Slashing Phragmites (Phragmites australis) prior to planting does not promote native vegetation establishment

Phragmites or Common Reed (Phragmites australis) is a tall rhizomatous cosmopolitan grass. While native to Australia, it can be invasive in wetlands, forming dense monocultures and reducing their ecological integrity. We assessed the potential for the cutting of Phragmites reeds prior to planting to promote the establishment of indigenous shrubs that might ultimately outcompete Phragmites. We established ten 5 m × 5 m quadrats in an area dominated by Phragmites, brush‐cut the reeds to ground level in five of them and left five uncut as controls. Within each quadrat, we planted 20 plants (~40 cm tall) of each of five indigenous shrub species, unguarded (4 plants/m²). We surveyed the plants one month after planting and annually for the following four years for survival, height and browsing damage. Browsing damage to plants was common (>50%) and unaffected by cutting. After four years, overall plant survival rates were ~25% and mean plant heights for the five shrub species ranged between 120 and 174 cm. Cutting of Phragmites had no positive effect on plant survival or height. In fact, two Melaleuca species grew taller in the uncut quadrats. Cutting of Phragmites reed beds prior to planting is unlikely to promote the establishment of woody plantings. However, planting within established Phragmites with or without prior brush‐cutting is worthy of further trialling as a potential tool for reinstating native diversity at Phragmites‐dominated sites.     

Goldsurfer2 (Gs2): A comprehensive tool for the analysis and visualization of genome wide association studies

Background  

Genome wide association (GWA) studies are now being widely undertaken aiming to find the link between genetic variations and common diseases. Ideally, a well-powered GWA study will involve the measurement of hundreds of thousands of single nucleotide polymorphisms (SNPs) in thousands of individuals. The sheer volume of data generated by these experiments creates very high analytical demands. There are a number of important steps during the analysis of such data, many of which may present severe bottlenecks. The data need to be imported and reviewed to perform initial quality control (QC) before proceeding to association testing. Evaluation of results may involve further statistical analysis, such as permutation testing, or further QC of associated markers, for example, reviewing raw genotyping intensities. Finally significant associations need to be prioritised using functional and biological interpretation methods, browsing available biological annotation, pathway information and patterns of linkage disequilibrium (LD).