Palaeoassocia as methodological tool for phytosociological analyses is further developed

Detailed understanding of the environment surrounding an archaeological site has always been a major goal in archaeobotanical analyses. Various methods have been used to characterize the environment, reaching from indicator species to judge upon specific environmental properties (e.g. salinity, acidity) to indicator values for all species in a spectrum.From an ecological perspective however, a disadvantage of this individualistic approach is that the interplay between plant species is largely ignored. The spatial manifestation and co-occurrence of plant species is known as vegetation. A basic understanding of vegetation is often acquired by grouping species in ‘ecological groups’ based on individual labelling of species as, for example, ‘arable weed’, ‘grassland species’, or ‘ruderal’.Another approach to plant sociology is phytosociology, the study of plant communities (syntaxa). Plant communities, as opposed to ecological groups, are defined on the basis of field observations of co-occurrence of species. Although substantial variation in field methods and research density exists, numerous systematic vegetation recordings (relevés or plots) are available in most countries.In the Netherlands, this adds up to more than 600000. In a previous paper we presented a method to systematically and objectively analyse archaeobotanical datasets (Schepers et al. 2013). We did so by developing an additional function for the associa software package (Van Tongeren et al. 2008). This package assigns plots from field observations to pre-defined and well-established plant communities. This version, palaeoassocia, basically treats archaeobotanical datasets as modern plots, but evidently requires some modifications. A major challenge to overcome is that the vast majority of archaeobotanical assemblages contain plant species from various environmental origins. The 2013 version is very time-consuming, requires substantial practice and experience, and was therefore potentially subject to interpersonal differences. Thus, we felt the need to develop a more automated, user-friendly, and faster version. A rich dataset from the Southern Netherlands village of Best, provided an excellent test set for this further development.