Why are prior distributions sometimes conditional probabilities? I came across the following Bayesian equation in textbook of evolutionary biology:
$f(t, r, \theta|X) \propto f(X|t, r, \theta)f(t|\theta)f(r|t,\theta)f(\theta)$
$f(X|t, r, \theta)$ is the likelihood and $f(t|\theta)$ is the prior distribution on times $t$ $f(r|t,\theta)$ is the prior distribution on rates $r$, and $f(\theta)$ is the prior of the substitution model.
I'm having trouble understanding what it means for a prior distribution to be conditioned on other values, as we have in $f(t|\theta)$ and $f(r|t,\theta)$. Does this mean that our prior beliefs about $t$ depend on $\theta$ and those about $r$ depend on both $t$ and $theta$? I.e., that without knowing $t$ and $theta$, we have no prior beliefs about $r$?
Does the specification of the priors in this way make this an example of a hierarchical model? If so, what exactly is the hierarchy?
If anyone can recommend any literature that might help me understand this, I'd be grateful for it.

Let's take a step back and revisit the Bayes theorem;
$$
p(t, r, \theta \vert X) \propto p(X\vert t, r, \theta)p(t, r, \theta).
$$
The expression you shown has done is to factorize the prior distribution $p(t, r, \theta)$. Of course, the factorization is not unique, for instance;
$$
\begin{aligned}
p(t, r, \theta) &= p(r \vert \theta, t)p(\theta \vert t)p(t)\\
&= p(\theta \vert r, t)p(r \vert t)p(t)\\
&= p(\theta \vert r, t)p(t \vert r)p(r)\\
&=\vdots
\end{aligned}
$$
All these factorizations are valid, but not all of them are useful. The particular factorization is chosen based on the physical picture of the system of interest. And if it is easy to model the behavior of the parameters $r$ given $t$ and $\theta$ and the behavior of $t$ given $\theta$, the factorization you have shown would be a sensible choice.
You can also model the joint distribution altogether without any factorization, this is more of a modelling problem instead of a statistical one.
