Calculating how much space recursive algorithms use can be tricky. Let’s break down some important points:

1. Call Stack Usage: When a function calls itself (that's recursion), it uses space in something called the call stack. If the function calls itself many times, this space can grow quickly.

2. Variable Storage: Each time the function goes deeper into recursion, it may need extra memory for local variables and parameters.

To figure out the space being used, you can follow these steps:

• Identify Recursive Depth: First, find out how deep the recursion goes. Let's call this the maximum depth of recursion, or just $d$.

• Calculate Local Memory: Next, add up the space needed for the variables, which we can call $s$, at each level of recursion.

So, we can often say the space complexity is about $O(d \times s)$. But figuring out $d$ can be hard unless you look at specific examples.