A comparison of two methods for generating artificial multi-assemblage ecological datasets

Simulated ecological datasets have been widely used to assess the ability of ordination techniques to portray patterns in ecological assemblage data. Such datasets typically contain a single assemblage sampled over an environmental gradient or set of gradients. Little has been done on the generation of artificial datasets that contain a number of different species assemblages, to aid in the evaluation of multivariate techniques that test for differences between assemblages of species. This paper describes and compares two simulation methods that generate ecologically realistic artificial multi-assemblage datasets. Both methods provide multivariate data (e.g. species abundances) for replicate sites within discretely different assemblages. The first technique is a coenocline model based on species' responses to variation modeled by a five-parameter β-function, where variation in species abundances both within and between assemblages is governed by differences in the positions of sites and assemblages along environmental gradients. The second technique, the resampling method, involves bootstrap resampling of real assemblage datasets, with the addition of selected types of controlled differences between assemblages. Here we use it to generate turnover in species composition. We calibrate both simulation methods based on a field assemblage of bird species. The two different simulation methods portray different levels and types of between-assemblage variation. The resampling method allows greater control over some aspects of assemblage difference (e.g. independently varying differences in species richness and compositional turnover) than the coenocline method. Both can generate usable replicated simulated datasets for assessing the ability of multivariate tests to detect ecological variation among assemblages. Crown