<br> Invasive rodents pose significant ecological, economic, and public health challenges. Robust methods are needed for estimating population abundance to guide effective management. Traditional methods such as capture-recapture are often impractical for invasive species due to ethical, legal and logistical constraints. Here, the application of hierarchical multinomial N-mixture models for estimating the abundance of invasive rodents using removal data is highlighted. Firstly, a simulation study was performed which demonstrated minimal bias, as well as good precision and reliable coverage of confidence intervals across a range of sampling scenarios. Additionally, the consequences of violating the population closure assumption were illustrated by showing how between-occasion dynamics can bias inference. Secondly, removal data was analyzed for two invasive rodent species, namely coypus (<br> Myocastor coypus<br> ) in France and muskrats (<br> Ondatra zibethicus<br> ) in the Netherlands. Using hierarchical multinomial N-mixture models, the effect of temperature on abundance was examined, while accounting for imperfect and time-varying capture probabilities. Additionally, this study demonstrated how to accommodate spatial variability using random effects, quantify uncertainty in parameter estimates, and account for violations of closure by fitting an open-population model to multi-year data. Taken together, these approaches demonstrate the flexibility and utility of hierarchical models in invasive species management.<br>
