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Isolation by distance is the phenomena that (even partial) reproductive isolation due to geographical distance between reproductive populations will result in greater genetic distance between those populations. This phenomena is not the only thing that can affect genetic distances, including other forms of reproductive isolation.

Given that the example plot of genetic distance on wikipedia appears to be a linear-ish relationship, perhaps the effect that geographical distance has on genetic distances could be subtracted via a linear regression model. I'm not sure how common linear relationships are for this type of data, but you can find examples of non-linear relationships such as found in "Genetic and phenotypic divergence in an island bird: isolation by distance, by colonization or by adaptation?". Other studies, such as "Direct and indirect measures of dispersal in the fairy shrimp Branchipodopsis wolfi indicate a small scale isolation-by-distance pattern", show pretty marginal linearity.

Is there a conventional procedure in the literature for removing the isolation by distance effect on the genetic distances?

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  • $\begingroup$ I think that a first question might be why you would want to do such a thing. I am sure that you can come up with a statistical approach that "removes" isolation by distance effects, but isolation by (geographic) distance would be pretty convoluted with genetic distance in the first place, is my intuition. $\endgroup$ Jun 28 at 5:35
  • $\begingroup$ Removing, or explicitly modelling, IBD effects would allow us to study variation that isn't explained by IBD. If what remains is just noise, that is interesting. If not, that is also quite interesting. $\endgroup$
    – Galen
    Jun 28 at 13:50
  • $\begingroup$ The relationship between geographic distance and genetic distance might be convoluted, and I'm looking forward to learning (here and from data) more about it. $\endgroup$
    – Galen
    Jun 28 at 13:54
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You can kind of do this with Mantel tests and related methods. As always there are issues.

If we look at this publication, we for example find a similar motivation:

Another possibility for using the Mantel test is to compare the relationship between two matrices, but taking into account the effect of a third one (usually the geographical distances), as originally proposed by Smouse et al. (1986). When analyzing spatially distributed data, the main issue is to find out if the two matrices are “causally” related (i.e., in the sense that they indicate an ecological or evolutionary process), or if the observed relationship appears only because both variables are spatially structured by intrinsic effects (i.e., distance-structured dispersal causing more similarity between neighboring populations).

In this case, you would be correcting in some way for the effects of isolation-by-distance, probably using some measure of geographic distance as the third matrix in question. However, Mantel tests can't really be interpreted in terms of variance partitioning, which is what you seem to want.

There are more spatial regression methods available that may be able to do this, though they seem to be less popular (probably because Mantel is simple and relatively assumption-free). They treat the isolation by distance effect as a spatial autocorrelation, which makes a certain degree of sense. If you are interested in a variance-partitioning type analysis, these are probably a better bet than Mantel.

But there are issues because it is not clear how to interpret isolation by distance in the absence of the genetic distances; simple distance is probably not good enough, and there are multiple plausible formulations of the distancing mechanism that are not restricted to naive measures of distance. All this is to say, you can do this, but beware of the assumptions that you're making going in.

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