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.

Marine snow studies in the Northeast Atlantic Ocean: distribution, composition and role as a food source for migrating plankton

During a 25 d Lagrangian study in May and June 1990 in the Northeast Atlantic Ocean, marine snow aggregates were collected using a novel water bottle, and the composition was determined microscopically. The aggregates contained a characteristic signature of a matrix of bacteria, cyanobacteria and autotrophic picoplankton with inter alia inclusions of the tintiniid Dictyocysta elegans and large pennate diatoms. The concentration of bacteria and cyanobacteria was much greater on the aggregates than when free-living by factors of 100 to 6000 and 3000 to 2 500 000, respectively, depending on depth. Various species of crustacean plankton and micronekton were collected, and the faecal pellets produced after capture were examined. These often contained the marine snow signature, indicating that these organisms had been consuming marine snow. In some cases, marine snow material appeared to dominate the diet. This implies a food-chain short cut wherby material, normally too small to be consumed by the mesozooplankton, and considered to constitute the diet of the microplankton can become part of the diet of organisms higher in the food-chain. The micronekton was dominated by the amphipod Themisto compressa, whose pellets also contained the marine snow signature. Shipboard incubation experiments with this species indicated that (1) it does consume marine snow, and (2) its gut-passage time is sufficiently long for material it has eaten in the upper water to be defecated at its day-time depth of several hundred meters. Plankton and micronekton were collected with nets to examine their vertical distribution and diel migration and to put into context the significance of the flux of material in the guts of migrants. “Gut flux” for the T. compressa population was calculated to be up to 2% of the flux measured simultaneously by drifting sediment traps and <5% when all migrants are considered. The in situ abundance and distribution of marine snow aggregates (>0.6 mm) was examined photographically. A sharp concentration peak was usually encountered in the depth range 40 to 80 m which was not associated with peaks of in situ fluorescence or attenuation but was just below or at the base of the upper mixed layer. The feeding behaviour of zooplankton and nekton may influence these concentration gradients to a considerable extent, and hence affect the flux due to passive settling of marine snow aggregates.