• Box, G.E.P. & Cox, D.R. (1964) An analysis of transformations. Journal of the Royal Statistical Society B, 26, 211252.
  • Crawley, M.J. (2003) Statistical Computing. An Introduction to Data Analysis using S-Plus. John Wiley & Sons Ltd, London.
  • Cuesta, D., Taboada, A., Calvo, L. & Salgado, J.M. (2008) Short- and medium-term effects of experimental nitrogen fertilization on arthropods associated with Calluna vulgaris heathlands in north-west Spain. Environmental Pollution, 152, 394402.
  • Dalthorp, D. (2004) The generalized linear model for spatial data: assessing the effects of environmental covariates on population density in the field. Entomologia Experimentalis et Applicata, 111, 117131.
  • Gebeyehu, S. & Samways, M.J. (2002) Grasshopper assemblage response to a restored national park (Mountain Zebra National Park, South Africa). Biodiversity and Conservation, 11, 283304.
  • Jiao, Y., Chen, Y., Schneider, D. & Wroblewski, J. (2004) A simulation study of impacts of error structure on modeling stock-recruitment data using generalized linear models. Canadian Journal of Fisheries and Aquatic Sciences, 61, 122133.
  • Magura, T., Tóthmérész, B. & Elek, Z. (2005) Impacts of leaf-litter addition on carabids in a conifer plantation. Biodiversity and Conservation, 14, 475491.
  • Maindonald, J. & Braun, J. (2007) Data Analysis and Graphics Using R – An Example-Based Approach, 2nd edn. Cambridge University Press, Cambridge.
  • McCullagh, P. & Nelder, J.A. (1989) Generalized Linear Models, 2nd edn. Chapman & Hall, London.
  • Miller, R.G., Jr (1997) Beyond anova. Chapman & Hall/CRC Press, London.
  • O’Hara, R.B. (2009) How to make models add up – a primer on GLMMs. Annales Zoologici Fennici, 46, 124137.
  • Piepho, H.-P. (2009) Data transformation in statistical analysis of field trials with changing treatment variance. Agronomy Journal, 101, 865869.
  • R Development Core Team (2009) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
  • Sileshi, G., Hailu, G. & Nyadzi, G.I. (2009) Traditional occupancy-abundance models are inadequate for zero-inflated ecological count data. Ecological Modelling, 220, 17641775.
  • Sokal, R.R. & Rohlf, F.J. (1995) Biometry, 3rd edn. Freeman and Company, New York, New York, USA.
  • Ver Hoef, J.M. & Boveng, P.L. (2007) Quasi-Poisson vs. negative binomial regression: how should we model overdispersed count data? Ecology, 88, 27662772.
  • Vernables, W.N. & Ripley, B.D. (2002) Modern Applied Statistics with S, 4th edn. Springer, New York, New York, USA.
  • White, G.C. & Bennetts, R.E. (1996) Analysis of frequency count data using the negative binomial distribution. Ecology, 77, 25492557.
  • Wright, D.H. (1991) Correlations between incidence and abundance are expected by chance. Journal of Biogeography, 18, 463466.
  • Zar, J.H. (1999) Biostatistical Analysis, 4th edn. Prentice Hall, Englewood Cliffs, New Jersey, USA.
  • Zuur, A.F., Ieno, E.N. & Smith, G.M. (2007) Analysing Ecological Data. Springer, New York, NY, USA.
  • Zuur, A.F., Ieno, E.N., Walker, N.J., Saveliev, A.A. & Smith, G.M. (2009) Mixed Effects Models and Extensions in Ecology with R. Springer, New York, NY, USA.
  • Zuur, A.F., Ieno, E.N. & Elphick, C.S. (2010) A protocol for data exploration to avoid common statistical problems. Methods in Ecology and Evolution, 1, 314.