Community detection, or clustering, identifies groups of nodes in a graph that are more densely connected to each other than to the rest of the network. Given the size and dynamic nature of real-world graphs, efficient community detection is crucial for tracking evolving communities, enhancing our understanding and management of complex systems. The Leiden algorithm, which improves upon the Louvain algorithm, efficiently detects communities in large networks, producing high-quality structures. However, existing multicore dynamic community detection algorithms based on Leiden are inefficient and lack support for tracking evolving communities. This technical report introduces the first implementations of parallel Naive-dynamic (ND), Delta-screening (DS), and Dynamic Frontier (DF) Leiden algorithms that efficiently track communities over time. Experiments on a 64-core AMD EPYC-7742 processor demonstrate that ND, DS, and DF Leiden achieve average speedups of 3.9x, 4.4x, and 6.1x, respectively, on large graphs with random batch updates compared to the Static Leiden algorithm, and these approaches scale at 1.4 - 1.5x for every thread doubling.