Mapping global marine biodiversity under sparse data conditions

Abstract

Sparse and spatiotemporally highly uneven sampling efforts pose major challenges to obtaining accurate species and biodiversity distributions. Here, we demonstrate how limited surveys can be integrated with global models to uncover hotspots and distributions of marine biodiversity. We test the skill of recent and advanced species distribution model setups to predict the global biodiversity of >560 phytoplankton species from 183,000 samples. Recent setups attain quasi-null skill, while models optimized for sparse data explain up to 91% of directly observed species richness variations. Using a refined spatial cross-validation approach to address data sparsity at multiple temporal resolutions we find that background choices are the most critical step. Predictor variables selected from broad sets of drivers and tuned for each species individually improve the models ability in identifying richness hotspots and latitude gradients. Optimal setups identify tropical hotspots, while common ones lead to polar hotspots disjunct from general marine diversity. Our results show that unless great care is taken to validate models, conservation areas in the ocean may be misplaced. Yet a game-changing advance in mapping diversity can be achieved by addressing data-sparse conditions that prevail for >80% of extant marine species. Authorship statementAll authors designed the research and contributed to the writing. D.R. designed the multiscale validation and predictor selection methods, developed the figures with input by M.V. and N.E.Z., performed research, and wrote the first draft.