Change in rotated NMDS scores as a response variable
On Fri, 2011-02-18 at 10:41 -0800, Erik Frenzel wrote:
Hello all, I'm interested in adapting a technique from a recent paper Harrison, S., E. I. Damschen and J. B. Grace 2010. Ecological contingency in the effects of climate change on forest herbs. Proceedings of the National Academy of Sciences (USA), 107: 19362-19367. In which a plot's change in NMDS scores over time was used as a response variable: "To measure the overall resemblance of any given herb community to communities found in warm (steep, southerly) versus cool (moderate, northerly) topographic microclimates, we used an ordination approach (also see 28). We ordinated the herb data using NMS ordination in PC-ORD version 4.14 (39), excluding species found in <5% of samples. We rotated axis 1 of the ordination to maximize its correlation with Whittaker?s topographic moisture gradient, so that a low axis 1 score indicated a community in a mesic environment such as a moderate north-facing slope, and a high axis 1 score indicated a community in a warm environment such as a steep south-facing slope. Under a warming climate, we expect the community at any given site to show a higher axis 1 score in 2007?2009 than in 1949?1951, indicating that herb composition has shifted over time in the same direction that composition changes over space from mesic (cooler and moister) to xeric (warmer and drier) topographic microclimates. For each site we calculated the difference between its 1949?1951 and 2007?2009 axis 1 ordination scores. In this case, a high value means a community that has shifted to become more dominated by xeric-adapted species." Jari Oksanen has a post on the the r-forge page (https://r-forge.r-project.org/forum/message.php?msg_id=1311&group_id=68) warning against using rotated NMDS scores in a Structural Equation Model. Are there problems with using a "change in scores" as a response variable in this kind of hypothesis testing?
I'm genuinely underwhelmed by this approach. i) there isn't such a thing as nMDS axes so does it make sense to take some 1-d coordinate system out of a 2-d coordinate system and relate it to an external variable? It would be like trying to identify patterns in all the cities of the world on the basis of what line of longitude they happened to lie on. Where this sort of thing does make sense is in methods that do identify orthogonal components from a data matrix such that axis 1 explains a component of the variation in the data, and axis 2 another, different (orthogonal) component of the variation. If this were me, I would have taken the 2-d nMDS configuration and fitted a response surface for Whittaker's topographic moisture into the ordination (using ordisurf) and then take the fitted values of the response surface for each site as the species-related topographic moisture "information", which could be plotted as a function of time. HTH G
This was done in PC Ord. Has anyone used "metaMDSrotate" in vegan to do this kind of analysis in R? Does anyone have any examples or code they'd be willing to share or point me to? Thanks, Erik
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