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Estimating invasive rodent abundance using removal data and hierarchical models

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, I showcase the application of hierarchical multinomial N-mixture models for estimating the abundance of invasive rodents using removal data. First, I perform a simulation study which demonstrates minimal bias, as well as good precision and reliable coverage of confidence intervals across a range of sampling scenarios. I also illustrate the consequences of violating the population closure assumption, showing how between-occasion dynamics can bias inference. Second, I analyze removal data for two invasive rodent species, namely coypus (Myocastor coypus) in France and muskrats (Ondatra zibethicus) in the Netherlands. Using hierarchical multinomial N-mixture models, I examine the effects of temperature on abundance while accounting for imperfect and time-varying capture probabilities. I also show 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. Overall, I hope to demonstrate the flexibility and utility of hierarchical models in invasive species management.

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