Estimating invasive rodent abundance using removal data and hierarchical models

Abstract

Invasive rodents pose signifi ecological, economic, and public health challenges. Robust methods are needed for estimating population abundance to guide eff e 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 confi 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 (Myocastor coypus) in France and muskrats (Ondatra zibethicus) in the Netherlands. Using hierarchical multinomial N-mixture models, the eff 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 eff quantify uncertainty in parameter estimates, and account for violations of closure by fi an open-population model to multi-year data. Taken together, these approaches demonstrate the fl and utility of hierarchical models in invasive species management.