Peter J. Toscas

Network analysis reveals that bacteria and fungi form modules that correlate independently with soil parameters

Network and multivariate statistical analyses were performed to determine interactions between bacterial and fungal community terminal restriction length polymorphisms as well as soil properties in paired woodland and pasture sites. Canonical correspondence analysis (CCA) revealed that shifts in woodland community composition correlated with soil dissolved organic carbon, while changes in pasture community composition correlated with moisture, nitrogen and phosphorus. Weighted correlation network analysis detected two distinct microbial modules per land use. Bacterial and fungal ribotypes did not group separately, rather all modules comprised of both bacterial and fungal ribotypes. Woodland modules had a similar fungal : bacterial ribotype ratio, while in the pasture, one module was fungal dominated. There was no correspondence between pasture and woodland modules in their ribotype composition. The modules had different relationships to soil variables, and these contrasts were not detected without the use of network analysis. This study demonstrated that fungi and bacteria, components of the soil microbial communities usually treated as separate functional groups as in a CCA approach, were co-correlated and formed distinct associations in these adjacent habitats. Understanding these distinct modular associations may shed more light on their niche space in the soil environment, and allow a more realistic description of soil microbial ecology and function.

Estimating prawn abundance and catchability from catch-effort data: comparison of fixed and random effects models using maximum likelihood and hierarchical Bayesian methods

Abundance and catchability are crucial quantities in fisheries management, yet they are very difficult to estimate, particularly for short-lived invertebrates. Using two distinct approaches - a standard non-hierarchical model (NH) and a hierarchical Bayesian model (HB) - abundance and catchability coefficients from a fishery depletion process for banana prawns (Penaeus merguiensis) in northern Australia were estimated. Non-hierarchical models treated each stock and year separately and individually, whereas the hierarchical models assumed some form of common underlying population from which the parameters for the individual cases generated by the combination of stock and year were drawn. Two HBs were considered. In HB1 it was assumed that annual abundance and catchability parameters came from separate populations, or distributions, for each stock. In HB2 it was assumed that these stock region distributions were not separate, but had their parameters drawn from a common distribution. Thus in HB2 all stocks shared information at the regional level. The results for both NH and HB methods were similar in most cases, indicating a fair degree of stability irrespective of the particular form of model chosen. However, the NH method suffered because the data were analysed in generally small sections and in many cases these sections were too small to allow precise estimation of both parameters and confidence intervals. The deviations of point estimates between the HB1, HB2 and NH models were more marked in catchability coefficient estimates than in abundance estimates, and large relative deviations typically occurred in stock regions and years with low fishing efforts, low catch or poor depletion trends over time. We conclude that the combined analysis using HB was superior because it could handle limited data, yielded credible interval estimates for all parameters and was computationally more efficient.

Likelihood-based analysis of longitudinal count data using a generalized Poisson model:

Models based on a generalization of the simple Poisson process are discussed and illustrated with an analysis of some longitudinal count data on frequencies of epileptic fits. The models enable a broad class of discrete distributions to be constructed, which cover a variety of dispersion properties that can be characterized in an intuitive and appealing way by a simple parameterization. This class includes the Poisson and negative binomial distributions as well as other distributions with greater dispersion than Poisson, and also distributions underdispersed relative to the Poisson distribution. Comparing a number of analyses of the data shows that some covariates have a more significant effect using this modelling than from using mixed Poisson models. It is argued that this could be due to the mixed Poisson models used in the other analyses not providing an appropriate description of the residual variation, with the greater flexibility of the generalized Poisson modelling generally enabling more critical assessment of covariate effects than more standard mixed Poisson modelling.

C/N ratio drives soil actinobacterial cellobiohydrolase gene diversity.

