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

TL;DR

This paper presents a novel method for integrating multiple sources of presence-only data, including social media, to model marine mammals' distribution, accounting for detection biases and demonstrated on dolphins in the Central Tyrrhenian Sea.

Contribution

It introduces a general approach using Log Gaussian Cox processes to combine diverse presence-only data sources with detection bias correction, implemented in R with INLA and inlabru.

Findings

Effective modeling of dolphin distribution in the study area.

Demonstrates broad applicability and ease of implementation of the approach.

Provides example code with simulated data for reproducibility.

Abstract

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.