I am trying to cluster a biological population on the basis of morphological characters using UPGMA clustering method, but I am not sure which distance should I use- Mahalanobis or Euclidean. What are the benefits of using the two distances and when to use them in reference to biological population clustering using morphological traits.
The Mahalanobis distance takes into account the distribution of the existing points. This distribution affects whether or not a point is considered "close" in a particular dimension. If your points extend along a particular direction, say the x-axis for simplicity, then a reduced x-distance is used to determine if new points belong to the cluster.
As pointed out by ttnphns, you would need to compute the covariance matrix of the clusters as you construct the groups. With UPGMA this would be possible and may lead to interesting insights.
Can you not compare and contrast the results with both Mahalanobis and Euclidean metrics?