ABSTRACT Cellulose accounts for approximately half of photosynthesis-fixed carbon; however, the ecology of its degradation in soil is still relatively poorly understood. The role of actinobacteria in cellulose degradation has not been extensively investigated despite their abundance in soil and known cellulose degradation capability. Here, the diversity and abundance of the actinobacterial glycoside hydrolase family 48 (cellobiohydrolase) gene in soils from three paired pasture-woodland sites were determined by using terminal restriction fragment length polymorphism (T-RFLP) analysis and clone libraries with gene-specific primers. For comparison, the diversity and abundance of general bacteria and fungi were also assessed. Phylogenetic analysis of the nucleotide sequences of 80 clones revealed significant new diversity of actinobacterial GH48 genes, and analysis of translated protein sequences showed that these enzymes are likely to represent functional cellobiohydrolases. The soil C/N ratio was the primary environmental driver of GH48 community compositions across sites and land uses, demonstrating the importance of substrate quality in their ecology. Furthermore, mid-infrared (MIR) spectrometry-predicted humic organic carbon was distinctly more important to GH48 diversity than to total bacterial and fungal diversity. This suggests a link between the actinobacterial GH48 community and soil organic carbon dynamics and highlights the potential importance of actinobacteria in the terrestrial carbon cycle.

Using Fisher Scoring to Fit Extended Poisson Process Models

SummaryThe extended Poisson Process model (EPPM) is a generalization of the simple Poisson process. It allows for the construction of distributions that can be over-dispersed or under-dispersed relative to the Poisson distribution. The modelling includes the Poisson and negative binomial distributions as special cases. Generally, a broad class of dispersion models can be characterized in an intuitive and appealing way by a simple parameterisation. In this paper a Fisher scoring algorithm is developed for fitting EPPMs with covariate dependent means.

Earthworm-induced shifts in microbial diversity in soils with rare versus established invasive earthworm populations

European earthworms have colonised many parts of Australia, although their impact on soil microbial communities remains largely uncharacterised. An experiment was conducted to contrast the responses to Aporrectodea trapezoides introduction between soils from sites with established (Talmo, 64 A. trapezoides m-2) and rare (Glenrock, 0.6 A. trapezoides m-2) A. trapezoides populations. Our hypothesis was that earthworm introduction would lead to similar changes in bacterial communities in both soils. The effects of earthworm introduction (earthworm activity and cadaver decomposition) did not lead to a convergence of bacterial community composition between the two soils. However, in both soils, the Firmicutes decreased in abundance and a common set of bacteria responded positively to earthworms. The increase in the abundance of Flavobacterium, Chitinophagaceae, Rhodocyclaceae and Sphingobacteriales were consistent with previous studies. Evidence for possible soil resistance to earthworms was observed, with lower earthworm survival in Glenrock microcosms coinciding with A. trapezoides rarity in this site, lower soil organic matter and clay content and differences in the diversity and abundance of potential earthworm mutualist bacteria. These results suggest that while the impacts of earthworms vary between different soils, the consistent response of some bacteria may aid in predicting the impacts of earthworms on soil ecosystems.

A comparison of methods for solving the sensor location problem

A problem that frequently arises in environmental surveillance is where to place a set of sensors in order to maximize collected information. In this article we compare four methods for solving this problem: a discrete approach based on the classical k-median location model, a continuous approach based on the minimization of the prediction error variance, an entropy-based algorithm, and simulated annealing. The methods are tested on artificial data and data collected from a network of sensors installed in the Springbrook National Park in Queensland, Australia, for the purpose of tracking the restoration of biodiversity. We present an overview of these methods and a comparison of results.

Modelling Inverse Gaussian Data with Censored Response Values: EM versus MCMC

Low detection limits are common in measure environmental variables. Building models using data containing low or high detection limits without adjusting for the censoring produces biased models. This paper offers approaches to estimate an inverse Gaussian distribution when some of the data used are censored because of low or high detection limits. Adjustments for the censoring can be made if there is between 2% and 20% censoring using either the EM algorithm or MCMC. This paper compares these approaches.

Fitting the Extended Poisson Process Model to Grouped Binary Data

SummaryExtended Poisson process modelling allows the construction of a broad class of distributions, including distributions over-dispersed or under-dispersed relative to the binomial distribution, with the binomial distribution being a special case. In this paper an iteratively re-weighted least squares algorithm for fitting such generalised binomial distributions is presented, and is illustrated with an example.