  Given a finite typed rooted tree $T$ with $n$ vertices, the {\em empirical
subtree measure} is the uniform measure on the $n$ typed subtrees of $T$ formed
by taking all descendants of a single vertex. We prove a large deviation
principle in $n$, with explicit rate function, for the empirical subtree
measures of multitype Galton-Watson trees conditioned to have exactly $n$
vertices. In the process, we extend the notions of shift-invariance and
specific relative entropy--as typically understood for Markov fields on
deterministic graphs such as $\mathbb Z^d$--to Markov fields on random trees.
We also develop single-generation empirical measure large deviation principles
for a more general class of random trees including trees sampled uniformly from
the set of all trees with $n$ vertices.
