  In ecology we may find scenarios where the same phenomenon (species
occurrence, species abundance, etc.) is observed using two different types of
samplers. For instance, species data can be collected from scientific sampling
with a completely random sample pattern, but also from opportunistic sampling
(e.g., whale or bird watching fishery commercial vessels), in which observers
tend to look for a specific species in areas where they expect to find
  Species Distribution Models (SDMs) are a widely used tool for analyzing this
kind of ecological data. Specifically, we have two models available for the
above data: a geostatistical model (GM) for the data coming from a complete
random sampler and a preferential model (PM) for data from opportunistic
sampling.
  Integration of information coming from different sources can be handled via
expert elicitation and integrated models. We focus here in a sequential
Bayesian procedure to connect two models through the update of prior
distributions. Implementation of the Bayesian paradigm is done through the
integrated nested Laplace approximation (INLA) methodology, a good option to
make inference and prediction in spatial models with high performance and low
computational costs. This sequential approach has been evaluated by simulating
several scenarios and comparing the results of sharing information from one
model to another using different criteria. The procedure has also been
exemplified with a real dataset.
  Our main results imply that, in general, it is better to share information
from the independent (completely random) to the preferential model than the
alternative way. However, it depends on different factors such as the spatial
range or the spatial arrangement of sampling locations.
