Systematics of the Ustilago-Sporisorium-Macalpinomyces complex of smut fungi

Smut fungi are important pathogens of grasses, including the cultivated crops maize, sorghum and sugarcane. Typically, smut fungi infect the inflorescence of their host plants. Three genera of smut fungi (Ustilago, Sporisorium and Macalpinomyces) form a complex with overlapping morphological characters, making species placement problematic. For example, the newly described Macalpinomyces mackinlayi possesses a combination of morphological characters such that it cannot be unambiguously accommodated in any of the three genera. Previous attempts to define Ustilago, Sporisorium and Macalpinomyces using morphology and molecular phylogenetics have highlighted the polyphyletic nature of the genera, but have failed to produce a satisfactory taxonomic resolution. A detailed systematic study of 137 smut species in the Ustilago-Sporisorium- Macalpinomyces complex was completed in the current work. Morphological and DNA sequence data from five loci were assessed with maximum likelihood and Bayesian inference to reconstruct a phylogeny of the complex. The phylogenetic hypotheses generated were used to identify morphological synapomorphies, some of which had previously been dismissed as a useful way to delimit the complex. These synapomorphic characters are the basis for a revised taxonomic classification of the Ustilago-Sporisorium-Macalpinomyces complex, which takes into account their morphological diversity and coevolution with their grass hosts. The new classification is based on a redescription of the type genus Sporisorium, and the establishment of four genera, described from newly recognised monophyletic groups, to accommodate species expelled from Sporisorium. Over 150 taxonomic combinations have been proposed as an outcome of this investigation, which makes a rigorous and objective contribution to the fungal systematics of these important plant pathogens.

Variational Bayes and the reduced dependence approximation for the autologistic model on an irregular grid with applications

Discrete Markov random field models provide a natural framework for representing images or spatial datasets. They model the spatial association present while providing a convenient Markovian dependency structure and strong edge-preservation properties. However, parameter estimation for discrete Markov random field models is difficult due to the complex form of the associated normalizing constant for the likelihood function. For large lattices, the reduced dependence approximation to the normalizing constant is based on the concept of performing computationally efficient and feasible forward recursions on smaller sublattices which are then suitably combined to estimate the constant for the whole lattice. We present an efficient computational extension of the forward recursion approach for the autologistic model to lattices that have an irregularly shaped boundary and which may contain regions with no data; these lattices are typical in applications. Consequently, we also extend the reduced dependence approximation to these scenarios enabling us to implement a practical and efficient non-simulation based approach for spatial data analysis within the variational Bayesian framework. The methodology is illustrated through application to simulated data and example images. The supplemental materials include our C++ source code for computing the approximate normalizing constant and simulation studies.

Insect distributions and sampling protocols for stored commodities

Purpose of review: This review provides an overview on the importance of characterising and considering insect distribution infor- mation for designing stored commodity sampling protocols. Findings: Sampling protocols are influenced by a number of factors including government regulations, management practices, new technology and current perceptions of the status of insect pest damage. The spatial distribution of insects in stored commodities influ- ences the efficiency of sampling protocols; these can vary in response to season, treatment and other factors. It is important to use sam- pling designs based on robust statistics suitable for the purpose. Future research: The development of sampling protocols based on flexible, robust statistics allows for accuracy across a range of spatial distributions. Additionally, power can be added to sampling protocols through the integration of external information such as treatment history and climate. Bayesian analysis provides a coherent and well understood means to achieve this.

