| Summary: | In recent years, the use of ecological niche models (ENMs) and species distribution models (SDMs) to explore the patterns and processes behind observed distribution of species has experienced an explosive growth. Although the use of these methods has been less common and more recent in marine ecosystems than in a terrestrial context, they have shown significant increases in use and applications. Herein, we provide a systematic review of 328 articles on marine ENMs and SDMs published between 1990 and 2016, aiming to identify their main applications and the diversity of methodological frameworks in which they are developed, including spatial scale, geographic realm, taxonomic groups assessed, algorithms implemented, and data sources. Of the 328 studies, 48 % were at local scales, with a hotspot of research effort in the North Atlantic Ocean. Most studies were based on correlative approaches and were used to answer ecological or biogeographic questions about mechanisms underlying geographic ranges (64 %). A few attempted to evaluate impacts of climate change (19 %) or to develop strategies for conservation (11 %). Several correlative techniques have been used, but most common was the machine-learning approach Maxent (46 %) and statistical approaches such as generalized additive models GAMs (22 %) and generalized linear models, GLMs (14 %). The groups most studied were fish (23 %), molluscs (16 %), and marine mammals (14 %), the first two with commercial importance and the last important for conservation. We noted a lack of clarity regarding the definitions of ENMs versus SDMs, and a rather consistent failure to differentiate between them. This review exposed a need to know, reduce, and report error and uncertainty associated with species’ occurrence records and environmental data. In addition, particular to marine realms, a third dimension should be incorporated into the modelling process, referring to the vertical position of the species, which will improve the precision and utility of these models. So too is of ...
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