Concordant molecular and phenotypic data delineate new taxonomy and conservation priorities for the endangered mountain yellow-legged frog

The mountain yellow-legged frog Rana muscosa sensu lato, once abundant in the Sierra Nevada of California and Nevada, and the disjunct Transverse Ranges of southern California, has declined precipitously throughout its range, even though most of its habitat is protected. The species is now extinct in Nevada and reduced to tiny remnants in southern California, where as a distinct population segment, it is classified as Endangered. Introduced predators (trout), air pollution and an infectious disease (chytridiomycosis) threaten remaining populations. A Bayesian analysis of 1901 base pairs of mitochondrial DNA confirms the presence of two deeply divergent clades that come into near contact in the Sierra Nevada. Morphological studies of museum specimens and analysis of acoustic data show that the two major mtDNA clades are readily differentiated phenotypically. Accordingly, we recognize two species, Rana sierrae, in the northern and central Sierra Nevada, and R. muscosa, in the southern Sierra Nevada and southern California. Existing data indicate no range overlap. These results have important implications for the conservation of these two species as they illuminate a profound mismatch between the current delineation of the distinct population segments (southern California vs. Sierra Nevada) and actual species boundaries. For example, our study finds that remnant populations of R. muscosa exist in both the southern Sierra Nevada and the mountains of southern California, which may broaden options for management. In addition, despite the fact that only the southern California populations are listed as Endangered, surveys conducted since 1995 at 225 historic (1899-1994) localities from museum collections show that 93.3% (n=146) of R. sierrae populations and 95.2% (n=79) of R. muscosa populations are extinct. Evidence presented here underscores the need for revision of protected population status to include both species throughout their ranges.

Quantifying Transmission of Campylobacter jejuni in Commercial Broiler Flocks

Since meat from poultry colonized with Campylobacter spp. is a major cause of bacterial gastroenteritis, human exposure should be reduced by, among other things, prevention of colonization of broiler flocks. To obtain more insight into possible sources of introduction of Campylobacter into broiler flocks, it is essential to estimate the moment that the first bird in a flock is colonized. If the rate of transmission within a flock were known, such an estimate could be determined from the change in the prevalence of colonized birds in a flock over time. The aim of this study was to determine the rate of transmission of Campylobacter using field data gathered for 5 years for Australian broiler flocks. We used unique sampling data for 42 Campylobacter jejuni-colonized flocks and estimated the transmission rate, which is defined as the number of secondary infections caused by one colonized bird per day. The estimate was 2.37 +/- 0.295 infections per infectious bird per day, which implies that in our study population colonized flocks consisting of 20,000 broilers would have an increase in within-flock prevalence to 95% within 4.4 to 7.2 days after colonization of the first broiler. Using Bayesian analysis, the moment of colonization of the first bird in a flock was estimated to be from 21 days of age onward in all flocks in the study. This study provides an important quantitative estimate of the rate of transmission of Campylobacter in broiler flocks, which could be helpful in future studies on the epidemiology of Campylobacter in the field.

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.

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.

Refined Global Analysis of Bemisia tabaci (Hemiptera: Sternorrhyncha: Aleyrodoidea: Aleyrodidae) Mitochondrial Cytochrome Oxidase 1 to Identify Species Level Genetic Boundaries.

Identifying species boundaries within morphologically indistinguishable cryptic species complexes is often contentious. For the whitefly Bemisia tabaci (Gennadius) (Hemiptera: Sternorrhyncha: Aleyrodoidea: Aleyrodidae), the lack of a clear understanding about the genetic limits of the numerous genetic groups and biotypes so far identified has resulted in a lack of consistency in the application of the terms, the approaches use to apply them and in our understanding of what genetic structure within B. tabaci means. Our response has been to use mitochondrial gene cytochrome oxidase one to consider how to clearly and consistently define genetic separation. Using Bayesian phylogenetic analysis and analysis of sequence pairwise divergence we found a considerably higher to number of genetic groups than had been previously determined with two breaks in the distribution, one at 11% and another at 3.5%. At >11% divergence, 11 distinct groups were resolved, whereas at >3.5% divergence 24 groups were identified. Consensus sequences for each of these groups were determined and were shown to be useful in the correct assignment of sequences of unknown origin. The 3.5% divergence bound is consistent with species level separations in other insect taxa and Suggests that B. tabaci is it cryptic species composed of at least 24 distinct species. We further show that the placement of Bemesia atriplex (Froggatt) within the B. tabaci in, group adds further weight to the argument for species level separation within B. tabaci. This new analysis, which constructs consensus sequences and uses these its a standard against which unknown sequences call be compared, provides for the first time it consistent means of identifying the genetic hounds of each species with it high degree of certainty.

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.

Resistance to phosphine in Sitophilus oryzae in Australia: A national analysis of trends and frequencies over time and geographical spread

In Australia, along with many other parts of the world, fumigation with phosphine is a vital component in controlling stored grain insect pests. However, resistance is a factor that may limit the continued efficacy of this fumigant. While strong resistance to phosphine has been identified and characterised, very little information is available on the causes of its development and spread. Data obtained from a unique national resistance monitoring and management program were analysed, using Bayesian hurdle modelling, to determine which factors may be responsible. Fumigation in unsealed storages, combined with a high frequency of weak resistance, were found to be the main criteria that led to the development of strong resistance in Sitophilus oryzae. Independent development, rather than gene flow via migration, appears to be primarily responsible for the geographic incidence of strong resistance to phosphine in S. oryzae. This information can now be utilised to direct resources and education into those areas at high risk and to refine phosphine resistance management strategies.