Improving Distribution Prediction by Integrating Expert Range Maps and Opportunistic Occurrences: Evidence From Japanese Sea Cucumber

Abstract

In an era of biodiversity crisis, it is critical to perform biodiversity assessments to better inform conservation strategies. In this regard, species distribution models (SDMs) represent a widely used tool for biodiversity assessment. Despite their popularity, the accuracy of SDM predictions has long been criticized because we have incomplete or biased information on species distribution. To overcome this limitation, researchers have proposed improving predictions of SDMs by integrating different types of distribution data, but this idea has rarely been explored in the marine realm. In this study, we explored the idea of data integration using the Japanese sea cucumber, whose distribution is known to be restricted by freshwater discharge of the Yangtze River. We first fitted SDMs for this species based on opportunistic occurrence records via four modeling algorithms, then built two types of ensemble models using stacked generalization: an ensemble model that solely used four model predictions and an expert-informed ensemble model that further accounted for distance to the IUCN expert range map. Our results showed that integrating an expert range map into the opportunistic occurrence model improved distribution prediction by avoiding overprediction in the south of the dispersal barrier for this species. Our study highlights the benefits of integrating expert range maps into opportunistic occurrence SDMs, which improve the reliability of species' spatial distributions.