Abstract
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.