Time dependency of molecular rate estimates and systematic overestimation of recent divergence times

Studies of molecular evolutionary rates have yielded a wide range of rate estimates for various genes and taxa. Recent studies based on population-level and pedigree data have produced remarkably high estimates of mutation rate, which strongly contrast with substitution rates inferred in phylogenetic (species-level) studies. Using Bayesian analysis with a relaxed-clock model, we estimated rates for three groups of mitochondrial data: avian protein-coding genes, primate protein-coding genes, and primate d-loop sequences. In all three cases, we found a measurable transition between the high, short-term (<1–2 Myr) mutation rate and the low, long-term substitution rate. The relationship between the age of the calibration and the rate of change can be described by a vertically translated exponential decay curve, which may be used for correcting molecular date estimates. The phylogenetic substitution rates in mitochondria are approximately 0.5% per million years for avian protein-coding sequences and 1.5% per million years for primate protein-coding and d-loop sequences. Further analyses showed that purifying selection offers the most convincing explanation for the observed relationship between the estimated rate and the depth of the calibration. We rule out the possibility that it is a spurious result arising from sequence errors, and find it unlikely that the apparent decline in rates over time is caused by mutational saturation. Using a rate curve estimated from the d-loop data, several dates for last common ancestors were calculated: modern humans and Neandertals (354 ka; 222–705 ka), Neandertals (108 ka; 70–156 ka), and modern humans (76 ka; 47–110 ka). If the rate curve for a particular taxonomic group can be accurately estimated, it can be a useful tool for correcting divergence date estimates by taking the rate decay into account. Our results show that it is invalid to extrapolate molecular rates of change across different evolutionary timescales, which has important consequences for studies of populations, domestication, conservation genetics, and human evolution.

Modeling random effect and excess zeros in road traffic accident prediction

Poisson distribution has often been used for count like accident data. Negative Binomial (NB) distribution has been adopted in the count data to take care of the over-dispersion problem. However, Poisson and NB distributions are incapable of taking into account some unobserved heterogeneities due to spatial and temporal effects of accident data. To overcome this problem, Random Effect models have been developed. Again another challenge with existing traffic accident prediction models is the distribution of excess zero accident observations in some accident data. Although Zero-Inflated Poisson (ZIP) model is capable of handling the dual-state system in accident data with excess zero observations, it does not accommodate the within-location correlation and between-location correlation heterogeneities which are the basic motivations for the need of the Random Effect models. This paper proposes an effective way of fitting ZIP model with location specific random effects and for model calibration and assessment the Bayesian analysis is recommended.

Spatiotemporal modelling in estimation of nitrous oxide emissions from soil

Nitrous oxide is a major greenhouse gas emission. The aim of this research was to develop and apply statistical models to characterize the complex spatial and temporal variation in nitrous oxide emissions from soils under different land use conditions. This is critical when developing site-specific management plans to reduce nitrous oxide emissions. These studies can improve predictions and increase our understanding of environmental factors that influence nitrous oxide emissions. They also help to identify areas for future research, which can further improve the prediction of nitrous oxide in practice.

An assessment of the geographical scale of recurrent gene flow in wild populations of two species of Mekong River carps (Henicorhynchus spp.)

