Phylogenetic relationships and uncertain taxonomy of Pedomicrobium species

The phylogenetic relationships among the species of the genus Pedomicrobium were studied by comparing their 16S rRNA sequences, The Pedomicrobium species form a coherent phylogenetic cluster within the genera of the hyphal budding bacteria in the alpha-Proteobacteria. The sequences of two strains of Pedomicrobium australicum were obtained from DNAs extracted from nonviable freeze-dried cells, which are the only source of material available, and were found to be almost identical (level of similarity, 99.9%), Overall, the Pedomicrobium species are closely related, with sequence similarities ranging from 96.2 to 99.9%, Pedomicrobium manganicum is phylogenetically the most distantly related species and exhibits the lowest similarity (96.2%) with Pedomicrobium americanum, Australian isolate Pedomicrobium sp, strain ACM 3067, P, americanum, and P, australicum are all very highly related, with similarities greater than 99%, Pedomicrobium sp, strain ACM 3067 is most closely related to P, australicum (level of similarity, 99.6%) and P, americanum (99.4%), These manganese-oxidizing species are more closely related to the iron-oxidizing species Pedomicrobium ferrugineum than to the other manganese-oxidizing species, P, manganicum. Taxonomic uncertainties resulting from the loss of the type culture of P, australicum are discussed.

Predicting relationships between speed and accuracy of targetting movements is important

While explaining a large proportion of any variance, accounts of the speed and accuracy of targetting movements use techniques (e.g., log transforms) that typically reduce variability before ''explaining'' the data. Therefore the predictive power of such accounts are important. We consider whether Plamondon's model can account for kinematics of targetting movements of clinical populations.

The role of integrated information acquisition and management in the analysis of coastal ecosystem change

This book chapter represents a synthesis of the work which started in my PhD and which has been the conceptual basis for all of my research since 1993. The chapter presents a method for scientists and managers to use for selecting the type of remotely sensed data to use to meet their information needs associated with a mapping, monitoring or modelling application. The work draws on results from several of my ARC projects, CRC Rainforest and Coastal projects and theses of P.Scarth , K.Joyce and C.Roelfsema.