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We measured relative bacterial and archaeal richness and its covariation with physical and other biological parameters during six cruises in the North Sea over prokaryotic productivity levels ranging over two orders of magnitude. Relative bacterial and archaeal richness was estimated as the number of peaks detected by terminalrestriction fragment length polymorphism (T-RFLP) analysis of polymerase chain reaction-amplified prokaryotic 16S rRNA gene fragments in unfiltered (total community) and 0.8-m filtered seawater (free-living community). Relative bacterial richness ranged from 20 to 57 peaks in the total community and from 16 to 56 peaks in the freeliving community. Relative archaeal richness varied between 2 and 14 peaks in the total community and between 2 and 21 peaks in the free-living community. Coamplified plastid DNA might have influenced relative bacterial richness in unfiltered but not in 0.8-m filtered seawater. Relative bacterial richness decreased with viral abundance and total and cell-specific prokaryotic production in the free-living and the total community. Stepwise multiple regression analysis revealed that temperature also influenced relative bacterial richness. Relative archaeal richness was not related to any other parameter. The data suggest that high prokaryotic productivity was sustained by a relatively small number of highly active bacterial populations that also maintained high viral abundance.The advent of molecular techniques prompted the discovery of highly diverse prokaryotic communities in aquatic environments. As a consequence, the question arose how such high prokaryotic diversity can be maintained in relatively homogenous aquatic environments with only a limited number of resources. This problem was first noted for phytoplankton and later extended to bacterioplankton. However, it also has been argued that the high complexity of dissolved organic matter and the large number of different metabolic pathways of prokaryotes allow for a large number of niches and, thus, offer the possibility of an alternative theory where high prokaryotic diversity is maintained by substrate diversity (Thingstad 2000). Three major factors are thought to regulate the composition of bacterioplankton: the availability of resources, size-selective grazing on prokaryotes mainly by heterotrophic nanoflagellates, and viral lysis. The influence of protozoan grazing and the availability of resources on the composition of prokaryotic communities have been demonstrated by a number of studies (e.g., Š imek et al. 2003). Also, prokaryotic communities can be affected by AcknowledgmentsWe thank the captain and the crew of R/V Pelagia for their support at sea. We thank Martien Baars and Corina Brussaard for their efforts as cruise leaders and the opportunity to join their cruises and Maite Pérez, Ingrid Obernosterer, and Thomas Reinthaler for contributing to the prokaryotic production measurements. Frede Thingstad kindly provided suggestions on a draft of the manuscript. Two anonymous reviewers provided v...

The temporal variability of the viral impact on bacterioplankton during the summer-winter transition in the North Sea was determined and artificial neural networks (ANNs) were developed to predict viral production and the frequency of infected bacterial cells (FIC). Viral production and FIC were estimated using a virus-dilution approach during four cruises in the southern North Sea between July and December 2000 and an additional cruise in June 2001. Supplementary data such as bacterial production, and bacterial and viral abundance were collected to relate changes in FIC and viral production to the dynamics of other biotic parameters. Average viral abundance varied between 4.4 x 10(6) ml(-1) in December and 29.8 x 10(6) ml(-1) in July. Over the seasonal cycle, viral abundance correlated best with bacterial production. Average bacterial abundance varied between 0.5 x 10(6) ml(-1) in December and 1.3 x 10(6) ml(-1) in July. Monthly average values of FIC ranged from 9% in September to 39% in June and the average viral production from 11 x 10(4) ml(-1) h(-1) in December to 35 x 10(4) ml(-1) h(-1) in July. The data set was used to develop ANN-based models of viral production and FIC. Viral production was modelled best using sampling time, and bacterial and viral abundance as input parameters to an ANN with two hidden neurons. Modelling of FIC was performed using bacterial production as an additional input parameter for an ANN with three hidden neurons. The models can be used to simulate viral production and FIC based on regularly recorded and easily obtainable parameters such as bacterial production, bacterial and viral abundance.