Svalbard 2010 mesocosm experiment: Protistan diversity

Hierarchical clustering. Taxonomic assignment of reads was performed using a preexisting database of SSU rDNA sequences from including XXX reference sequences generated by Sanger sequencing. Experimental amplicons (reads), sorted by abundance, were then concatenated with the reference extracted sequences sorted by decreasing length. All sequences, experimental and referential, were then clustered to 85% identity using the global alignment clustering option of the uclust module from the usearch v4.0 software (Edgar, 2010). Each 85% cluster was then reclustered at a higher stringency level (86%) and so on (87%, 88%,.) in a hierarchical manner up to 100% similarity. Each experimental sequence was then identified by the list of clusters to which it belonged at 85% to 100% levels. This information can be viewed as a matrix with the lines corresponding to different sequences and the columns corresponding to the cluster membership at each clustering level. Taxonomic assignment for a given read was performed by first looking if reference sequences clustered with the experimental sequence at the 100% clustering level. If this was the case, the last common taxonomic name of the reference sequence(s) within the cluster was used to assign the environmental read. If not, the same procedure was applied to clusters from 99% to 85% similarity if necessary, until a cluster was found containing both the experimental read and reference sequence(s), in which case sequences were taxonomically assigned as described above.

Environmental variables and multivariate seabed classification maps via k means clustering of Potter Cove, Antarctica, link to input and result files in GeoTIFF format

This study subdivides the Potter Cove, King George Island, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis includes in total 42 different environmental variables, interpolated based on samples taken during Australian summer seasons 2010/2011 and 2011/2012. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared and the most reasonable method has been applied. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested and 4, 7, 10 as well as 12 were identified as reasonable numbers for clustering the Potter Cove. Especially the results of 10 and 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.