Using global occurrence data to predict suitable habitats for widely distributed marine species in data-scarce regions

DE WYSIECKI, AGUSTÍN M.; BARNETT, ADAM; SÁNCHEZ-CARNERO, NOELA; CORTÉS, FEDERICO; MILESSI, ANDRÉS C.; TROBBIANI, GASTÓN A.; JAUREGUIZAR, ANDRÉS J.

BIODIVERSITY AND CONSERVATION

SPRINGER

Lugar: Berlin; Año: 2025 vol. 34 p. 1497 - 1523

0960-3115

This study addresses the challenge of advancing habitat use knowledge of widely distributed marine species populations when regional data is scarce. We use an approach basedon ecological niche models (ENMs) calibrated with global presence data to estimate theglobal niche of species, enabling 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 scientifc papers, government data, biodiversity repositories, andcitizen science contributions, to overcome regional data scarcity, which are then integratedwith environmental variables to predict habitat suitability. As a case study, we apply thismethod to predict suitable habitats of copper (Carcharhinus brachyurus) and sand tiger(Carcharias taurus) sharks in the Southwest Atlantic, two species of conservation concernin a region with limited data. Suitable habitats for both species were predicted, providingcritical information for guiding conservation eforts. Environmental factors (i.e., bottomtemperature, distance to the coast, and surface temperature) were key to shaping predicteddistribution patterns of these large predatory sharks, aligning with previous knowledge andhistorical records of their latitudinal ranges. The results have implications for conservation planning and sustainable management of shark populations in the Southwest Atlantic,contributing to broader eforts in marine biodiversity conservation. Additionally, the studyhighlights the potential of ENMs to identify essential habitats even in the absence of efortdata, underscoring their value in marine conservation. This study advances the use of nichemodelling in marine systems and demonstrates its applicability for area-based conservationinitiatives, particularly in data-poor regions.