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Introduction of pests and diseases through trade is one of the main socio‐ecological challenges worldwide. Although Binomial sampling inspection at the border can reduce pest entry risk, it is common for consignments to fail inspection, wasting resources for both exporter and importer. Outsourcing the inspection to the exporting country could reduce the cost of inspection for both parties. However, there is then a need to assess the quality of the offshore inspection. In this paper, we develop an inverse method combining past inspection data on the pathway, an onshore inspection sample, and the Beta‐Binomial model to infer the sample size of the offshore inspection. We illustrate the method on two case studies: the importation of live plants through germplasm into Australia and the importation of pelleted seeds in New Zealand. In these case studies, we found that detecting four to five infested units in a single onshore inspection was typically sufficient to significantly doubt the presence of a compliant offshore inspection. We also ran a simulation experiment to quantify the statistical power to reject or accept the presence of compliant offshore inspection in practice: In highly infested pathways, we could detect the absence of offshore inspections after inspecting five consignments onshore. Less infested pathways required inspecting 20 to 60 consignments onshore. Our study demonstrates that Binomial sampling onshore can be used to assess the quality of offshore inspections. [ABSTRACT FROM AUTHOR]

• Develops a deep Bayesian survival model that integrates deep neural network and Bayesian inference into the survival analysis for rail useful lifetime modelling. • Demonstrates survival analysis is an effective way to handle censored data problems in rail useful lifetime modelling. • Conducts extensive evaluation to verify the effectiveness of this model, using Concordance index, root mean square error, and a proposed CW-index. • The estimated lifetimes obtained by this model are the closest to real lifetimes compared to other commonly used models. • This model can provide the confidence interval of the estimated lifetime, which is more useful than point estimates in decision-making and maintenance planning of railroad systems. Reliable estimation of rail useful lifetime can provide valuable information for predictive maintenance in railway systems. However, in most cases, lifetime data is incomplete because not all pieces of rail experience failure by the end of the study horizon, a problem known as censoring. Ignoring or otherwise mistreating the censored cases might lead to false conclusions. Survival approach is particularly designed to handle censored data for analysing the expected duration of time until one event occurs, which is rail failure in this paper. This paper proposes a deep Bayesian survival approach named BNN-Surv to properly handle censored data for rail useful lifetime modelling. The proposed BNN-Surv model applies the deep neural network in the survival approach to capture the non-linear relationship between covariates and rail useful lifetime. To consider and quantify uncertainty in the model, Monte Carlo dropout, regarded as the approximate Bayesian inference, is incorporated into the deep neural network to provide the confidence interval of the estimated lifetime. The proposed approach is implemented on a four-year dataset including track geometry monitoring data, track characteristics data, various types of defect data, and maintenance and replacement (M&R) data collected from a section of railway tracks in Australia. Through extensive evaluation, including Concordance index (C-index) and root mean square error (RMSE) for evaluating model performance, as well as a proposed CW-index for evaluating uncertainty estimations, the effectiveness of the proposed approach is confirmed. The results show that, compared with other commonly used models, the proposed approach can achieve the best concordance index (C-index) of 0.80, and the estimated rail useful lifetimes are closer to real lifetimes. In addition, the proposed approach can provide the confidence interval of the estimated lifetime, with a correct coverage of 81% of the actual lifetime when the confidence interval is 1.38, which is more useful than point estimates in decision-making and maintenance planning of railroad systems. [ABSTRACT FROM AUTHOR]

Monitoring waterbird populations in Australia is challenging for reasons of counting logistics, and because population aggregation and dispersion can shift rapidly in response to large spatio-temporal variations in resource availability. The East Australian Waterbird survey has conducted annual, aerial, systematic counts of waterbirds over eastern Australia for almost 40 years. It was designed to monitor waterbird populations using design-based inference though for many species this form of inference appears inadequate in the face of these challenges. Here we develop a state-space model-based Bayesian approach that, in addition to explicitly incorporating process noise and observation uncertainty, uses random effects and rainfall-derived covariates to model the year-to-year variation in the proportion of the total (super) population that is present on surveyed wetlands, and available to be counted. We use this model-based approach to estimate the superpopulation size of 45 waterbird species annually, and model the rate of population increase as a function of antecedent rainfall. The results confirm the strong positive effect of antecedent rainfall on population growth rates for nearly all species, and illustrate that species respond to rainfall differently in terms of habitat use, which influences whether they are present on surveyed wetlands. For many species, the year-to-year variation in the estimated proportion of the population on surveyed wetlands is very high. The results have implications for making inferences on population trends from these data, with the ability to model the year-to-year sampling variation a key requirement before the rate of population increase can be estimated with any precision. This study illustrates how to progress this approach, and infers that under average rainfall conditions, the general trend is for estimated superpopulation rates of increase to be negative, though for only a few species is this occurring with strong belief. [ABSTRACT FROM AUTHOR]

