Does Geijera parviflora Lindl. (Rutaceae) facilitate understorey species in semi-arid Australia?
- Warnock, Andrew, Westbrooke, Martin, Florentine, Singarayer, Hurst, Cameron
- Authors: Warnock, Andrew , Westbrooke, Martin , Florentine, Singarayer , Hurst, Cameron
- Date: 2007
- Type: Text , Journal article
- Relation: Rangeland Journal Vol. 29, no. 2 (2007), p. 207-216
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- Description: Species composition under tree canopies often differs from that of surrounding micro-environments. In arid and semi-arid zones, trees can be beneficial to understorey vegetation. This study examined zones of vegetation composition and soil physiochemical parameters associated with Geijera parviflora Lindl. The importance of shade, rainfall redistribution, seed bank and soil moisture were examined. Species abundance, soil moisture, seed bank composition, rainfall redistribution and soil nutrient concentration were measured under five randomly selected mature G. parviflora trees in south-western New South Wales, Australia. To complement the findings from this study, artificial shade plots were constructed in a canopy-free area and species abundance measured seven months after shade construction. The study demonstrated that G. parviflora was associated with zonation of understorey vegetation. Two zones of understorey vegetation were found in relation to G. parviflora: (i) under the tree canopy with high species diversity, and (ii) beyond the canopy, this community being dominated by Dissocarpus paradoxus throughout the year with Crassula colorata appearing after rainfall. The zone beyond the canopy also had lower soil nutrient concentrations. Soil moisture, nutrient concentration and the seed bank density were significantly higher under the canopy. However, the canopy reduced precipitation reaching the soil surface. The effects of the canopy on understorey species composition and soil moisture were enhanced after winter rainfall. Artificial shade increased species abundance and richness under a 90%-shading treatment. The results indicated that G. parviflora generated spatial heterogeneity over the broader plant community increasing species richness, abundance and diversity under the canopy. This emphasises the importance of arid zone trees in conserving understorey plant diversity. Shading, soil nutrient concentration and increased seed bank density and soil moisture appeared to be key influences on the plant communities under the canopy. © Australian Rangeland Society 2007.
- Description: C1
- Description: 2003004817
- Authors: Warnock, Andrew , Westbrooke, Martin , Florentine, Singarayer , Hurst, Cameron
- Date: 2007
- Type: Text , Journal article
- Relation: Rangeland Journal Vol. 29, no. 2 (2007), p. 207-216
- Full Text:
- Reviewed:
- Description: Species composition under tree canopies often differs from that of surrounding micro-environments. In arid and semi-arid zones, trees can be beneficial to understorey vegetation. This study examined zones of vegetation composition and soil physiochemical parameters associated with Geijera parviflora Lindl. The importance of shade, rainfall redistribution, seed bank and soil moisture were examined. Species abundance, soil moisture, seed bank composition, rainfall redistribution and soil nutrient concentration were measured under five randomly selected mature G. parviflora trees in south-western New South Wales, Australia. To complement the findings from this study, artificial shade plots were constructed in a canopy-free area and species abundance measured seven months after shade construction. The study demonstrated that G. parviflora was associated with zonation of understorey vegetation. Two zones of understorey vegetation were found in relation to G. parviflora: (i) under the tree canopy with high species diversity, and (ii) beyond the canopy, this community being dominated by Dissocarpus paradoxus throughout the year with Crassula colorata appearing after rainfall. The zone beyond the canopy also had lower soil nutrient concentrations. Soil moisture, nutrient concentration and the seed bank density were significantly higher under the canopy. However, the canopy reduced precipitation reaching the soil surface. The effects of the canopy on understorey species composition and soil moisture were enhanced after winter rainfall. Artificial shade increased species abundance and richness under a 90%-shading treatment. The results indicated that G. parviflora generated spatial heterogeneity over the broader plant community increasing species richness, abundance and diversity under the canopy. This emphasises the importance of arid zone trees in conserving understorey plant diversity. Shading, soil nutrient concentration and increased seed bank density and soil moisture appeared to be key influences on the plant communities under the canopy. © Australian Rangeland Society 2007.
- Description: C1
- Description: 2003004817
A new scoring system in Cystic Fibrosis : Statistical tools for database analysis - A preliminary report
- Hafen, Gaudenz, Hurst, Cameron, Yearwood, John, Smith, Julie, Dzalilov, Zari, Robinson, P. J.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
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- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
- Authors: Hafen, Gaudenz , Hurst, Cameron , Yearwood, John , Smith, Julie , Dzalilov, Zari , Robinson, P. J.
- Date: 2008
- Type: Text , Journal article
- Relation: BMC Medical Informatics and Decision Making Vol. 8, no. 44 (2008), p.1-11
- Full Text:
- Reviewed:
- Description: Background. Cystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system. Methods. The evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets. Results. (1) Feature selection: CAP has a more effective "modelling" focus than DA. (2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males. Conclusion. Our preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset. © 2008 Hafen et al; licensee BioMed Central Ltd.
Quantification of intermarket influence on the Australian All Ordinary Index based on optimization techniques
- Tilakaratne, Chandima, Morris, Sidney, Mammadov, Musa, Hurst, Cameron
- Authors: Tilakaratne, Chandima , Morris, Sidney , Mammadov, Musa , Hurst, Cameron
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at CTAC 2006: The 13th Biennial Computational Techniques and Applications Conference p. 42-49
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- Authors: Tilakaratne, Chandima , Morris, Sidney , Mammadov, Musa , Hurst, Cameron
- Date: 2007
- Type: Text , Conference paper
- Relation: Paper presented at CTAC 2006: The 13th Biennial Computational Techniques and Applications Conference p. 42-49
- Full Text:
- Reviewed:
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