Does the book address the issue of stale data?

Most statistics assumes that the underlying process is stable: if you're sampling from a population, you're sampling from the same population every time. If you estimated some parameters of model, the assumption is that these parameters will be applicable for the forecast period.

Unfortunately, in real life underlying processes tend to be unstable. For a trivial example of a known-to-not-be-stable process consider weather. Let's say I live outside of tropics and I measure air temperature over, say, 60 days. Will my temperature estimates provide a good forecast for the next month? No, they won't because the year has seasons and my "population" of days changes with time.

Or take an example from the book, catch-recatch. Imagine that a considerable period of time passed between the original "catch" and the "recatch". Does the estimation procedure still work? Well, not really -- you need estimates of mortality and birth rate now, you need to know how did your population change between the first and the second measurements.