While operations rank and select on static bitvectors can be supported in constant time, lower bounds show that supporting updates raises the cost per operation to \(\varTheta (\log n/ \log \log n)\) . This is a shame in scenarios where updates are possible but uncommon. We develop a representation of bitvectors that, if there are q queries per update, supports all the operations in \(O(\log (n/q))\) amortized time. Our experimental results support the theoretical findings, displaying speedups of orders of magnitude compared to standard dynamic implementations.