The Mekong is the most productive river fishery in the world, and such as, the Mekong River Basin (MRB) is very important to very large human populations across the region as a source of revenue (through fishing and marketing of aquatic resources products) and as the major source for local animal protein. Threats to biodiversity in the MRB, either to the fishery sector itself or to other sectors are a major concern, even though currently, fisheries across this region are still very productive. If not managed properly however, fish population declines will cause significant economic impact and affect livelihoods of local people and will have a major impact on food security and nutrition. Biodiversity declines will undoubtedly affect food security, income and socio-economic status of people in the MRB that depend on aquatic resources. This is an indicator of unsustainable development and hence should be avoided. Genetic diversity (biodiversity) that can be measured using techniques based on DNA markers; refers to variation within and among populations within the same species or reproductive units. In a population, new genetic variation is generated by sexual recombination contributed by individuals with mutations in genes and chromosomes. Over time, populations of a species that are not reproducing together will diverge as differential impacts of selection and genetic drift change their genetic attributes. For mud carp (Henicorhynchus spp.), understanding the status of breeding units in the MRB will be important for their long term persistence, sustainability and for implementing effective management strategies. Earlier analysis of stock structure in two economically important mud carp species (Henicorhynchus siamensis and H. lobatus) in the MRB completed with mtDNA markers identified a number of populations of both species where gene flow had apparently been interrupted or reduced but applying these data directly to management unit identification is potentially compromised because information was only available about female dispersal patterns. The current study aimed to address this problem and to fully assess the extent of current gene flow (nDNA) and reproductive exchange among selected wild populations of two species of carp (Henicorhynchus spp.) of high economic importance in the MRB using combined mtDNA and nDNA markers. In combination, the data can be used to define effective management units for each species. In general, nDNA diversity for H. lobatus (with average allelic richness (A) 7.56 and average heterozygosity (Ho) 0.61) was very similar to that identified for H. siamensis (A = 6.81 and Ho = 0.75). Both mud carp species show significant but low FST estimates among populations as a result of lower genetic diversity among sampled populations compared with genetic diversity within populations that may potentially mask any 'real' population structure. Overall, population genetic structure patterns from mtDNA and nDNA in both Henicorhynchus species were largely congruent. Different population structures however, were identified for the two Henicorhynchus species across the same geographical area. Apparent co-similarity in morphology and co-distribution of these two relatively closely related species does not apparently imply parallel evolutionary histories. Differences in each species population structure likely reflect historical drainage rearrangement of the Mekong River. The data indicate that H. siamensis is likely to have occupied the Mekong system for much longer than has H. lobatus in the past. Two divergent stocks were identified for H. lobatus in the MRB below the Khone Falls while a single stock had been evident in the earlier mtDNA study. This suggests that the two Henicorhynchus species may possess different life history traits and that different patterns of gene flow has likely influenced modern genetic structure in these close congeners. In combination, results of the earlier mtDNA and the current study have implications for effective management of both Henicorhynchus species across the MRB. Currently, both species are essentially treated as a single management unit in this region. This strategy may be appropriate for H. lobatus as a single stock was evident in the main stream of the MRB, but may not be appropriate for H. siamensis as more than a single stock was identified across the same range for this species. Management strategies should consider this difference to conserve overall biodiversity (local discrete populations) and this will include maintaining natural habitat and migration pathways, provision of fish sanctuaries (refuges) and may also require close monitoring of any stock declines, a signal that may require effective recovery strategies.

Combining expert opinions in prior elicitation

We consider the problem of combining opinions from different experts in an explicitly model-based way to construct a valid subjective prior in a Bayesian statistical approach. We propose a generic approach by considering a hierarchical model accounting for various sources of variation as well as accounting for potential dependence between experts. We apply this approach to two problems. The first problem deals with a food risk assessment problem involving modelling dose-response for Listeria monocytogenes contamination of mice. Two hierarchical levels of variation are considered (between and within experts) with a complex mathematical situation due to the use of an indirect probit regression. The second concerns the time taken by PhD students to submit their thesis in a particular school. It illustrates a complex situation where three hierarchical levels of variation are modelled but with a simpler underlying probability distribution (log-Normal).

Towards a phylogeny of the Tenebrionoidea (Coleoptera)

The phylogenetic relationships of the beetle superfamily Tenebrionoidea are investigated using the most comprehensive genetic data set compiled to date. With ∼34,000 described species in approximately 1250 genera and 28 families, Tenebrionoidea represent one of the most diverse and species-rich superfamilies of beetles. The interfamilial relationships of the Tenebrionoidea are poorly known; previous morphological and molecular phylogenies recovered few well-supported and often conflicting relationships between families. Here we present a molecular phylogeny of Tenebrionoidea based on genes commonly used to resolve family and superfamily-level phylogenies of beetles (18S, 28S, 16S, 12S, tRNA Val and COI). The alignment spanned over 6.5 KB of DNA sequence and over 300 tenebrionoid genera from 24 of the 28 families were sampled. Maximum Likelihood and Bayesian analysis could not resolve deeper level divergences within the superfamily and very few relationships between families were supported. Increasing gene coverage in the alignment by removing taxa with missing data did not improve clade support but when rogue taxa were removed increased resolution was recovered. Investigation of signal strength suggested conflicting phylogenetic signal was present in the standard genes used for beetle phylogenetics, even when rogue taxa were removed. Our study of Tenebrionoidea highlights that even with relatively comprehensive taxon sampling within a lineage, this standard set of genes is unable to resolve relationships within this superfamily.

