  Continuous space species distribution models (SDMs) have a long-standing
history as a valuable tool in ecological statistical analysis. Geostatistical
and preferential models are both common models in ecology. Geostatistical
models are employed when the process under study is independent of the sampling
locations, while preferential models are employed when sampling locations are
dependent on the process under study. But, what if we have both types of data
collectd over the same process? Can we combine them? If so, how should we
combine them? This study investigated the suitability of both geostatistical
and preferential models, as well as a mixture model that accounts for the
different sampling schemes. Results suggest that in general the preferential
and mixture models have satisfactory and close results in most cases, while the
geostatistical models presents systematically worse estimates at higher spatial
complexity, smaller number of samples and lower proportion of completely random
samples.
