I have matrices of genetic distances for x number of individuals within a population and their corresponding point coordinates -one genetic distance matrix per point coordinates.
I was imagining using the Mantel test, i.e. evaluate the significance of correlations between genetic and geographic distances. However, what I did not think of was that these two types of matrices will have different dimensions (e.g. a genetic matrix of 15 ind = 15x15, sampled at three locations = 3x3), hence the Mantel test will not work. I am now looking into Moran's I, testing for spatial autocorrelation, but it will not work with my data as it currently is. I would need one measure of variation per point coordinates. Would it be legit (never seen anyone do this) to derive a measure of variation (e.g. var, sd, cv) from a single distance matrix? Since the matrix is normalized, ranging from 0-1, maybe var or sd would work?
I am kinda new to analyzing spatial data, so I would appreciate any pointers or alternative tests I might be able to do.
Thanks,