A global systematic review of species distribution modelling approaches for cetaceans and sea turtles

In the last decades, interest in species distribution models (SDMs) has grown greatly. The descriptive and predictive power of correlative SDMs is highly valued to meet the high demand for filling gaps in the spatial ecology of wide-ranging and elusive species, such as cetaceans and sea turtles, living in habitats that are technically challenging to survey and where the availability of high quality, unbiased data at appropriate spatial and temporal resolution is not straightforward. This study endeavours to offer a comprehensive global overview of recent advancements in modelling techniques within the realm of SDMs applied to cetaceans and sea turtles. Through a rigorous systematic review of 295 research papers, we identified gaps in species and geographic coverage and highlighted the underrepresentation of biotic, anthropogenic, and water column variables. Our examination revealed a diverse array of modelling approaches, showcasing a notable preference for standard regression-based or machine-learning models, such as GAMs or Maxent, with Bayesian-based models emerging and experiencing growing development. Critical limitations and decisions in constructing and evaluating SDMs were discussed, proposing best practices for future studies. Emphasis was placed on the importance of validating models using fully independent datasets, particularly in the context of conservationist studies. This work not only sheds light on the state of the field but also serves as a valuable tool for those interested in modelling the distribution of these magnificent and enigmatic animals, as well as other cryptic species, offering insights that can guide researchers in making informed decisions in the realm of SDMs.