Laboratory Simulation Of Chemical Processes Induced By Estuarine Mixing - The Behavior Of Iron And Phosphate In Estuaries

A series of well stirred tank reactors has been shown to provide an adaptable laboratory analogue of a one-dimensional estuarine mixing profile which can be applied dynamically to the study of the chemistry of estuarine mixing. Simulations of the behaviour of iron and phosphate in the low salinity region of an estuary have been achieved with this system. The well documented general features of iron removal, involving rapid aggregation of river-borne colloids, were reproduced. Phosphate removal is attributable in part to the coagulation process, although specific adsorption of phosphate by colloids also appears to be significant.

A new procedure to optimize the selection of groups in a classification tree

Agglomerative cluster analyses encompass many techniques, which have been widely used in various fields of science. In biology, and specifically ecology, datasets are generally highly variable and may contain outliers, which increase the difficulty to identify the number of clusters. Here we present a new criterion to determine statistically the optimal level of partition in a classification tree. The criterion robustness is tested against perturbated data (outliers) using an observation or variable with values randomly generated. The technique, called Random Simulation Test (RST), is tested on (1) the well-known Iris dataset [Fisher, R.A., 1936. The use of multiple measurements in taxonomic problems. Ann. Eugenic. 7, 179–188], (2) simulated data with predetermined numbers of clusters following Milligan and Cooper [Milligan, G.W., Cooper, M.C., 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179] and finally (3) is applied on real copepod communities data previously analyzed in Beaugrand et al. [Beaugrand, G., Ibanez, F., Lindley, J.A., Reid, P.C., 2002. Diversity of calanoid copepods in the North Atlantic and adjacent seas: species associations and biogeography. Mar. Ecol. Prog. Ser. 232, 179–195]. The technique is compared to several standard techniques. RST performed generally better than existing algorithms on simulated data and proved to be especially efficient with highly variable datasets.