Data, code and manuscript 'Estimating invasive rodent abundance using removal data and hierarchical models'

• analyses
  • coypus.Rmd: R code for running the coypus analyses
  • muskrats.Rmd: R code for running the muskrats analyses
  • simulations.Rmd: R code for running the simulations
  • data
    • coypus.rds: coypus data
    • temperature_netherlands.rds: temperature data for 2014
    • temperature_netherlands_allperiod.rds: temperature data for 1987-2014
• manuscript
  • rodent-abundance-from-removal.Rmd: master file to produce manuscript

Author:
Gimenez, Olivier – CNRS Montpellier, France

Abstract: 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 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 performed a simulation study which demonstrated minimal bias in abundance estimates across a range of sampling scenarios. Second, I analyzed removal data for two invasive rodent species: coypus (Myocastor coypus) in France and muskrats (Ondatra zibethicus) in the Netherlands. Using hierarchical multinomial N-mixture models, I examined the effects of temperature on abundance while accounting for imperfect and time-varying capture probabilities. I also showed how to accommodate spatial variability using random effects, and quantified uncertainty in parameter estimates. Overall, I hope to demonstrate the flexibility and utility of hierarchical models in invasive species management. I provide reproducible code and data to encourage broader adoption of multinomial N-mixture models.