Soetaert, K. & C. Heip, 1990. Sample-size dependence of diversity indices and the determination of sufficient sample size in a high-diversity deep-sea environment. Mar. Ecol. Progr. Ser. 59: 305-307.
Van den Meersche , K., Soetaert, K., Van Oevelen, D., 2009. xsample: an R function for sampling over- and underdetermined linear inverse problems. Journal of Statistical Software, Code Snippets 30(1), 1-15. url
Soetaert, K., Gregoire, M.L., 2011. Data assimilation of the carbonate pH system – a kalman filter tested. Ecol. Modelling 222, 1929-1942.
Soetaert, K., Cash, J.R., Mazzia, F., 2012. Solving differential equations in R. UseR, Springer, 248 pp. web.
Ever since the very beginning of my scientific work, I have been intrigued by mathematical techniques. As a very young scientist (Soetaert and Heip, 1990) I used monte carlo simulation to show that diversity estimates should be used with caution due to their dependence on sample size. Much later I worked on mathematical methods to sample underdetermined linear systems (van den Meersche et al., 2009), or investigated the usability of data assimilation techniques in aquatic systems (Soetaert and Gregoire, 2011). The most extreme example of my work in the field of mathematics, however, is my last book (Soetaert et al., 2012). This deals with both the theory of numerically solving differential equations and their implementation in the software R. I wrote the R example chapters, but also two theoretical chapters – I leave it to the reader to guess which.