Bayesian N-Mixture Models Applied to Estimating Insect Abundance

This repository contains all necessary files to carry out the analyses performed in Mimnagh et al., (2023).

Appendix Files

The following files form the appendix for this chapter:

• Appendix_A.R provides R code to simulate data for, and fit the original N-mixture model (Royle, 2004).
• Appendix_B.R provides R code to simulate data for, and fit the multi-species N-mixture model model (Mimnagh et al., 2022).
• Appendix_C.R provides R code to produce the covariance diagnostic proposed by Dennis et al., (2015).

Case Study Files

The following files form the case study used in this chapter, in which the original N-mixture model (Royle, 2004) and the multi-species N-mixture model (Mimnagh et al., 2022) are fitted to data collected as part of the BeeWalk Survey Scheme (Comont 2017, http://www.beewalk.org.uk) in the UK.

• BeeWalk_case study.R provides code for data preparation, and implementing models in JAGS.
• bees.xlsx is a subset of the BeeWalk Survey data.

References

Comont, R. 2017. RSPB Spotlight Bumblebees. Bloomsbury Publishing.

Dennis, E. B., Morgan, B. J., & Ridout, M. S. (2015). Computational aspects of N‐mixture models. Biometrics, 71(1), 237-246.

Mimnagh, N., Parnell, A., & Prado, E. (2023). Bayesian N-Mixture Models Applied to Estimating Insect Abundance. In Modelling Insect Populations in Agricultural Landscapes (pp. 185-210). Cham: Springer International Publishing.

Mimnagh, N., Parnell, A., Prado, E., & Moral, R. D. A. (2022). Bayesian multi-species N-mixture models for unmarked animal communities. Environmental and Ecological Statistics, 29(4), 755-778.

Royle, J. A. (2004). N-mixture models for estimating population size from spatially replicated counts. Biometrics, 60(1), 108-115.