When trying to visualize how sites vary in terms of species composition, for too long ecologists have been using distance-based methods of unconstrained ordination such as NMDS and CA, with little but precedence to guide them on what dissimilarity measure to use and what transformation and/or standardization to apply.
In collaboration with some folks in New Zealand and Finland, we've been working on a couple of model-based approaches to unconstrained ordination, which offer several advantages such as explicitly accounting for key properties of the data and model variable tools to select key aspects of the analysis. Simulations also show our proposed methods either perform the same or way better than distance-based approaches at the getting the ordinations correct!
Check out our manuscript, now available for early view at:
http://onlinelibrary.wiley.com/doi/10.1111/2041-210X.12236/abstract
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