Dear colleagues, on behalf of my co-authors, I am pleased to announce our new publication in Ecography: Martino S*, Pace DS*, Moro S, Casoli E, Ventura D, Frachea A, Silvestri M, Arcangeli A, Giacomini G, Ardizzone G, & Jona-Lasinio G (2021) Integration of presence-only data from several data sources: A case study on dolphins' spatial distribution. *equal contribution The article is open access and can be found here: https://onlinelibrary.wiley.com/doi/full/10.1111/ecog.05843 Abstract Presence-only data are typical occurrence information used in species distribution modelling. Data may be originated from different sources, and their integration is a challenging exercise in spatial ecology as detection biases are rarely fully considered. We propose a new protocol for presence-only data fusion, where information sources include social media platforms, to investigate several possible solutions to reduce uncertainty in the modelling outputs. As a case study, we use spatial data on two dolphin species with different ecological characteristics and distribution, collected in central Tyrrhenian through traditional research campaigns and derived from a careful selection of social media images and videos. We built a spatial log-Gaussian cox process that incorporates different detection functions and thinning for each data source. To finalize the model in a Bayesian framework, we specified priors for all model parameters. We used slightly informative priors to avoid identifiability issues when estimating both the animal intensity and the observation process. We compared different types of detection function and accessibility explanations. We showed how the detection function's variation affects ecological findings on two species representatives for different habitats and with different spatial distribution. Our findings allow for a sound understanding of the species distribution in the study area, confirming the proposed approach's appropriateness. Besides, the straightforward implementation in the R software, and the provision of examples' code with simulated data, consistently facilitate broader applicability of the method and allow for further validations. The proposed approach is widely functional and can be considered with different species and ecological contexts. With very best wishes, Daniela -- Daniela Silvia Pace, PhD Department of Environmental Biology Marine Ecology Lab Sapienza University of Rome Viale dell’Università 32 00185 Rome, Italy mail: danielasilvia.pace@uniroma1.it mobile: +39 346 1039652 office: +39 06 4991 4763 skype: lagenorinco Orcid ID: https://orcid.org/0000-0001-5121-7080 [image: Risultati immagini per logo sapienza] -- ________________________________________________________ Le informazioni contenute in questo messaggio di posta elettronica sono strettamente riservate e indirizzate esclusivamente al destinatario. Si prega di non leggere, fare copia, inoltrare a terzi o conservare tale messaggio se non si è il legittimo destinatario dello stesso. Qualora tale messaggio sia stato ricevuto per errore, si prega di restituirlo al mittente e di cancellarlo permanentemente dal proprio computer. The information contained in this e mail message is strictly confidential and intended for the use of the addressee only. If you are not the intended recipient, please do not read, copy, forward or store it on your computer. If you have received the message in error, please forward it back to the sender and delete it permanently from your computer system. -- Fai crescere i nostri giovani ricercatori dona il 5 per mille alla Sapienza *codice fiscale 80209930587*