The role of Riparian habitats in forest mosaics in promoting Avifauna at the landscape scale

The importance of riparian habitats to bird communities is well documented internationally, however the value of these habitats in largely intact landscapes is less well understood, particularly in Australia. Thirty paired riparian and adjacent non-riparian sites were selected within extensive forest mosaics of the Victorian Central Highlands and were surveyed over a two year period. Bird assemblages occurring within riparian habitats supported a significantly greater richness, abundance and diversity of species. These assemblages were also found to have species compositions significantly different from those occurring at adjacent non-riparian sites separated by a distance of approximately 750 m. Differences were attributed to a suite of distinctive species and significant contrasts in the densities of a range of species that occur in both habitat types. At the landscape level, there was a strong patterning of the avifauna centred on riparian habitats. Bird assemblages typically comprised four distinct suites of species: 1. species widespread in forests and woodlands of southeastern Australia; 2. riparian associated species (wet forest intruders); 3. riparian selective species, and; 4. riparian avoiding species. Both physiognomic and floristic differences between riparian and adjacent non-riparian habitats appear to drive responses in the structure of bird communities. There exists a distinctiveness and variability among the range of vegetation types and associated bird assemblages occurring throughout the forest matrix, including in riparian habitats. The occurrence of complimentary bird assemblages throughout the landscape mosaic highlights the importance of whole landscape planning for avifauna conservation.

Habitat requirements of the yellow-footed antechinus (Antechinus flavipes) in box–ironbark forest, Victoria, Australia

Understanding the habitat requirements of a species is critical for effective conservation-based management. In this study, we investigated the influence of forest structure on the distribution of the yellow-footed antechinus (Antechinus flavipes), a small dasyurid marsupial characteristic of dry forests on the inland side of the Great Dividing Range, Australia. Hair-sampling tubes were used to determine the occurrence of A. flavipes at 60 sites stratified across one of the largest remaining tracts of dry box–ironbark forest in south-eastern Australia. We considered the role of six potential explanatory variables: large trees, hollow-bearing trees, coppice hollows, logs, rock cover and litter. Logistic regression models were examined using an information-theoretic approach to determine the variables that best explained the presence or absence of the species. Hierarchical partitioning was employed to further explore relationships between occurrence of A. flavipes and explanatory variables. Forest structure accounted for a substantial proportion of the variation in occurrence of A. flavipes between sites. The strongest influence on the presence of A. flavipes was the cover of litter at survey sites. The density of hollow-bearing trees and rock cover were also positive influences. The conservation of A. flavipes will be enhanced by retention of habitat components that ensure a structurally complex environment in box–ironbark forests. This will also benefit the conservation of several threatened species in this dry forest ecosystem.

Subcellular localisation of proteins in fluorescent microscope images using a random forest

This paper presents a system that employs random forests to formulate a method for subcellular localisation of proteins. A random forest is an ensemble learner that grows classification trees. Each tree produces a classification decision, and an integrated output is calculated. The system classifies the protein-localisation patterns within fluorescent microscope images. 2D images of HeLa cells that include all major classes of subcellular structures, and the associated feature set are used. The performance of the developed system is compared against that of the support vector machine and decision tree approaches. Three experiments are performed to study the influence of the training and test set size on the performance of the examined methods. The calculated classification errors and execution times are presented and discussed. The lowest classification error (2.9%) has been produced by the developed system.

A random forest for lung nodule identification

A method is presented for identification of lung nodules. It includes three stages: image acquisition, background removal, and nodule detection. The first stage improves image quality. The second stage extracts long lobe regions. The third stage detects lung nodules. The method is based on the random forest learner. Training set contains nodule, non-nodule, and false-positive patterns. Test set contains randomly selected images. The developed method is compared against the support vector machine. True-positives of 100% and 85.9%, and false-positives of 1.27 and 1.33 per image were achieved by the developed method and the support vector machine, respectively.

understanding Fiji's declining foreign reserves position

This article examines the volatility in Fiji’s foreign reserves—in particular, whether shocks have an asymmetrical effect on volatility and whether shocks have a  persistent impact on volatility. In the pre-coup period (1975–86), shocks had a temporary effect on volatility; in the coup period (1987–2006), shocks had a more lasting impact. In the pre-coup period, negative shocks contributed more to  foreign reserves volatility than positive shocks; but in the period including the  coups, positive shocks increased the volatility of foreign reserves more than  negative shocks. The reasons for, and the policy implications of, this  asymmetrical behaviour are explored.