ANALYZING OPPORTUNISTIC SIGHTINGS WITH POINT PROCESSES MODELS: THE MEDITERRANEAN WHITE SHARK AS A CASE STUDY

Sharks represent one of the globally most endangered groups of fish, particularly in the Mediterranean Sea where 57% of the species are considered threatened by the IUCN. Occurrence sightings are increasingly scarcer in the area and targeted surveys are rare and limited to specific sectors. However new opportunities are emerging to exploit unconventional sources of data such as new methods to analyze citizen science data and other opportunistic sightings. Analyzing opportunistic records is challenging for two main reasons. First, no systematic surveys were carried out to collect these data. Hence, no information was recorded about sampling or observation effort. Second, opportunistic records are presence-only data so they need to be modeled while making inferences about possible processes generating absence and regimes of sampling effort. In this pilot study, we combined all the available data on the white shark presence in the Mediterranean Sea collecting information from different sources (MEDLEM-Mediterranean Large Elasmobranchs Monitoring and sharkPulse), and used these data to test a different approach in characterizing the spatial distribution of the species in the area from presence-only data. We used spatio-temporal Point Processes Models (PPM) both in a Maximum likelihood and Bayesian framework to analyze the Mediterranenan white shark distribution. In this way we obtained the probability of occurrence of the species in the area on a set of covariates associated to the point pattern itself (i.e. SST, fishing effort), controlling for the observation effort through the use of a simple proxy based on coastal human population trajectories. This approach can be surely extended to other species and different areas and might represent a useful tool to better characterize elasmobranchs distribution patterns and conservation status all around the world, as well as postulating new ecological hypotheses on species so far understudied for a lack of conventional scientific and adequate fisheries data.