The redlegged earth mite, Halotydeus destructor (Tucker, 1925: Trombidiformes, Eupodoidea, Penthaleidae), is an invasive mite species. In Australia, this mite has become a pest of winter pastures and grain crops. We report the complete mitogenome for H. destructor, the first to represent the family Penthaleidae, superfamily Eupodoidea. The mitogenome of H. destructor is 14,691 bp in size, and has a GC content of 27.87%, 13 protein‐coding genes, two rRNA genes, and 22 tRNA genes. We explored evolutionary relationships of H. destructor with other members of the Trombidiformes using phylogenetic analyses of nucleotide sequences and the order of protein‐coding and rRNA genes. We found strong, consistent support for the superfamily Tydeoidea being the sister taxon to the superfamily Eupodoidea based on nucleotide sequences and gene arrangements. Moreover, the gene arrangements of Eupodoidea and Tydeoidea are not only identical to each other but also identical to that of the hypothesized arthropod ancestor, showing a high level of conservatism in the mitogenomic structure of these mite superfamilies. Our study illustrates the utility of gene arrangements for providing complementary information to nucleotide sequences with respect to inferring the evolutionary relationships of species within the order Trombidiformes. The mitogenome of H. destructor provides a valuable resource for further population genetic studies of this important agricultural pest. Given the co‐occurrence of closely related, morphologically similar Penthaleidae mites with H. destructor in the field, a complete mitogenome provides new opportunities to develop metabarcoding tools to study mite diversity in agro‐ecosystems. Moreover, the H. destructor mitogenome fills an important taxonomic gap that will facilitate further study of trombidiform mite evolution. [ABSTRACT FROM AUTHOR]

Aim: We aimed to investigate whether the 12-item Multidimensional Scale of Perceived Social Support (MSPSS) constitutes a valid and reliable measure of social support for the general adult Australian population. Methods: Data were from Australia's National Survey of Adult Oral Health 2004–2006 and included 3899 participants aged 18 years old and over. The psychometric properties were evaluated with Bayesian confirmatory factor analysis. One-, two-, and three-factor (Significant Other, Family and Friends) structures were tested. Model fit was assessed with the posterior predictive p -value (PPPχ2), Bayesian root mean square error of approximation (BRMSEA), and Bayesian comparative fit index (BCFI). Dimensionality was tested by comparing competing factorial structures with the Bayes factor (BF). Reliability was evaluated with the Bayesian Ω H . Convergent validity was investigated with the Perceived Stress Scale (PSS) and discriminant validity with the Perceived Dental Control scale (PDC-3). Results: The theoretical three-factor model (Significant Other, Family, and Friends) provided a good fit to the data [PPPχ2 < 0.001, BRMSEA = 0.089-95% credible interval (CrI) (0.088, 0.089); BCFI = 0.963-95% CrI (0.963, 0.964)]. The BF provided decisive support for the three-factor structure in relation to the other structures. The SO [BΩ H = 0.95 - 95% CrI (0.90, 0.99)], FA (BΩ H = 0.92 - 95% CrI (0.87, 0.97), and FR (BΩ H = 0.92 - 95% CrI (0.88, 0.97)] subscales displayed excellent reliability. The MSPSS displayed initial evidence of convergent and discriminant validity. Conclusion: The MSPSS demonstrated good psychometric properties and excellent reliability in a large Australian sample. This instrument can be applied in national surveys and provide evidence of the role of social support in the Australian population. [ABSTRACT FROM AUTHOR]

Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes that determine vertical reef accretion and assemblage changes in fossil reef drill cores. We present a Bayesian framework called Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core that represent environmental conditions affecting the growth of coral assemblages in geological timescales. Weencounter multimodal posterior distributions and investigate the challenges of sampling using Markov chain Monte-Carlo (MCMC) methods, which includes parallel tempering MCMC. We use a synthetic reef-core to investigate fundamental issues and then apply the methodology to a selected reef-core from the Great Barrier Reef in Australia. The results show that Bayesreef accurately estimates and provides uncertainty quantification of the selected parameters that represent environment and ecological conditions in pyReef-Core. Bayesreef provides insights into the complex posterior distributions of the parameters in pyReef-Core , which provides the groundwork for future research in this area. • Estimating the impact of environmental processes on vertical reef development in geological time is a very challenging task. • pyReef-Core is a deterministic carbonate stratigraphic forward model designed to simulate the key biological and environmental processes. • This paper presents Bayesreef for the estimation and uncertainty quantification of parameters in pyReef-Core. • The results show that Bayesreef accurately estimates and provides uncertainty quantification for parameters in pyReef-Core. • Bayesreef provides insights in of pyReef-Core and establishes the groundwork for future research. [ABSTRACT FROM AUTHOR]