Estimating the spatial distribution of the white shark in the Mediterranean Sea via an integrated species distribution model accounting for physical barriers
Glasgow;
University ofGoettingen;
USA;
Norway;
Department of Integrative MarineEcology;
Rome;
Blackburg;
Goettingen;
Virginia Tech;
Chair of Statistics;
Department of Fish andWildlifeConservation;
Stazione Zoologica Anton Dohrn;
Trondheim;
Germany||School of Mathematics and Statistics;
Sapienza University of Rome;
Norwegian University of Science andTechnology;
Department of Statistical Sciences;
Italy;
University of Glasgow;
Scotland;
Rome;
Virginia;
Department of Mathematical Sciences;
关键词：
Barrier model;
spatial logGaussian Cox process;
conservation;
ecology;
integrated species distribution modeling;
white shark;
Mediterranean Sea;
SDM;
期刊名称：
Environmetrics
i s s n：
1180-4009
年卷期：
2025 年
36 卷
1 期
页 码：
e2876.1-e2876.16
页 码：
摘 要：
Conserving oceanic apex predators, such as sharks, is of utmost importance. However, scant abundance and distribution data often challenge understanding the population status of many threatened species. Occurrence records are often scarce and opportunistic, and fieldwork aimed to retrieve additional data is expensive and prone to failure. Integrating various data sources becomes crucial to developing species distribution models for informed sampling and conservation purposes. The white shark, for example, is a rare but persistent inhabitant of theMediterranean Sea. Here, it is considered Critically Endangered by the IUCN, while population abundance, distribution patterns, and habitat use are still poorly known. This study uses available occurrence records from 1985 to 2021 from diverse sources to construct a spatial log-Gaussian Cox process, with data-source specific detection functions and thinning, and accounting for physical barriers. This model estimateswhite shark presence intensity alongside uncertainty through a Bayesian approach with Integrated Nested Laplace Approximation (INLA) and the inlabru R package. For the first time, we projected species occurrence hot spots and landscapes of relative abundance (continuousmeasure of animal density in space) throughout the Mediterranean Sea. This approach can be used with other rare species for which presence-only data from different sources are available.
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