Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Clustering in non-parametric multivariate analyses.

Non-parametric multivariate analyses of complex ecological datasets are widely used. Following appropriate pre-treatment of the data inter-sample resemblances are calculated using appropriate measures. Ordination and clustering derived from these resemblances are used to visualise relationships among samples (or variables). Hierarchical agglomerative clustering with group-average (UPGMA) linkage is often the clustering method chosen. Using an example dataset of zooplankton densities from the Bristol Channel and Severn Estuary, UK, a range of existing and new clustering methods are applied and the results compared. Although the examples focus on analysis of samples, the methods may also be applied to species analysis. Dendrograms derived by hierarchical clustering are compared using cophenetic correlations, which are also used to determine optimum  in flexible beta clustering. A plot of cophenetic correlation against original dissimilarities reveals that a tree may be a poor representation of the full multivariate information. UNCTREE is an unconstrained binary divisive clustering algorithm in which values of the ANOSIM R statistic are used to determine (binary) splits in the data, to form a dendrogram. A form of flat clustering, k-R clustering, uses a combination of ANOSIM R and Similarity Profiles (SIMPROF) analyses to determine the optimum value of k, the number of groups into which samples should be clustered, and the sample membership of the groups. Robust outcomes from the application of such a range of differing techniques to the same resemblance matrix, as here, result in greater confidence in the validity of a clustering approach.

Why marine phytoplankton calcify

Calcifying marine phytoplankton - coccolithophores - are some of the most successful yet enigmatic organisms in the ocean, and are at risk from global change. In order to better understand how they will be affected we need to know 'why' coccolithophores calcify. Here we review coccolithophorid evolutionary history, cell biology, and insights from recent experiments to provide a critical assessment of the costs and benefits of calcification. We conclude that calcification has high energy demands, and that coccolithophores might have calcified initially to reduce grazing pressure, but that additional benefits such as protection from photo-damage and viral-bacterial attack further explain their high diversity and broad spectrum ecology. The cost-versus-benefit of these traits is illustrated by novel ecosystem modeling, although conclusive observations are still limited. In the future ocean, the trade-off between changing ecological and physiological costs of calcification and their benefits will ultimately decide how this important group is affected by ocean acidification and global warming.

Why marine phytoplankton calcify

Calcifying marine phytoplankton - coccolithophores - are some of the most successful yet enigmatic organisms in the ocean, and are at risk from global change. In order to better understand how they will be affected we need to know 'why' coccolithophores calcify. Here we review coccolithophorid evolutionary history, cell biology, and insights from recent experiments to provide a critical assessment of the costs and benefits of calcification. We conclude that calcification has high energy demands, and that coccolithophores might have calcified initially to reduce grazing pressure, but that additional benefits such as protection from photo-damage and viral-bacterial attack further explain their high diversity and broad spectrum ecology. The cost-versus-benefit of these traits is illustrated by novel ecosystem modeling, although conclusive observations are still limited. In the future ocean, the trade-off between changing ecological and physiological costs of calcification and their benefits will ultimately decide how this important group is affected by ocean acidification and global warming.