Phylogeographic differentiation in the mitochondrial control region in the koala, Phascolarctos cinereus (Goldfuss 1817)

The koala, Phascolarctos cinereus, is a geographically widespread species endemic to Australia, with three currently recognized subspecies: P.c. adustus, P.c. cinereus, and P.c. victor. Intraspecific variation in the mitochondrial DNA (mtDNA) control region was examined in over 200 animals from 16 representative populations throughout the species' range. Eighteen different haplotypes were defined in the approximate to 860 bp mtDNA control region as determined by heteroduplex analysis/temperature gradient gel electrophoresis (HDA/TGGE). Any single population typically possessed only one or two haplotypes yielding an average within-population haplotypic diversity of 0.180 +/- 0.003, and nucleotide diversity of 0.16%. Overall, mtDNA control region sequence diversity between populations averaged 0.67%, and ranged from 0% to 1.56%. Nucleotide divergence between populations averaged 0.51%, and ranged from 0% to 1.53%. Neighbour-joining methods revealed limited phylogenetic distinction between geographically distant populations of koalas, and tentative support for a single evolutionarily significant unit (ESU). This is consistent with previous suggestions that the morphological differences formalized by subspecific taxonomy may be interpreted as clinal variation. Significant differentiation in mtDNA-haplotype frequencies between localities suggested that little gene now currently exists among populations. When combined with microsatellite analysis, which has revealed substantial differentiation among koala populations, we conclude that the appropriate short-term management unit (MU) for koalas is the local population.

Critical comparison of Julius Kruttschnitt Mineral Research Centre (JKMRC) blast fragmentation models

Blast fragmentation can have a significant impact on the profitability of a mine. An optimum run of mine (ROM) size distribution is required to maximise the performance of downstream processes. If this fragmentation size distribution can be modelled and controlled, the operation will have made a significant advancement towards improving its performance. Blast fragmentation modelling is an important step in Mine to Mill™ optimisation. It allows the estimation of blast fragmentation distributions for a number of different rock mass, blast geometry, and explosive parameters. These distributions can then be modelled in downstream mining and milling processes to determine the optimum blast design. When a blast hole is detonated rock breakage occurs in two different stress regions - compressive and tensile. In the-first region, compressive stress waves form a 'crushed zone' directly adjacent to the blast hole. The second region, termed the 'cracked zone', occurs outside the crush one. The widely used Kuz-Ram model does not recognise these two blast regions. In the Kuz-Ram model the mean fragment size from the blast is approximated and is then used to estimate the remaining size distribution. Experience has shown that this model predicts the coarse end reasonably accurately, but it can significantly underestimate the amount of fines generated. As part of the Australian Mineral Industries Research Association (AMIRA) P483A Mine to Mill™ project, the Two-Component Model (TCM) and Crush Zone Model (CZM), developed by the Julius Kruttschnitt Mineral Research Centre (JKMRC), were compared and evaluated to measured ROM fragmentation distributions. An important criteria for this comparison was the variation of model results from measured ROM in the-fine to intermediate section (1-100 mm) of the fragmentation curve. This region of the distribution is important for Mine to Mill™ optimisation. The comparison of modelled and Split ROM fragmentation distributions has been conducted in harder ores (UCS greater than 80 MPa). Further work involves modelling softer ores. The comparisons will be continued with future site surveys to increase confidence in the comparison of the CZM and TCM to Split results. Stochastic fragmentation modelling will then be conducted to take into account variation of input parameters. A window of possible fragmentation distributions can be compared to those obtained by Split . Following this work, an improved fragmentation model will be developed in response to these findings.