Ugh, yeah, ugly stuff. The absolute value of eigenvalues creeps me out too - I think that could be very misleading, but probably only in some weird cases. My understanding on PCoA and negative eigenvalues: any negative eigenvalues mean you can't actually fit the n samples in the n-1 dimensional space, but negative eigenvalues of small magnitude mean the distances in the n-1 dim space almost match the input distance matrix. So eigenvalues like:

λ=100, 10,4,2,-.0001
write note to log

λ=100,10,4,2,-3
warn more loudly - the distance between samples along the 4th dimension is comparable to the inaccuracy in plotting the samples ("comparable" is vague here, but maybe that's ok).

λ=100, 10, 4, 2, -80
yell and scream. Maybe error with override option.