Species abundance distributions (SADs) follow one of ecologyÕs oldest and most universal laws -every community shows a hollow curve or hyperbolic shape on a histogram with many rare species and just a few common species. Here, we review theoretical, empirical and statistical developments in the study of SADs. Several key points emerge. (i) Literally dozens of models have been proposed to explain the hollow curve. Unfortunately, very few models are ever rejected, primarily because few theories make any predictions beyond the hollow-curve SAD itself. (ii) Interesting work has been performed both empirically and theoretically, which goes beyond the hollow-curve prediction to provide a rich variety of information about how SADs behave. These include the study of SADs along environmental gradients and theories that integrate SADs with other biodiversity patterns. Central to this body of work is an effort to move beyond treating the SAD in isolation and to integrate the SAD into its ecological context to enable making many predictions. (iii) Moving forward will entail understanding how sampling and scale affect SADs and developing statistical tools for describing and comparing SADs. We are optimistic that SADs can provide significant insights into basic and applied ecological science.

Summary 1.One of the general characteristics of ecological communities is that the number of species accumulates with increasing area sampled. However, it is important to distinguish between the species-area relationship and species accumulation curves. The species-area relationship is concerned with the number of species in areas of different size irrespective of the identity of the species within the areas, whereas the species accumulation curve is concerned with accumulation rates of new species over the sampled area and depends on species identity. 2. We derive an exact analytical expression for the expectance and variance of the speciesaccumulation curve in all random subsets of samples from a given area. The analytical species accumulation curve may be approximated by a semilog curve. Both the exact accumulation curve and its semilog approximation are independent of the underlying species abundance distributions, but are influenced strongly by the distribution of species among the samples and the spatial relationship of the samples that are randomized. 3. To estimate species richness in larger areas than that sampled we take account of the spatial relationship between samples by dividing the sampled area into subareas. First a species-accumulation curve is obtained for randomized samples of all the single subareas. Then the species-accumulation curve for all combinations of two subareas is calculated and the procedure is repeated for all subareas. From these curves a new total species (T-S) curve is obtained from the terminal point of the subarea plots. The T-S curve can then be extrapolated to estimate the probable total number of species in the area studied. 4. Data from the Norwegian continental shelf show that extrapolation of the traditional species-accumulation curve gave a large underestimate of total species richness for the whole shelf compared with that predicted by the T-S curve. Application of nonparametric methods also gave large underestimates compared with actual data obtained from more extensive sampling than the data analysed here. Although marine soft sediments sampled in Hong Kong were not as variable as those from the Norwegian shelf, nevertheless here the new method also gave higher estimates of total richness than the traditional species-accumulation approaches. 5. Our data show that both the species-accumulation curve and the accompanying T-S curve apply to large heterogeneous areas varying in depth and sediment properties as well as a relatively small homogeneous area with small variation in depth and sediment properties.

The species–abundance distribution (SAD) describes the abundances of all species within a community. Many different models have been proposed to describe observed SADs. Best known are the logseries, the lognormal, and a variety of niche division models. They are most often visualized using either species richness – log abundance class (Preston) plots or abundance – species rank order (Whittaker) plots. Because many of the models predict very similar shapes, model distinction and testing become problematic. However, the variety of models can be classified into three basic types: one that predicts a double S‐shape in Whittaker plots and a unimodal distribution in Preston plots (the lognormal type), a second that lacks the mode in Preston plots (the logseries type), and a third that predicts power functions in both plotting types (the power law type). Despite the interest of ecologists in SADs no formal meta‐analysis of models and plotting types has been undertaken so far. Here we use a compilation of 558 species–abundance distributions from 306 published papers to infer the frequency of the three SAD shapes in dependence of environmental variables and type of plotting. Our results highlight the importance of distinguishing between fully censused and incompletely sampled communities in the study of SADs. We show that completely censused terrestrial or freshwater animal communities tend to follow lognormal type SADs more often than logseries or power law types irrespective of species richness, spatial scale, and geographic position. However, marine communities tend to follow the logseries type, while plant communities tend to follow the power law. In incomplete sets the power law fitted best in Whittaker plots, and the logseries in Preston plots. Finally our study favors the use of Whittaker over Preston plots.

We investigated changes in the root-associated fungal communities associated with the ectomycorrhizal herb Bistorta vivipara along a primary succession gradient using 454 amplicon sequencing. Our main objective was to assess the degree of variation in fungal richness and community composition as vegetation cover increases along the chronosequence. Sixty root systems of B. vivipara were sampled in vegetation zones delimited by dated moraines in front of a retreating glacier in Norway. We extracted DNA from rinsed root systems, amplified the ITS1 region using fungal-specific primers and analysed the amplicons using 454 sequencing. Between 437 and 5063 sequences were obtained from each root system. Clustering analyses using a 98.5% sequence similarity cut-off yielded a total of 470 operational taxonomic units (OTUs), excluding singletons. Between eight and 41 fungal OTUs were detected within each root system. Already in the first stage of succession, a high fungal diversity was present in the B. vivipara root systems. Total number of OTUs increased significantly along the gradient towards climax vegetation, but the average number of OTUs per root system stayed unchanged. There was a high patchiness in distribution of fungal OTUs across root systems, indicating that stochastic processes to a large extent structure the fungal communities. However, time since deglaciation had impact on the fungal community structure, as a systematic shift in the community composition was observed along the chronosequence. Ectomycorrhizal basidiomycetes were the dominant fungi in the roots of B. vivipara, when it comes to both number of OTUs and number of sequences.

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