spectral clustering vs hierarchical clustering

Can anyone please explain that is there any advantage of using hierarchical clustering over spectral clustering? I know how they work but I want to know in which situations its better to use hierarchical clustering over spectral clustering.

Hierarchical Clustering:

• It is good for taxonomical data.
• Keep connecting similar points until you end up with a single cluster containing all the data points.
• From the grouping that you get after combining all the data, it is called a dendrogram.
• Depending on how many clusters you want, you cut the dendrogram at a level.

Spectral Clustering:

• It is for clustering data which is in complex shape as K-means and Density-based clustering fail in these cases.
• Treating the data points as vertices of a graph, connect the vertices that are close enough. Hence you also choose an ε value. All vertices closer than ε are connected.
• Unlike Hierarchical clustering, you do not get a fully connected single graph (unless you take a high ε value and there are no non-connected components). Instead you get multiple connected components, each of them representing a cluster.