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AMCL is a global localization algorithm in the sense that it fuses LIDAR scan matching with a source of odometry to provide an estimate of the robot's pose w.r.t a global map reference frame.
It is common to use an EKF/UKF such as those implemented in the robot_localization package to fuse wheel odometry with an IMU (or other sensors) and create an improved odometry estimate (local pose estimation) for AMCL.
I am not sure where the example you mention uses both AMCL and an EKF, but it is probably something similar.
Another way to use an EKF together with AMCL is to fuse two global estimates, e.g to fuse the pose provided by AMCL with the pose provided by another global localization method (e.g beacon-based triangulation..)
If you haven't done it already, I suggest you have a look at the robot_localization wiki page and this talk from the 2015 ROSCON
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