Development of a model based on Bayesian networks to estimate the probability of sheep lice presence at shearing

This paper describes the development of a model, based on Bayesian networks, to estimate the likelihood that sheep flocks are infested with lice at shearing and to assist farm managers or advisers to assess whether or not to apply a lousicide treatment. The risk of lice comes from three main sources: (i) lice may have been present at the previous shearing and not eradicated; (ii) lice may have been introduced with purchased sheep; and (iii) lice may have entered with strays. A Bayesian network is used to assess the probability of each of these events independently and combine them for an overall assessment. Rubbing is a common indicator of lice but there are other causes too. If rubbing has been observed, an additional Bayesian network is used to assess the probability that lice are the cause. The presence or absence of rubbing and its possible cause are combined with these networks to improve the overall risk assessment.

Hierarchical Bayesian modelling of early detection surveillance for plant pest invasions

Early detection surveillance programs aim to find invasions of exotic plant pests and diseases before they are too widespread to eradicate. However, the value of these programs can be difficult to justify when no positive detections are made. To demonstrate the value of pest absence information provided by these programs, we use a hierarchical Bayesian framework to model estimates of incursion extent with and without surveillance. A model for the latent invasion process provides the baseline against which surveillance data are assessed. Ecological knowledge and pest management criteria are introduced into the model using informative priors for invasion parameters. Observation models assimilate information from spatio-temporal presence/absence data to accommodate imperfect detection and generate posterior estimates of pest extent. When applied to an early detection program operating in Queensland, Australia, the framework demonstrates that this typical surveillance regime provides a modest reduction in the estimate that a surveyed district is infested. More importantly, the model suggests that early detection surveillance programs can provide a dramatic reduction in the putative area of incursion and therefore offer a substantial benefit to incursion management. By mapping spatial estimates of the point probability of infestation, the model identifies where future surveillance resources can be most effectively deployed.

Hierarchical Bayesian modelling of plant pest invasions with human-mediated dispersal

Hierarchical Bayesian models can assimilate surveillance and ecological information to estimate both invasion extent and model parameters for invading plant pests spread by people. A reliability analysis framework that can accommodate multiple dispersal modes is developed to estimate human-mediated dispersal parameters for an invasive species. Uncertainty in the observation process is modelled by accounting for local natural spread and population growth within spatial units. Broad scale incursion dynamics are based on a mechanistic gravity model with a Weibull distribution modification to incorporate a local pest build-up phase. The model uses Markov chain Monte Carlo simulations to infer the probability of colonisation times for discrete spatial units and to estimate connectivity parameters between these units. The hierarchical Bayesian model with observational and ecological components is applied to a surveillance dataset for a spiralling whitefly (Aleurodicus dispersus) invasion in Queensland, Australia. The model structure provides a useful application that draws on surveillance data and ecological knowledge that can be used to manage the risk of pest movement.

A Bayesian hurdle model for analysis of an insect resistance monitoring database

Motivated by the analysis of the Australian Grain Insect Resistance Database (AGIRD), we develop a Bayesian hurdle modelling approach to assess trends in strong resistance of stored grain insects to phosphine over time. The binary response variable from AGIRD indicating presence or absence of strong resistance is characterized by a majority of absence observations and the hurdle model is a two step approach that is useful when analyzing such a binary response dataset. The proposed hurdle model utilizes Bayesian classification trees to firstly identify covariates and covariate levels pertaining to possible presence or absence of strong resistance. Secondly, generalized additive models (GAMs) with spike and slab priors for variable selection are fitted to the subset of the dataset identified from the Bayesian classification tree indicating possibility of presence of strong resistance. From the GAM we assess trends, biosecurity issues and site specific variables influencing the presence of strong resistance using a variable selection approach. The proposed Bayesian hurdle model is compared to its frequentist counterpart, and also to a naive Bayesian approach which fits a GAM to the entire dataset. The Bayesian hurdle model has the benefit of providing a set of good trees for use in the first step and appears to provide enough flexibility to represent the influence of variables on strong resistance compared to the frequentist model, but also captures the subtle changes in the trend that are missed by the frequentist and naive Bayesian models. © 2014 Springer Science+Business Media New York.

Predictive modelling to aid the regional-scale management of a vertebrate pest

Extensive resources are allocated to managing vertebrate pests, yet spatial understanding of pest threats, and how they respond to management, is limited at the regional scale where much decision-making is undertaken. We provide regional-scale spatial models and management guidance for European rabbits (Oryctolagus cuniculus) in a 260,791 km(2) region in Australia by determining habitat suitability, habitat susceptibility and the effects of the primary rabbit management options (barrier fence, shooting and baiting and warren ripping) or changing predation or disease control levels. A participatory modelling approach was used to develop a Bayesian network which captured the main drivers of suitability and spread, which in turn was linked spatially to develop high resolution risk maps. Policy-makers, rabbit managers and technical experts were responsible for defining the questions the model needed to address, and for subsequently developing and parameterising the model. Habitat suitability was determined by conditions required for warren-building and by above-ground requirements, such as food and harbour, and habitat susceptibility by the distance from current distributions, habitat suitability, and the costs of traversing habitats of different quality. At least one-third of the region had a high probability of being highly suitable (support high rabbit densities), with the model supported by validation. Habitat susceptibility was largely restricted by the current known rabbit distribution. Warren ripping was the most effective control option as warrens were considered essential for rabbit persistence. The anticipated increase in disease resistance was predicted to increase the probability of moderately suitable habitat becoming highly suitable, but not increase the at-risk area. We demonstrate that it is possible to build spatial models to guide regional-level management of vertebrate pests which use the best available knowledge and capture fine spatial-scale processes.

Field evaluation of tolerance to Tobacco streak virus in sunflower germplasm, and observations of seasonal disease spread

Strong statistical evidence was found for differences in tolerance to natural infections of Tobacco streak virus (TSV) in sunflower hybrids. Data from 470 plots involving 23 different sunflower hybrids tested in multiple trials over 5 years in Australia were analysed. Using a Bayesian Hierarchical Logistic Regression model for analysis provided: (i) a rigorous method for investigating the relative effects of hybrid, seasonal rainfall and proximity to inoculum source on the incidence of severe TSV disease; (ii) a natural method for estimating the probability distributions of disease incidence in different hybrids under historical rainfall conditions; and (iii) a method for undertaking all pairwise comparisons of disease incidence between hybrids whilst controlling the familywise error rate without any drastic reduction in statistical power. The tolerance identified in field trials was effective against the main TSV strain associated with disease outbreaks, TSV-parthenium. Glasshouse tests indicate this tolerance to also be effective against the other TSV strain found in central Queensland, TSV-crownbeard. The use of tolerant germplasm is critical to minimise the risk of TSV epidemics in sunflower in this region. We found strong statistical evidence that rainfall during the early growing months of March and April had a negative effect on the incidence of severe infection with greatly reduced disease incidence in years that had high rainfall during this period.