Hi Belinda,
I agree with Brian’s comment. I’d also add the more general point that OU
models don’t really have a clearly interpretable rate parameter in the same
way that BM models do. In BM models, the sigma parameter controls the pace
of trait evolution: the expected change over a given time interval depends
strightforwardly on sigma (and only on sigma).
OU models also have a sigma parameter that has the same meaning — it is
the variance of the diffusion process. But, it is perilous to interpret it
in the OU context as a rate parameter as one would for BM. This is because
the expected change over a given interval depends on sigma, but also on
alpha and the distance to the optimum. This complexity means that OU
models don’t have any single number you can point to as a generic rate of
change for the process.
So, if your scientific question hinges on saying something about rates on
branches, I think this is all the more reason to follow Brian’s suggestion
of looking into one of the BM rate heterogeneity methods.
Best,
Gene
--
Gene Hunt
Curator, Department of Paleobiology
National Museum of Natural History
Smithsonian Institution [NHB, MRC 121]
P.O. Box 37012
Washington DC 20013-7012
Phone: 202-633-1331 Fax: 202-786-2832
http://paleobiology.si.edu/staff/individuals/hunt.cfm
From: Brian O'Meara <omeara.br...@gmail.com<mailto:omeara.br...@gmail.com
Reply-To: "omeara.br...@gmail.com<mailto:omeara.br...@gmail.com>" <
omeara.br...@gmail.com<mailto:omeara.br...@gmail.com>>
Date: Friday, April 15, 2016 at 11:00 AM
To: Belinda Kahnt <belind...@gmx.de<mailto:belind...@gmx.de>>, "mailman,
r-sig-phylo" <r-sig-phylo@r-project.org<mailto:r-sig-phylo@r-project.org>>
Subject: Re: [R-sig-phylo] estimating the evolutionary rate of a continous
trait
Hi, Belinda.
One thing to watch is over-intepretation: BM is consistent with drift, OU
is consistent with selection, but various kinds of selection can also lead
to BM. [1]. A lot of the people involved in these methods (including me)
are guilty of sloppiness in language in this area, leading to confusion.
Also, another issue could be branch lengths: are they proportional to time
(or, arguably, something like number of generations)? All these methods are
basically stretching the tree in various ways (and, for OU, adjusting
expected means), so if branch lengths are arbitrary, so are the results. I
ask due to the lambda fit. Other things that can cause this are
unincorporated measurement error (b/c it looks like a lot of evolution
right at the tips, meaning little evolution along the branches). I'd
suggest incorporating this (you can give known estimates of measurement
error; some programs allow this to be estimated as well (I forget whether
we have the estimation bit exposed in OUwie, but known error should be
there at least)).
Surface can estimate different regimes on the tree, but IIRC it does not
estimate different rates, just OU means. I believe auteur (in geiger) and
BAMM can model different BM rates on different branches, which sounds like
your question. We have some code to try different OU models (including
perhaps ones with different rates) on different branches buried somewhere
in OUwie (we call it OUwie-dredge so people know to be cautious) but it
hasn't been tested or published yet.
One other caution: if you have a BM model, and are just modeling different
rates, it can be done well. Trying to estimate different rates with OU
models is harder to do well: there's interaction between alpha and sigma
that can make them difficult to distinguish (not formally nonidentifiable
[unlike OU means sometimes], just difficult). For me, I'd probably lean
towards one of the BM rate heterogeneity methods for your question.
Hope this helps,
Brian
[1] Hansen, T. F. and E. P. Martins. 1996. Translating between
microevolutionary process and macroevolutionary patterns: the correlation
structure of interspecific data. Evolution 50:1404-1417.
_______________________________________
Brian O'Meara
Associate Professor
Dept. of Ecology & Evolutionary Biology
U. of Tennessee, Knoxville
http://www.brianomeara.info
On Fri, Apr 15, 2016 at 10:20 AM, Belinda Kahnt <belind...@gmx.de<mailto:
belind...@gmx.de>> wrote:
Dear all,
I am a newbie to this mailing list and phylogenetic analyses in R and hope
you can comment on a question I have. I would like to estimate the rate of
evolution of a continous trait and check if the trait evolves faster along
some branches of the topology. I already checked with Pagels lambda if the
trait evolves according to a Brownian motion model,(i.e. pure drift), which
it did not (lambda ~ 0). Modelling the trait evolution under the
Ornstein-Uhlenbeck (OU) model provided a significant better fit (p<- 0.01)
with a very high alpha parameter of 8.7 (indicating a role of selection in
trait evolution). In order to infer now the rate of trait evolution I am
searching for a R package that is able to do this inference based on the OU
model. However, so far I either just found methods based on the Brownian
motion model (e.g. brownie.lite function in phytools, Motmot) or methods
that also rely on the reconstruction of ancestral selective regimes
beforehand (like mvMORPH or OUwie). I want to keep the model as simple as
possible and therefore don't know if it is necessary to also model
ancestral selective regimes if I just want to know if the evolutionary rate
of the trait varies along branches and which branches show an accelerated
rate?! Which programm would you recommend? I would be very thankful for
your help and recommendations.
Best wishes,
Belinda
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