Food web implications

Important consumers of the zooplankton production in the Baltic Sea are a minor number of fish species. Larvae and young-of-year of the main fish species cod (Gadus morhua L.), sprat (Sprattus sprattus L.) and herring (Clupea harengus L.) take up a large portion of the zooplankton production (Arrhenius & Hansson 1993) and their recruitment is related to the availability of these resources. Since the 1980s the diet composition of fish larvae showed distinct changes (Voss et al. 2003), while the stock size of cod decreased and sprat

9 “Entia non sunt multiplicanda praeter necessitatem.” Entities should not be multiplied beyond necessity.

(William of Ockham, 14th century).

creased. In addition to fisheries pressure (Bagge et al. 1994) interactions between the zoo-plankton composition in different strata of the Bornholm Sea and the depth preference of larval fish seems important, as they bias the availability of resources. Cod larvae need higher salinities for development and their main habitat is close to the halocline, where older stages of Pseudocalanus sp., assigned to zooplankton utilisation mode V, are the most important food items (Voss et al. 2003). Adult cod is the main piscivorous fish spe-cies in the central Baltic Sea, with sprat and herring as the dominant prey (Rudstam et al.

1994, Bagge 1989). Young and larval sprat concentrate in shallower layers (Wieland &

Zuzarte 1991, Makarchouk & Hinrichsen 1998, Dickmann et al. submitted). Thus, the diet of larval sprat is dominated by the zooplankton species of the modes I and II, close to the surface. Analyses of larval sprat gut contents show high feeding rates on Acartia spp. and lower on Temora longicornis, while Pseudocalanus sp. is absent in the diet (Voss et al.

2003, Dickmann 2005). This may help to explain, why years with high Acartia spp. abun-dances are correlated with strong year classes of sprat (Dickmann 2005). Another surplus can be the temporal match of the B. coregoni maritima peak (Paper Z3) and the growth of sprat larvae in July, when the larvae become large enough to ingest this cladoceran (Dick-mann 2005). Adult sprat has a shallow vertical distribution in summer, while they concen-trate below 50 m the rest of the year (Köster & Schnack 1994). In these depths sprat com-petes with herring for Pseudocalanus sp. and Temora sp. (Möllmann & Köster 2002). Fur-thermore, both clupeids feed on cod eggs with rates of 20 to 100%, depressing the recruit-ment success of cod (Schnack & Köster 1994). Therefore, an increase in clupeids de-presses the probability of strong year classes of cod and vice versa (Rudstam et al. 1994).

The improved conditions for zooplankton species of the modes I and II, in concert with deteriorating conditions for species of mode V, bears obviously an at least partial explana-tion of the observed ecosystem shift from a gadoid towards a clupeid dominated system as a result of changes in hydrography. Furthermore, the idea of active selection (Viitasalo et al. 2001) appears as not sufficiently concise with the knowledge of the spatial predator-prey overlap. Thus, selection indices calculated from unstratified zooplankton samples not necessarily reflect active behaviour, but rather spatial heterogeneity of prey distribution in relation to the preferred depth of the predators. Preference for more active prey, due to hydrodynamic detection, is obvious (Viitasalo et al. 1998), but needs to consider the re-spective prey field in the vicinity of a predator.

5.2. Investigative approaches

5.2.1. Numerical ecology

The approach of a Multivariate Discriminant Function Analysis (MDFA) was successfully used in this thesis to differentiate between distinct zooplankton assemblage patterns. Today the application of this statistical approach to marine pelagic data is not common and the papers and manuscripts in this study are probably the first published applications in the field of zooplankton ecology. The MDFA turned out to be an adequate method to identify heterogeneities in the vertical distribution of the zooplankton community, in relation to the hydrography of the Bornholm Sea. In contrast to similarity based methods (Clarke &

Warwick 1994) MDFA takes abundance variations into account and is less sensitive to joint absence or presence between two sites (Zuur et al. in press). The method allows de-termining the impact of a single variable and the examination of proportional differences.

Abundance variations are retained and not reduced to binary flags during the identification of similarities (e.g. adjacency matrices and similar approaches, like Jaccard-Index, Sören-sen-Index; confer Legendre & Legendre 1986). As most multivariate methods MDFA re-quires to satisfy initial assumptions and the power of the model is prone to a violation of these. Collinearities may be present when two or more variables respond equally to the same combination of parameters or may be artificially created when integrated or averaged values are linked. Artificial dependencies can result in ill-conditioned matrices and lower the statistical power of a model. Multivariate normality is also seldom achieved in ecologi-cal data and often seems to be ignored. The advantage of the MDFA is that normality is not required for the method itself (Hair et al. 1998). If normality is not achieved the signifi-cance of the model can be determined by other methods, like Wilk’s Lambda (Rao 1951).

The modules included in the software package Ocean Sneaker’s Tool (OST) reflect the requirements that arose during the analysis and visualisation of several parameters pre-sented in this thesis (Paper S1). As the specific needs are not restricted to this thesis it was made available to the public¹⁰. Today OST is referenced by several international institutes, projects and data centres. Among these are the Alfred Wegener Institute¹¹, Censor¹², Pan-gaea¹³ and the Ocean Teacher Program of the Intergovernmental Oceanographic

10 http://www.awi.de/Software/OST

11 http://www.awi.de

12 http://www.censor.name

13 http://www.pangaea.de/

sion/UNESCO¹⁴, where it is listed as one of the official tools to convert different formats of geographic positions (Paper S1). Further developments are in progress and include a numerical library. Emphasis is put on the multivariate investigation of data from the zoo-plankton imaging system under development (Chapter 4.3). This allows combining obser-vations on single species with the ambient environmental conditions. An observation on n variables can be conceived as an object in an n-dimensional space. In terms of species it is able to correlate n ambient environmental parameters for every observation of an individ-ual. Plotted in the multidimensional space observations with similar properties form clus-ters and groups. Significant differences between taxa result in discriminable clusclus-ters in such a space. Along the axes for which a species shows a stenoecious capability small scat-ter is expected, while scatscat-ter increases on axes on which species show euryoecious toler-ances. The fringes of the cluster span the ecological range that a species tolerates on the observed n parameters. Distances between single observations and the centroid of a respec-tive cluster can be expressed as the observations probability to belong to this cluster. Inter-actions between two or more species can only be expected when at least small intersections in the multivariate space exist and allow a more detailed insight than with broad integration intrinsic to sample sets collected by plankton nets.