Affiliation: Queensland University of Technology
Location: Brisbane, Australia
Computational and Statistical Ecology
- Comment on "Genetic structure of human populations"Laurent Excoffier
Computational and Molecular Population, Genetics LabZoological Institute, University of Bern, Batzerstrasse 63012, Bern, Switzerland
Science 300:1877; author reply 1877. 2003
- Bayesian estimation of recent migration rates after a spatial expansionGrant Hamilton
Computational and Molecular Population Genetics Lab, Zoological Institute, University of Bern, Switzerland
Genetics 170:409-17. 2005..Estimates based on both markers suggest that expansion occurred <10,000 years ago, after the most recent glaciation, and that migration rates are strongly male biased...
- Molecular analysis reveals tighter social regulation of immigration in patrilocal populations than in matrilocal populationsGrant Hamilton
Computational and Molecular Population Genetics Laboratory, Zoological Institute, University of Bern, Baltzerstrasse 6, 3012 Bern, Switzerland
Proc Natl Acad Sci U S A 102:7476-80. 2005..This finding is compatible with the hypothesis that men are strictly controlling male immigration and promoting female immigration in patrilocal populations and that immigration is much less regulated in matrilocal populations...
- An Integrated Bayesian Network approach to Lyngbya majuscula bloom initiationSandra Johnson
Queensland University of Technology, Brisbane, Australia
Mar Environ Res 69:27-37. 2010..The merger of multiple models which explore different aspects of the problem through an IBN approach can apply to many multi-faceted environmental problems...
- Bayesian model averaging for harmful algal bloom predictionGrant Hamilton
School of Natural Resource Sciences, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia
Ecol Appl 19:1805-14. 2009..This study demonstrates the power of BMA as an important management tool that is capable of high predictive performance while fully accounting for both parameter and model uncertainty...
- Improving detection probabilities for pests in stored grainDavid Elmouttie
Discipline of Biogeosciences, Queensland University of Technology, Brisbane, Queensland, Australia
Pest Manag Sci 66:1280-6. 2010..In this paper, a sampling methodology is demonstrated that accounts for the heterogeneous distribution of insects in bulk grain...