  Presence-only data are a typical occurrence in species distribution modeling.
They include the presence locations and no information on the absence. Their
modeling usually does not account for detection biases. In this work, we aim to
merge three different sources of information to model the presence of marine
mammals. The approach is fully general and it is applied to two species of
dolphins in the Central Tyrrhenian Sea (Italy) as a case study. Data come from
the Italian Environmental Protection Agency (ISPRA) and Sapienza University of
Rome research campaigns, and from a careful selection of social media (SM)
images and videos. We build a Log Gaussian Cox process where different
detection functions describe each data source. For the SM data, we analyze
several choices that allow accounting for detection biases. Our findings allow
for a correct understanding of Stenella coeruleoalba and Tursiops truncatus
distribution in the study area. The results prove that the proposed approach is
broadly applicable, it can be widely used, and it is easily implemented in the
R software using INLA and inlabru. We provide examples' code with simulated
data in the supplementary materials.
