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

Integration of presence-only data from several sources. A case study on dolphins' spatial distribution