The importance of forest area and configuration relative to local habitat factors for conserving forest mammals: A case study of koalas in Queensland, Australia

The loss and fragmentation of forest habitats by human land use are recognised as important factors influencing the decline of forest-dependent fauna. Mammal species that are dependent upon forest habitats are particularly sensitive to habitat loss and fragmentation because they have highly specific habitat requirements, and in many cases have limited ability to move through and utilise the land use matrix. We addressed this problem using a case study of the koala (Phascolarctos cinereus) surveyed in a fragmented rural-urban landscape in southeast Queensland, Australia. We applied a logistic modelling and hierarchical partitioning analysis to determine the importance of forest area and its configuration relative to site (local) and patch-level habitat variables. After taking into account spatial auto-correlation and the year of survey, we found koala occurrence increased with the area of all forest habitats, habitat patch size and the proportion of primary Eucalyptus tree species; and decreased with mean nearest neighbour distance between forest patches, the density of forest patches, and the density of sealed roads. The difference between the effect of habitat area and configuration was not as strong as theory predicts, with the configuration of remnant forest becoming increasingly important as the area of forest habitat declines. We conclude that the area of forest, its configuration across the landscape, as well as the land use matrix, are important determinants of koala occurrence, and that habitat configuration should not be overlooked in the conservation of forest-dependent mammals, such as the koala. We highlight the implications of these findings for koala conservation. (c) 2006 Elsevier Ltd. All rights reserved.

BM+-tree: A hyperplane-based index method for high-dimensional metric spaces

In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency.

Virtual teachers: Negotiating new spaces for teaching bodies

In 2002, the authors reviewed the educational performance of a state education department virtual schooling service during its first 2 years of operation, 2000-2001 (Pendergast, Kapitzke, Land, Luke, & Bahr, 2002). Established by Education Queensland, the Virtual Schooling Service (VSS) utilises synchronous and asynchronous online delivery strategies and a range of learning technologies to support students at a distance (see http://education.qld.gov.au/learningplace/vss/). The service commenced with a focus on senior secondary subjects. At present, there are over 700 students in 89 schools across the state enrolled in 9 subjects. In response to the recommendations of the study, a series of professional development activities were conducted with the VSS teachers by the authors. Opportunity for critical reflection was provided, including consideration of the ways in which the teachers were developing as a learning community. Some data, including visual representations, were collected from participants with the purpose of understanding how VSS teachers are constructed as professionals. This study compares and contrasts that data with self-constructions of teacher professionals in other fields.