The power to detect trends in community composition depends on the sample size, the magnitude of the trend, and the magnitude of random variation, just as it does with univariate analyses (Philippi et al. 1998). The present design with five replicate van-Veen grabs at four selected stations se.ems sufficient to follow the local community development. However, as discussed in the previous chapter, single stations do only allow inferences about the local development, which may be only one element in a large scale mosaic cycle. To differentiate between the natural fluctuations of mosaic elements and long term trends, a repeated mapping of the area would be needed (Reise 1991).
For studies of the development of the benthic communities in larger heterogeneous areas, a stratified random sampling is presumably the most powerful design (Krebs 1998). Under the assumption that each stratum represents a homogeneous area (which can adequately be represented by e.g.
a mean density per species), randomly selected stations with only a single replicate per station provide the largest power to detect community differences (Cuff & Coleman 1979; Van der Meer 1997). These strata should not include heterogeneous communities and borders between strata should be avoided, because this would greatly reduce the power to detect changes (Philippi et al.
1998). For marine community studies, formal random selection of sites is less important since the spatially stochastic distribution of populations will probably generate an appropriate randomness from evenly spaced sites (Milne 1959;
Clarke & Green 1988). Random selection may sometimes even lead to an uneven distribution of effort across an area and an appropriate spatial dispersion of sites across the area needs to ascertained (Hurlbert 1984).
Once the stations are selected, the Same stations should be revisited in succeeding years to reduce the confounding effects of spatial variation and increase the power to detect temporal changes (Thrush et al. 1994; Stewart- Oaten et al. 1995; Van der Meer 1997) T i - i i r nppropriate number of stations depends on the size of the area and i~:; ,; :!..:, !;+riability. A larger extent will incorporate greater spatial heterogeneity i kV;.;c "A9; Philippi et al. 1998).
Such a large-scale stratified random sed in the Dutch offshore monitoring program BIOMON (Van d Itmann et al. 1999). This approach includes all spatial variation in the designated strata and therefore leads to very large vai ta especially with small- sized sampling gear (e.g. Holtmann m i e s 2000). Apart from this, it precludes comparisons among I changes in the spatial distribution within the stratum are likely 10 p ; r i b unnoticed. The variability of single grab samples is too high to detect differences between sub-areas except for the most extreme ones as, e.g., between different communities. If any comparison between sampling sites is intended, replicates are needed to assess the within-site similarity between replicates (Clarke & Green 1988;
Philippi et al. 1998). Two replicates are the minimum, but more replicates allow a more precise description of the local community and greatly enhance the
power of comparisons by increasing the possible number of permutations in randomisation tests (see chapter 5.2.2).
A nested design over different spatial scales allows a distinction of the variability and relevant processes at different scales (Morrisey et al. 1992; Thrush et al.
1997; Kendall & Widdicombe 1999; Paiva 2001; Hewitt et al. 2002). In nested designs, it is important to allocate the effort across the levels efficiently to maximise the power for the most important questions of the study. The optimal balance can be calculated when the variability at the respective scales is known (Clarke & Green 1988). A selection of sites for the German Bight could b e realised by analysing the spatial distribution of benthic communities within the designated communities from large scale surveys (Salzwedel et al. 1985;
Rachor & Nehmer 2003) and comparing it to the local variability analysed here.
Time series of benthic communities are mostly characterised by a considerable seasonal variation (Arntz & Rumohr 1982; 1986; Muehlenhardt-Siegel 1988;
Frid et al. 1996; Krönck et al. 1998). For long-term observations of benthic communities, the late-winter or early spring situation is preferred as the communities are reduced to those species that are able to survive in the area over longer periods. In summer many species settle in the area, sometimes in very large numbers, but may not be able to survive until the next year. When interspecific interactions control the community development, the establishment of large densities often of opportunistic species may be impaired or even inhibited (Crowe et al. 1987; Olafsson et al. 1994) and the development may mainly be characterised by density increases of the species already present (Weigelt 1991). While this general pattern occurs in most years, the identity of the successful species varies and is not predictable. It may be a question of which species happens to be in the area at the right time (Reise 1991). This seasonal variation adds to the spatial variation and complicates the identification of long-term trends. While single sampling occasions in early spring are appropriate to document long-term changes in benthic communities, each change observed to the previous year's situation is a result of various processes including recruitment and mortality over the whole year. If inferences about processes were intended, biannual sampling in spring and autumn would be necessary (Alden et al. 1997).
Beyond the description of community variability, inferences about responsible factors can only be made if appropriate data of possibly influential variables are available. As several of these factors also vary on various spatial and temporal scales, measurements should ideally be directly connected to the sampling of the benthic communities. Values for all relevant variables should be determined from each replicate sample (on the appropriate scale for the respective variable), thus matching the biological, physical and chemical data as closely as possible (Clarke & Green 1988).
A general statement about the regional community development beyond the local conditions would require a larger extent of sampling within each community. Any conclusions about changes in the spatial distribution would require several stations with replicate samples. A more functional analysis would only be possible with a higher frequency of sampling to identify the exact timing of changes in benthic communities in relation to fluctuations of environmental variables and a continuous recording of the relevant environmental factors locally at the benthos sampling stations.
If annual sampling of numerous stations is not feasible, information on the long- term development of benthic communities beyond local conditions could be derived by a combination of annual sampling at selected stations with extensive large-scale surveys at longer intervals (e.g. every ten years). A combined analysis of the large-scale spatial and local temporal variability may be Seen as a compromise to investigate the spatial distribution of the communities and their long-term development.
5.7 Open questions and further analyses