Novel use of global occurrence data to indirectly predict suitable habitats for widely distributed marine species in data-scarce regions

This study addresses the challenge of advancing habitat use knowledge of widely distributed marine species populations when regional data is scarce. To achieve this, we use an innovative approach based on ecological niche models (ENMs) calibrated with global presence data to estimate the global niche of species, allowing for indirect predictions of suitable habitats and potential distribution in one or more regions of interest. The method leverages a range of global occurrence records, including scientific papers, government data, biodiversity repositories, and citizen science contributions, to overcome regional data scarcity, which are then integrated with environmental variables to predict habitat suitability. As a case study, we apply this method to predict suitable habitats of copper ( Carcharhinus brachyurus ) and sand tiger ( Carcharias taurus ) sharks in the Southwest Atlantic, two species of conservation concern in a region with limited data. Suitable habitats for both species were predicted, information required to guide conservation efforts. Environmental factors were key to shaping predicted distribution patterns of these large predatory sharks, aligning with previous knowledge and historical records of their latitudinal ranges. The results have significant implications for the conservation planning and sustainable management of shark populations in the Southwest Atlantic, contributing to broader efforts of marine biodiversity preservation. Additionally, the study highlights the potential of ENMs to identify essential habitats even in the absence of effort data, underscoring their value in marine conservation. This study not only advances the use of niche modelling in marine systems but also demonstrates its applicability for area-based conservation initiatives, particularly in data-poor regions.