  This paper analyses the effect of preferential sampling in Geostatistics when
the choice of new sampling locations is the main interest of the researcher. A
Bayesian criterion based on maximizing utility functions is used. Simulated
studies are presented and highlight the strong influence of preferential
sampling in the decisions. The computational complexity is faced by treating
the new local sampling locations as a model parameter and the optimal choice is
then made by analysing its posterior distribution. Finally, an application is
presented using rainfall data collected during spring in Rio de Janeiro. The
results showed that the optimal design is substantially changed under
preferential sampling effects. Furthermore, it was possible to identify other
interesting aspects related to preferential sampling effects in estimation and
prediction in Geostatistics. With the Rejoinder to Comments [arXiv:1509.04817],
[arXiv:1509.04819], [arXiv:1509.04821].