Paying the extinction debt: Woodland birds in the Mount Lofty Ranges, South Australia

Approximately 90% of the original woodlands of the Mount Lofty Ranges of South Australia has been cleared, modified or fragmented, most severely in the last 60 years, and affecting the avifauna dependent on native vegetation. This study identifies which woodland-dependent species are still declining in two different habitats, Pink GumBlue Gum woodland and Stringybark woodland. We analyse the Mount Lofty Ranges Woodland Bird Long-Term Monitoring Dataset for 1999-2007, to look for changes in abundance of 59 species. We use logistic regression of prevalence on lists in a Bayesian framework, and List Length Analysis to control for variation in detectability. Compared with Reporting Rate Analysis, a more traditional approach, List Length Analysis provides tighter confidence intervals by accounting for changing detectability. Several common species were declining significantly. Increasers were generally large-bodied generalists. Many birds have already disappeared from this modified and naturally isolated woodland island, and our results suggest that more specialist insectivores are likely to follow. The Mount Lofty Ranges can be regarded as a 'canary landscape' for temperate woodlands elsewhere in Australia without immediate action their bird communities are likely to follow the trajectory of the Mount Lofty Ranges avifauna. Alternatively, with extensive habitat restoration and management, we could avoid paying the extinction debt. © Royal Australasian Ornithologists Union 2011.

Transdimensional sequential Monte Carlo using variational Bayes — SMCVB

A new transdimensional Sequential Monte Carlo (SMC) algorithm called SM- CVB is proposed. In an SMC approach, a weighted sample of particles is generated from a sequence of probability distributions which ‘converge’ to the target distribution of interest, in this case a Bayesian posterior distri- bution. The approach is based on the use of variational Bayes to propose new particles at each iteration of the SMCVB algorithm in order to target the posterior more efficiently. The variational-Bayes-generated proposals are not limited to a fixed dimension. This means that the weighted particle sets that arise can have varying dimensions thereby allowing us the option to also estimate an appropriate dimension for the model. This novel algorithm is outlined within the context of finite mixture model estimation. This pro- vides a less computationally demanding alternative to using reversible jump Markov chain Monte Carlo kernels within an SMC approach. We illustrate these ideas in a simulated data analysis and in applications.

Anthropogenic nitrogen deposition predicts local grassland primary production worldwide

Humans dominate many important Earth system processes including the nitrogen (N) cycle. Atmospheric N deposition affects fundamental processes such as carbon cycling, climate regulation, and biodiversity, and could result in changes to fundamental Earth system processes such as primary production. Both modelling and experimentation have suggested a role for anthropogenically altered N deposition in increasing productivity, nevertheless, current understanding of the relative strength of N deposition with respect to other controls on production such as edaphic conditions and climate is limited. Here we use an international multiscale data set to show that atmospheric N deposition is positively correlated to aboveground net primary production (ANPP) observed at the 1-m2 level across a wide range of herbaceous ecosystems. N deposition was a better predictor than climatic drivers and local soil conditions, explaining 16% of observed variation in ANPP globally with an increase of 1 kg N·ha-1·yr-1 increasing ANPP by 3%. Soil pH explained 8% of observed variation in ANPP while climatic drivers showed no significant relationship. Our results illustrate that the incorporation of global N deposition patterns in Earth system models are likely to substantially improve estimates of primary production in herbaceous systems. In herbaceous systems across the world, humans appear to be partially driving local ANPP through impacts on the N cycle.