  We study analytically the late time statistics of the number of particles in
a growing tree model introduced by Aldous and Shields. In this model, a cluster
grows in continuous time on a binary Cayley tree, starting from the root, by
absorbing new particles at the empty perimeter sites at a rate proportional to
c^{-l} where c is a positive parameter and l is the distance of the perimeter
site from the root. For c=1, this model corresponds to random binary search
trees and for c=2 it corresponds to digital search trees in computer science.
By introducing a backward Fokker-Planck approach, we calculate the mean and the
variance of the number of particles at large times and show that the variance
undergoes a `phase transition' at a critical value c=sqrt{2}. While for
c>sqrt{2} the variance is proportional to the mean and the distribution is
normal, for c<sqrt{2} the variance is anomalously large and the distribution is
non-Gaussian due to the appearance of extreme fluctuations. The model is
generalized to one where growth occurs on a tree with $m$ branches and, in this
more general case, we show that the critical point occurs at c=sqrt{m}.
