extent are examined at local, district and state-wide scales. The local model estimates the rate of natural spread and the influence of host architecture, host suitability and inspector efficiency. These parameter estimates can support the development of robust surveillance programs. Hierarchical Bayesian models for the human-mediated spread of spiralling whitefly are developed for the colonisation of discrete cells connected by a modified gravity model. By estimating dispersal parameters, the model can be used to predict the extent of the pest over time. An extended model predicts the climate restricted distribution of the pest in Queensland. These novel human-mediated movement models are well suited to demonstrating area freedom at coarse spatio-temporal scales. At finer scales, and in the presence of ecological complexity, exploratory models are developed to investigate the capacity for surveillance information to estimate the extent of red banded mango caterpillar. It is apparent that excessive uncertainty about observation and ecological parameters can impose limits on inference at the scales required for effective management of response programs. The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities. Hierarchical Bayesian modelling is demonstrated as both a useful analytical tool for estimating pest extent and a natural investigative paradigm for developing and focussing biosecurity programs.