The importance of different abiotic and biotic drivers on wet heathland vegetation is investigated using a spatial and temporal structural equation model in a hierarchical Bayesian framework. Ecological data from 39 Danish sites, each with several wet heathlands plots, were sampled in the period from 2007 to 2014. Including resampling over the years a total of 1322 plots was sampled. The cover of different plant species was measured using the pin-point method and the joint distribution of the key plant species in the wet heathland ecosystem, Erica tetralix, Calluna vulgaris, and Molinia caerulea was estimated assuming a Dirichlet-multinomial mixture distribution. The investigated selected drivers of wet heathland vegetation include nitrogen deposition, soil type, pH, precipitation and grazing.
The results suggest that soil type is an important factor underlying the current changes in wet heathland plant communities, and, more surprisingly, that grazing had a negative effect on the abundance of E. tetralix. Generally, the study demonstrated that important insight of ecosystem dynamics and regulation may be obtained by spatial and temporal structural equation modelling in a hierarchical Bayesian framework, and that the proper statistical modelling of the joint species abundance is a key feature of such models.