What drives plant biodiversity in the clay floodplain grasslands of NSW?

An assessment of the relative influences of management and environment on the composition of floodplain grasslands of north-western New South Wales was made using a regional vegetation survey sampling a range of land tenures (e. g. private property, travelling stock routes and nature reserves). A total of 364 taxa belonging to 55 different plant families was recorded. Partitioning of variance with redundancy analysis determined that environmental variables accounted for a greater proportion (61.3%) of the explained variance in species composition than disturbance-related variables (37.6%). Soil type (and fertility), sampling time and rainfall had a strong influence on species composition and there were also east-west variations in composition across the region. Of the disturbance-related variables, cultivation, stocking rate and flooding frequency were all influential. Total, native, forb, shrub and subshrub richness were positively correlated with increasing time since cultivation. Flood frequency was positively correlated with graminoid species richness and was negatively correlated with total and forb species richness. Site species richness was also influenced by environmental variables (e. g. soil type and rainfall). Despite the resilience of these grasslands, some forms of severe disturbance (e. g. several years of cultivation) can result in removal of some dominant perennial grasses (e. g. Astrebla spp.) and an increase in disturbance specialists. A simple heuristic transitional model is proposed that has conceptual thresholds for plant biodiversity status. This knowledge representation may be used to assist in the management of these grasslands by defining four broad levels of community richness and the drivers that change this status.

Landscape structure influences tree density patterns in fragmented woodlands in semi-arid eastern Australia.

Landscape and local-scale influences are important drivers of plant community structure. However, their relative contribution and the degree to which they interact remain unclear. We quantified the extent to which landscape structure, within-patch habitat and their confounding effects determine post-clearing tree densities and composition in agricultural landscapes in eastern subtropical Australia. Landscape structure (incorporating habitat fragmentation and loss) and within-patch (site) features were quantified for 60 remnant patches of Eucalyptus populnea (Myrtaceae) woodland. Tree density and species for three ecological maturity classes (regeneration, early maturity, late maturity) and local site features were assessed in one 100 × 10 m plot per patch. All but one landscape characteristic was determined within a 1.3-km radius of plots; Euclidean nearest neighbour distance was measured inside a 5-km radius. Variation in tree density and composition for each maturity class was partitioned into independent landscape, independent site and joint effects of landscape and site features using redundancy analysis. Independent site effects explained more variation in regeneration density and composition than pure landscape effects; significant predictors were the proportion of early and late maturity trees at a site, rainfall and the associated interaction. Conversely, landscape structure explained greater variation in early and late maturity tree density and composition than site predictors. Area of remnant native vegetation within a landscape and patch characteristics (area, shape, edge contrast) were significant predictors of early maturity tree density. However, 31% of the explained variation in early mature tree differences represented confounding influences of landscape and local variables. We suggest that within-patch characteristics are important in influencing semi-arid woodland tree regeneration. However, independent and confounding effects of landscape structure resulting from previous vegetation clearing may have exerted a greater historical influence on older cohorts and should be accounted for when examining woodland dynamics across a broader range of environments.

Microbial indicators related to yield and disease and changes in soil microbial community structure with ginger farm management practices

TRFLP (terminal restriction fragment length polymorphism) was used to assess whether management practices that improved disease suppression and/or yield in a 4-year ginger field trial were related to changes in soil microbial community structure. Bacterial and fungal community profiles were defined by presence and abundance of terminal restriction fragments (TRFs), where each TRF represents one or more species. Results indicated inclusion of an organic amendment and minimum tillage increased the relative diversity of dominant fungal populations in a system dependant way. Inclusion of an organic amendment increased bacterial species richness in the pasture treatment. Redundancy analysis showed shifts in microbial community structure associated with different management practices and treatments grouped according to TRF abundance in relation to yield and disease incidence. ANOVA also indicated the abundance of certain TRFs was significantly affected by farming system management practices, and a number of these TRFs were also correlated with yield or disease suppression. Further analyses are required to determine whether identified TRFs can be used as general or soil-type specific bio-indicators of productivity (increased and decreased) and Pythium myriotylum suppressiveness.

Maize kernel hardness classification by near infrared (NIR) hyperspectral imaging and multivariate data analysis.

The use of near infrared (NIR) hyperspectral imaging and hyperspectral image analysis for distinguishing between hard, intermediate and soft maize kernels from inbred lines was evaluated. NIR hyperspectral images of two sets (12 and 24 kernels) of whole maize kernels were acquired using a Spectral Dimensions MatrixNIR camera with a spectral range of 960-1662 nm and a sisuChema SWIR (short wave infrared) hyperspectral pushbroom imaging system with a spectral range of 1000-2498 nm. Exploratory principal component analysis (PCA) was used on absorbance images to remove background, bad pixels and shading. On the cleaned images. PCA could be used effectively to find histological classes including glassy (hard) and floury (soft) endosperm. PCA illustrated a distinct difference between glassy and floury endosperm along principal component (PC) three on the MatrixNIR and PC two on the sisuChema with two distinguishable clusters. Subsequently partial least squares discriminant analysis (PLS-DA) was applied to build a classification model. The PLS-DA model from the MatrixNIR image (12 kernels) resulted in root mean square error of prediction (RMSEP) value of 0.18. This was repeated on the MatrixNIR image of the 24 kernels which resulted in RMSEP of 0.18. The sisuChema image yielded RMSEP value of 0.29. The reproducible results obtained with the different data sets indicate that the method proposed in this paper has a real potential for future classification uses.

