Differential abundance (DA) analysis in microbiome studies has recently been used to uncover a plethora of associations between microbial composition and various health conditions. While current approaches to DA typically apply only to cross-sectional data, many studies feature a longitudinal design to better understand the underlying microbial dynamics. To perform DA on longitudinal microbial studies, we propose a novel varying coefficient mixed-effects model with local sparsity. The proposed method can identify time intervals of significant group differences while accounting for temporal dependence. Specifically, we exploit a penalized kernel-local polynomial smoothing approach for parameter estimation and extend local regression to include a random effect. Further, we obtain point-wise confidence intervals using bootstrapping to determine intervals of significant differences. Synthetic data experiments demonstrate the necessity of modelling dependence for precise estimation and support recovery. The application to a longitudinal study of mice oral microbiome undergoing cancer development with and without a mutation of interest reveals novel scientific insights.