Summary 1.Compared with most terrestrial data sets used in development of theoretical models, what characterizes data sets from marine soft sediments is that they have a very high number of species. Marine data also show large numbers of rare species, even if the extent of the study is greatly increased from a few square metres to hundreds of square kilometres. 2. Fitting log-normal distributions to data on species abundance distributions (SADs) for marine benthic assemblages from the continental shelf of Norway, and for the wellstudied tropical tree data of Barro Colorado Island, Panama (BCI), show patterns suggesting that more than one log-normal distribution occurs. 3. We have developed a simple model of SADs comprising two groups of log-normally distributed species, a group of rare species and a group of common species. The model appears to fit the marine data and also applies to the BCI tropical tree data and yet is simpler than Hubbell's Zero-Sum Multinomial (ZSM) distribution model. 4.The plots of the model results show that the marine data are dominated by the rare group of species at all scales studied. In the tropical tree assemblage a similar pattern to that in the marine data is apparent only at small spatial scales (5 and 10 ha), but not at the larger extent (25-50 ha). It is important to note, however, that marine data at all the scales studied are samples from a given area, whereas the terrestrial data are exact counts of all species of tree within the total 50 ha plot studied. 5. Most ecological data are likely to be based on samples rather than exact counts so the patterns found with the marine data are likely to be common, and our findings general ones. 6. A biological explanation for the two-group log-normal model is that recruitment of rare species from outside the studied area dominates benthic assemblages of soft sediments at all sampled scales. Within the tropical forest the dominance of recruitment processes are only apparent at small scales. 7. Thus, the interpretation of SAD patterns depends greatly on the type of data collected, i.e. whether they are exact counts or samples. We believe that structuring processes are similar in marine and many terrestrial systems and these processes probably conform to the mass-effects paradigm of the newly termed meta-community concept.

Summary1. There has been a revival of interest in species abundance distribution (SAD) models, stimulated by the claim that the log-normal distribution gave an underestimate of the observed numbers of rare species in species-rich assemblages. This led to the development of the neutral Zero Sum Multinomial distribution (ZSM) to better fit the observed data.2. Yet plots of SADs, purportedly of the same data, showed differences in frequencies of species and of statistical fits to the ZSM and log-normal models due to the use of different binning methods. 3. We plot six different binning methods for the Barro Colorado Island (BCI) tropical tree data. The appearances of the curves are very different for the different binning methods. Consequently, the fits to different models may vary depending on the binning system used. 4. There is no agreed binning method for SAD plots. Our analysis suggests that a simple doubling of the number of individuals per species in each bin is perhaps the most practical one for illustrative purposes. Alternatively rank-abundance plots should be used. 5. For fitting and testing models exact methods have been developed and application of these does not require binning of data. Errors are introduced unnecessarily if data are binned before testing goodness-of-fit to models.

The determination of predicted no-effect concentrations (PNECs) and sediment quality guidelines (SQGs) of toxic chemicals in marine sediment is extremely important in ecological risk assessment. However, current methods of deriving sediment PNECs or threshold effect levels (TELs) are primarily based on laboratory ecotoxicity bioassays that may not be ecologically and environmentally relevant. This study explores the possibility of utilizing field data of benthic communities and contaminant loadings concurrently measured in sediment samples collected from the Norwegian continental shelf to derive SQGs. This unique dataset contains abundance data for ca. 2200 benthic species measured at over 4200 sampling stations, along with co-occurring concentration data for >25 chemical species. Using barium, cadmium, and total polycyclic aromatic hydrocarbons (PAHs) as examples, this paper describes a novel approach that makes use of the above data set for constructing field-based species sensitivity distributions (f-SSDs). Field-based SQGs are then derived based on the f-SSDs and HCx values [hazardous concentration for x% of species or the (100 − x)% protection level] by the nonparametric bootstrap method. Our results for Cd and total PAHs indicate that there are some discrepancies between the SQGs currently in use in various countries and our field-data-derived SQGs. The field-data-derived criteria appear to be more environmentally relevant and realistic. Here, we suggest that the f-SSDs can be directly used as benchmarks for probabilistic risk assessment, while the field-data-derived SQGs can be used as site-specific guidelines or integrated into current SQGs.