## Introduction

Hubbell (2001) has revived interest in studies of species abundance distributions (SADs) that were popular in the 1960s following the pioneering work of Fisher, Corbet & Williams (1943), Preston (1948, 1962), MacArthur (1957), MacArthur & MacArthur (1961) and Williams (1964). Hubbell's study was based on detailed analyses of SAD patterns of data from tropical trees, mainly from Barro Colorado Island (BCI), Panama (Condit *et al*. 2002). Hubbell's basic thesis was that the SAD patterns did not follow a log-normal distribution as there was an overweight of species with low abundances. He therefore, developed the ZSM model to account for the overabundance. Yet, analyses of purportedly the same data set by McGill (2003) and Volkov *et al*. (2003) showed that a log-normal distribution (Preston 1948), fitted the data as well as the ZSM model. Volkov *et al*. (2003) argued that a slightly lower value of the chi square test for goodness-of-fit argued in favour of the ZSM, but both models fitted within the accepted statistical bounds. However, the plots shown by McGill (2003) and Volkov *et al*. (2003) differed from those shown by Hubbell (2001).

Preston's (1948) method of binning the data to obtain a log-normal was derived before the advent of computers and was aimed at turning discrete data into a continuous distribution. He erected doubling classes of abundances (log_{2}) which he called octaves. While many have claimed to have used Preston's original (1948) plotting method this is not in fact the case and most have used a modified version (described later in the Methods section), which was first suggested by Williams (1964). In addition to making plots of a great variety of data using this modified Preston method Williams (1964) often used another binning method that of × 3 classes to fit a log-normal distribution. Others have used a hybrid method (Gray 1987; Hubbell 2001; Plotkin & Muller-Landau 2002; O’Hara & Oksanen 2003; Chave 2004; Hubbell & Borda-de-Agua 2004). In discussing the log-normal distribution Magurran (2004, p. 32) stated that ‘It is not however, necessary to use log_{2}; any base is valid and log_{3} and log_{10} are common alternatives’. Magurran & Henderson (2003) used the log_{10} base for their analysis of SAD patterns in a fish assemblage from a British estuary. Williamson & Gaston (2005) also plotted data on log_{10} scale using probability plots to compare models. While it is of course reasonable to plot using any logarithmic base, the plots that one obtains will vary not only with the base of the logarithm but also with the type of binning system used (Hubbell & Borda-de-Agua 2004). The consequences of using different binning methods are that different interpretations may be derived for the same data, especially where testing of fit to the model is done after binning. Here we plot the BCI data using a variety of different binning methods and show that the binning methods greatly influence the shapes of the plots produced.