Leveraging Public Data to Predict Global Niches and Distributions of Rhizostome Jellyfishes

SIMPLE SUMMARY: With human activities and climate change threatening biodiversity, marine resource managers must establish globally oriented, data-driven conservation practices. As the internet expands and the world becomes more connected, science is more accessible than ever, requiring only the internet to access powerful computing tools and expansive databases. Here, we demonstrate the power of citizen science, online databases, and open-source tools by using citizen-derived jellyfish reports from iNaturalist.org (accessed on 3 November 2022) in conjunction with publicly available environmental data to predict the distribution of the most conspicuous and economically relevant group of marine jellyfishes (Rhizostomeae). Online databases come with many biases, most of which can be tied back to resolution. The integration of distribution data from the published literature allows us to evaluate citizen-derived data quality and make a plan for improving data resolution. Going forward, expanding collaborations and citizen participation in underrepresented regions will decrease participation biases and improve data resolution, increasing the power of online databases and their potential to inform marine management strategies. ABSTRACT: As climate change progresses rapidly, biodiversity declines, and ecosystems shift, it is becoming increasingly difficult to document dynamic populations, track fluctuations, and predict responses to climate change. Concurrently, publicly available databases and tools are improving scientific accessibility, increasing collaboration, and generating more data than ever before. One of the most successful projects is iNaturalist, an AI-driven social network doubling as a public database designed to allow citizen scientists to report personal biodiversity reports with accuracy. iNaturalist is especially useful for the research of rare, dangerous, and charismatic organisms, but requires better integration into the marine system. Despite their abundance and ecological relevance, there are few long-term, high-sample datasets for jellyfish, which makes management difficult. To provide some high-sample datasets and demonstrate the utility of publicly collected data, we synthesized two global datasets for ten genera of jellyfishes in the order Rhizostomeae containing 8412 curated datapoints from both iNaturalist (n = 7807) and the published literature (n = 605). We then used these reports in conjunction with publicly available environmental data to predict global niche partitioning and distributions. Initial niche models inferred that only two of ten genera have distinct niche spaces; however, the application of machine learning-based random forest models suggests genus-specific variation in the relevance of abiotic environmental variables used to predict jellyfish occurrence. Our approach to incorporating reports from the literature with iNaturalist data helped evaluate the quality of the models and, more importantly, the quality of the underlying data. We find that free, accessible online data is valuable, yet subject to biases through limited taxonomic, geographic, and environmental resolution. To improve data resolution, and in turn its informative power, we recommend increasing global participation through collaboration with experts, public figures, and hobbyists in underrepresented regions capable of implementing regionally coordinated projects.