An analysis of assessment outcomes from eight years' operation of the Australian border weed risk assessment system

The majority of Australian weeds are exotic plant species that were intentionally introduced for a variety of horticultural and agricultural purposes. A border weed risk assessment system (WRA) was implemented in 1997 in order to reduce the high economic costs and massive environmental damage associated with introducing serious weeds. We review the behaviour of this system with regard to eight years of data collected from the assessment of species proposed for importation or held within genetic resource centres in Australia. From a taxonomic perspective, species from the Chenopodiaceae and Poaceae were most likely to be rejected and those from the Arecaceae and Flacourtiaceae were most likely to be accepted. Dendrogram analysis and classification and regression tree (TREE) models were also used to analyse the data. The latter revealed that a small subset of the 35 variables assessed was highly associated with the outcome of the original assessment. The TREE model examining all of the data contained just five variables: unintentional human dispersal, congeneric weed, weed elsewhere, tolerates or benefits from mutilation, cultivation or fire, and reproduction by vegetative propagation. It gave the same outcome as the full WRA model for 71% of species. Weed elsewhere was not the first splitting variable in this model, indicating that the WRA has a capacity for capturing species that have no history of weediness. A reduced TREE model (in which human-mediated variables had been removed) contained four variables: broad climate suitability, reproduction in less or than equal to 1 year, self-fertilisation, and tolerates and benefits from mutilation, cultivation or fire. It yielded the same outcome as the full WRA model for 65% of species. Data inconsistencies and the relative importance of questions are discussed, with some recommendations made for improving the use of the system.

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.

Isolation of multiple novel paramyxoviruses from pteropid bat urine

Bats have been found to harbor a number of new emerging viruses with zoonotic potential and there has been a great deal of interest in identifying novel bat pathogens to determine risk to human and animal health. Many groups have identified novel viruses in bats by detection of viral nucleic acid, however virus isolation is still a challenge and there are few reports of viral isolates from bats. In recent years, our group has developed optimized procedures for virus isolation from bat urine, including the use of primary bat cells. In previous reports we have described the isolation of Hendra virus, Menangle virus and Cedar virus, in Queensland, Australia. Here, we report the isolation of four additional novel bat paramyxoviruses from urine collected from beneath pteropid bat (flying fox) colonies in Queensland and New South Wales during 2009-2011.

Human-associated fluoroquinolone-resistant Escherichia coli clonal lineages, including ST354, isolated from canine feces and extraintestinal infections in Australia

Phylogenetic group D extraintestinal pathogenic Escherichia coli (ExPEC), including O15:K52:H1 and clonal group A, have spread globally and become fluoroquinolone-resistant. Here we investigated the role of canine feces as a reservoir of these (and other) human-associated ExPEC and their potential as canine pathogens. We characterized and compared fluoroquinolone-resistant E. coli isolates originally identified as phylogenetic group D from either the feces of hospitalized dogs (n = 67; 14 dogs) or extraintestinal infections (n = 53; 33 dogs). Isolates underwent phylogenetic grouping, random amplified polymorphic DNA (RAPD) analysis, virulence genotyping, resistance genotyping, human-associated ExPEC O-typing, and multi-locus sequence typing. Five of seven human-associated sequence types (STs) exhibited ExPEC-associated O-types, and appeared in separate RAPD clusters. The largest subgroup (16 fecal, 26 clinical isolates) were ST354 (phylogroup F) isolates. ST420 (phylogroup B2); O1-ST38, O15:K52:H1-ST393, and O15:K1-ST130 (phylogroup D); and O7-ST457, and O1-ST648 (phylogroup F) were also identified. Three ST-specific RAPD sub-clusters (ST354, ST393, and ST457) contained closely related isolates from both fecal or clinical sources. Genes encoding CTX-M and AmpC β-lactamases were identified in isolates from five STs. Major human-associated fluoroquinolone-resistant ± extended-spectrum cephalosporin-resistant ExPEC of public health importance may be carried in dog feces and cause extraintestinal infections in some dogs.