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Turning to Traits to Understand Phytoplankton Biomass Patterns
The Irwin Group uses statistical modeling techniques to understand niches, traits, and interactions within marine microbial communities. A new paper confirms the value of trait-based approaches in investigating the mechanisms underlying phytoplankton biomass dynamics, and in predicting the community response to environmental changes. Continue reading “Turning to Traits to Understand Phytoplankton Biomass Patterns”
NEW CBIOMES PUBLICATION
Mutshinda. C.M., Finkel, Z.V., Widdicombe, C.E. & Irwin, A.J. (2020), A trait-based taxonomy for phytoplankton biomass modeling and prediction, Diversity, doi: 10.3390/d12080295
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2020 Annual Meeting e-poster: A Trait-based Clustering for Phytoplankton Biomass Modeling and Prediction – Crispin Mutshinda (Dal)
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May 2020 CBIOMES e-meeting – Crispin Mutshinda (Dal)
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NEW CBIOMES PUBLICATION
Crispin M. Mutshinda, Andrew J. Irwin, Mikko J. Sillanpää (2020), A Bayesian Framework for Robust Quantitative Trait Locus Mapping and Outlier Detection, The International Journal of Biostatistics, doi: 10.1515/ijb-2019-0038
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NEW CBIOMES PUBLICATION
C. Mutshinda, C. Widdicombe, Z.V. Finkel, A.J. Irwin (2019), Bayesian inference to partition determinants of community dynamics from observational time series, Community Ecology, doi: 10.1556/168.2019.20.3.4
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CBIOMES at CMStatistics2019
In December, several from CBIOMES traveled to London, England, to present their work at CMStatistics2019, a conference organized by the European Research Consortium for Informatics (ERCIM) Working Group on Computational and Methodological Statistics (CMStatistics) and Birkbeck University of London. Continue reading “CBIOMES at CMStatistics2019”