Hello all,
I have a question that is perhaps esoteric, since it's on a method I don't see
used often. I am looking at the dynamics of body size evolution, and have come
upon ancestor-vs-change plots described in Alroy 2000 ("Understanding the
dynamics of evolutionary trends", Paleobiology). This is interesting because it
will allow me to see if rate of body size change depends on body size. I
haven't seen this method widely used, so for anyone unaware how this works: for
each branch, you plot the ancestral state vs. the amount/rate of change along
the branch. In theory, by looking at a scatterplot of ancestral size vs.
change, I can answer questions such as "Are smaller taxa more likely to evolve
to a larger size?" I don't see too many people using this method, so I thought
I'd ask why. Is there a particular reason for it not being used? Are there more
powerful methods to answer this same question that have supplanted it?
I also have a more specific question, assuming that ancestor-vs-change plots
are valid. For my dataset, I have reconstructed ancestral states for my clade
from tip data and pic, and then plotted ancestral states versus the rate of
change along every branch (attached). While there are a couple outliers, you
can see the distribution roughly hangs around 0 along the entire graph, but I'd
like to fit a line so I can present if the slope significantly differs from 0
(ie. body size evolutionary rate truly does not depend on body size of
ancestors). Alroy (2000) describes that the shape of the plot can be
informative on different dynamics of evolutionary change, implying fitting of
linear and polynomial lines, but doesn't really discuss how to test this
statistically. Alroy seems to use this method and fits a polynomial line in
Alroy 1998 ("Cope's Rule and the Dynamics of Body Mass Evolution in North
American Fossil Mammals", Science), but he doesn't really describe his
statistics !
in depth.
So my question is how can I fit a line to this data? Problematically, since
each branch has a sister branch that shares the same ancestral node, each
ancestral state is represented twice. I think this makes the observations
non-independent. If they are, this makes me think that a linear regression is
inappropriate, but I'm not sure. I could average the amount of change along
each branch for every ancestral node, but I'm not sure if that's the best way.
Does anyone have any insight on an appropriate way to determine a best fit line
statistically?
Thanks in advance,
Milton Tan
Auburn University
Department of Biological Sciences
PhD Candidate
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