Various methods have been used to divide communities into core species and occasional or satellite species. Some methods are somewhat arbitrary and there is evidence that many communities are more multi-modal than bimodal. They also tend to rely on having multiple years of data. A completely novel method is presented that not only has no requirement for long-term datasets but can divide communities into multiple groups. It is based on probability a species is present, calculated using Simpson's index and the sequential removal of species from the data. The sequential Simpson’s index method was applied to species data from a grassland insect community. It was also applied to eleven other datasets that had been divided into core and occasional species in previously published studies. The new method was found not only to be consistent with previous core-occasional assessments but was able to identify multimodality in species-abundance distributions. Although ideally used with a measure of persistence (frequency of occurrence) to rank species, community structure is consistently described even with only species abundance data. The method can be applied to short or long-term datasets, help identify multimodality and provide a valuable insight into how communities change in time or space.

The dataset was collected between 2002 and 2005, as part of the Ag-Biota Project. Samples of Hemiptera and Coleoptera were collected using a Vortis suction sampler from a cattle grazed grassland at Teagasc Grange, Ireland. Specimens were identified to species level in most cases, although some to genus level, or morphospecies. The dataset consists of numbers of individuals of each species collected. Rows represent separate samples, with each row having a unique combination of sample reference number, experiment reference number and date. Further details of sampling can be found in:

Helden, A. J., Anderson, A., Finn, J., & Purvis, G. (2015). The response of sward-dwelling arthropod communities to reduced grassland management intensity in pastures. Irish Journal of Agricultural and Food Research, 54(2), 107–120. https://doi.org/10.1515/ijafr-2015-0012