We introduce a new model for dynamic algorithms in which the algorithm has read-only oracle access to a dynamically changing input and random access to its own bounded workspace. It is a dynamic analogue of the time-space tradeoffs for static inputs studied in complexity theory, where only writable memory counts as space. We study dynamic undirected $s$-$t$ connectivity: after every edge update to an $n$-vertex graph, an algorithm with $S$ words of workspace must report whether $s$ and $t$ are connected. The graph is accessed via adjacency list queries (degree, $j$-th neighbor) or adjacency matrix queries. Our algorithms are randomized; our lower bounds hold for randomized algorithms with $cn$ bits of workspace, $c>0$ a small constant ($cn/\log n$ for the worst-case matrix bound). We prove: (1) With adjacency lists, worst-case time $\tildeΘ(n^2/S^2)$ per insertion is necessary and sufficient for incremental algorithms. (2) With an adjacency matrix, worst-case time $\tildeΩ(n^2/S)$ per insertion is necessary for incremental algorithms, and sufficient for $S\ge\sqrt{n}$. (3) With amortization, incremental algorithms achieve $\tilde O(n/S)$ time per insertion with adjacency lists and $\tilde O(n^2/S)$ with an adjacency matrix, while $\tildeΩ(\sqrt{n/S})$ and $\tildeΩ(n\sqrt{n/S})$, respectively, are necessary. (4) Fully dynamic algorithms need amortized time $\tildeΩ(n/S)$ per update with adjacency lists and $\tildeΩ(n^2/S)$ with an adjacency matrix, even on forests, where both bounds are tight (lists: under bounded degree). Conventional dynamic connectivity admits polylogarithmic update time with $Ω(n)$ words of space. In contrast, our lower bounds show that polynomial update time is necessary whenever $S\le n^{1-\varepsilon}$, even for a single pair of vertices and even for incremental algorithms with amortized guarantees.

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