Integration of presence‐only data from several sources: a case study on dolphins' spatial distribution. Issue 10 (14th September 2021)

• Record Type: Journal Article
• Authors: Martino, Sara; Pace, Daniela Silvia; Moro, Stefano; Casoli, Edoardo; Ventura, Daniele; Frachea, Alessandro; Silvestri, Margherita; Arcangeli, Antonella; Giacomini, Giancarlo; Ardizzone, Giandomenico; Jona Lasinio, Giovanna
• Is Part Of: Ecography. Volume 44:Issue 10(2021)
• Page Start: 1533
• Page End: 1543
• Publication Date: 2021-09-14
• Subjects: cetacean -- data fusion -- dolphins -- Mediterranean Sea -- presence-only data -- point processes; Ecology -- Periodicals; Biodiversity -- Periodicals; 574.5
• DOI: 10.1111/ecog.05843
• Languages: English
• ISSNs: 0906-7590

Abstract

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.