Effects of study design and analysis on the spatial community structure detected by multiscale ordination

Question: What are the effects of different aspects of data collection and analysis on the spatial partitioning of ordination results by multiscale ordination? Locations: Heterogeneous pasture at Marchairuz in the Jura mountains, Switzerland and Mixed hardwood-pine forest in Oostings Natural Area, NC, USA. Methods: We evaluated the efficiency of different sampling designs for identifying the spatial structure of plant communities and analysed two datasets with multiscale ordination. We compared the effects of quadrat size, the number of species included in the analysis, data type, detrending and ordination method on the shape and precision of the community variogram summarizing spatial community structure. Results: A three-block sampling design provided a more even distribution of the number of pairs of observation per distance class than random, transect or grid designs. The precision of variogram estimates depended more strongly on the number of species than on the number of quadrats. In contrast, the choice of data type (abundance transformation) had little influence on the shape of the variogram. Detrending reduced the range of spatial autocorrelation. An increase in quadrat size resulted in a smoother variogram and stronger spatial autocorrelation. Principal components analysis (PCA) and Redundancy analysis (RDA) resulted in a larger range of spatial autocorrelation values than did Correspondence analysis ( CA) and Canonical correspondence analysis (CCA), but the shape of the variograms was similar. Conclusion: Random samples as well as transects and regular grids may not be efficient sampling designs for spatial analysis of community structure. While the number of species considered strongly affects the precision of a community variogram, its shape depends on the size of the sampling units. Earlier studies may have overestimated the spatial scale of internal organization in plant communities.