Phylogenetic analysis of betanodavirus isolates from Australian finfish

In Australia, disease caused by betanodavirus has been reported in an increasing number of cultured finfish since the first report of mortalities in 1990. Partial coat protein gene sequences from the T2 or T4 regions of 8 betanodaviruses from barramundi Lates calcarifer, sleepy cod Oxyeleotris lineolata, striped trumpeter Latris lineata, barramundi cod Cromileptes altivelis, Australian bass Macquaria novemaculata and gold-spotted rockcod Epinephelus coioides from several Australian states were determined. Analysis of the 606 bp nucleotide sequences of the T2 region of 4 isolates demonstrated the close relationship with isolates from the red-spotted grouper nervous necrosis virus (RGNNV) genotype and the Cluster Ia subtype. Comparison of a smaller 289 bp sequence from the T4 region identified 2 distinct groupings of the Australian isolates within the RGNNV genotype. Isolates from barramundi from the Northern Territory, barramundi, sleepy cod, barramundi cod and gold-spotted rockcod from Queensland, and striped trumpeter from Tasmania shared a 96.2 to 99.7%, nucleotide identity with each other. These isolates were most similar to the RGNNV genotype Cluster Ia. Isolates from Australian bass from New South Wales and from barramundi from South Australia shared a 98.6% sequence identity with each other. However, these isolates only shared an 85.8 to 87.9%, identity with the other Australian isolates and representative RGNNV isolates. The closest nucleotide identity to sequences reported in the literature for the New South Wales and South Australian isolates was to an Australian barramundi isolate (Ba94Aus) from 1994. These 2 Australian isolates formed a new subtype within the RGNNV genotype, which is designated as Cluster Ic.

Campylobacter jejuni and Campylobacter coli Genotyping by High-Resolution Melting Analysis of a flaA Fragment.

The highly variable flagellin-encoding flaA gene has long been used for genotyping Campylobacter jejuni and Campylobacter coli. High-resolution melting (HRM) analysis is emerging as an efficient and robust method for discriminating DNA sequence variants. The objective of this study was to apply HRM analysis to flaA-based genotyping. The initial aim was to identify a suitable flaA fragment. It was found that the PCR primers commonly used to amplify the flaA short variable repeat (SVR) yielded a mixed PCR product unsuitable for HRM analysis. However, a PCR primer set composed of the upstream primer used to amplify the fragment used for flaA restriction fragment length polymorphism (RFLP) analysis and the downstream primer used for flaA SVR amplification generated a very pure PCR product, and this primer set was used for the remainder of the study. Eighty-seven C. jejuni and 15 C. coli isolates were analyzed by flaA HRM and also partial flaA sequencing. There were 47 flaA sequence variants, and all were resolved by HRM analysis. The isolates used had previously also been genotyped using single-nucleotide polymorphisms (SNPs), binary markers, CRISPR HRM, and flaA RFLP.flaA HRM analysis provided resolving power multiplicative to the SNPs, binary markers, and CRISPR HRM and largely concordant with the flaA RFLP. It was concluded that HRM analysis is a promising approach to genotyping based on highly variable genes.

Molecular phylogenetic analysis reveals six new species of Diaporthe from Australia

Six new species of Diaporthe, D. beilharziae on Indigofera australis, D. fraxini-angustifoliae on Fraxinus angustifolia subsp. oxycarpa, D. litchicola on Litchi chinensis, D. nothofagi on Nothofagus cunninghamii, D. pascoei on Persea americana and D. salicicola on Salix purpurea from Australia are described and illustrated based on morphological characteristics and molecular analyses. Three of the new species no longer produced sporulating structures in culture and two of these were morphologically described from voucher specimens. Phylogenetic relationships of the new species with other Diaporthe species are revealed by DNA sequence analyses based on the internal transcribed spacer (ITS) region, and partial regions of the β-tubulin (BT) and translation elongation factor 1-alpha (TEF). © 2013 Mushroom Research Foundation.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Highly heritable resistance to root-lesion nematode (Pratylenchus thornei) in Australian chickpea germplasm observed using an optimised glasshouse method and multi-environment trial analysis

Pratylenchus thornei is a root-lesion nematode (RLN) of economic significance in the grain growing regions of Australia. Chickpea (Cicer arietinum) is a significant legume crop grown throughout these regions, but previous testing found most cultivars were susceptible to P. thornei. Therefore, improved resistance to P. thornei is an important objective of the Australian chickpea breeding program. A glasshouse method was developed to assess resistance of chickpea lines to P. thornei, which requires relatively low labour and resource input, and hence is suited to routine adoption within a breeding program. Using this method, good differentiation of chickpea cultivars for P. thornei resistance was measured after 12 weeks. Nematode multiplication was higher for all genotypes than the unplanted control, but of the 47 cultivars and breeding lines tested, 17 exhibited partial resistance, allowing less than two fold multiplication. The relative differences in resistance identified using this method were highly heritable (0.69) and were validated against P. thornei data from seven field trials using a multi-environment trial analysis. Genetic correlations for cultivar resistance between the glasshouse and six of the field trials were high (>0.73). These results demonstrate that resistance to P. thornei in chickpea is highly heritable and can be effectively selected in a limited set of environments. The improved resistance found in a number of the newer chickpea cultivars tested shows that some advances have been made in the P. thornei resistance of Australian chickpea cultivars, and that further targeted breeding and selection should provide incremental improvements.