Use of secondary diversity data to improve diversity estimates at multiple geographic scales

Studying the patterns and properties of biological diversity at multiple geographic scales is essential to answering biogeographical and macroecological questions. Here, we tested the hypothesis that diversity estimates derived from stacked species distribution models (stacked SDMs) would be robust and positively correlated when compared to estimates from well-surveyed areas with species checklists, but potentially more ambiguous when compared to estimates based on species’ occurrences. We used these three diversity sources to evaluate alpha and beta diversity, per-site range size, total nestedness and completeness at five geographic scales (1/2°, 1/4°, 1/8°, 1/16°, and 1/32°). Estimates from the species’ occurrences dataset and stacked SDMs showed poor positive correlation with alpha diversity in well-surveyed areas (except for stacked SDMs at coarse scales). However, beta diversity in well-surveyed areas exhibited a strong correlation with both the species’ occurrence dataset and stacked SDMs at finer scales. The nestedness pattern from stacked SDMs remained relatively constant across all geographic scales; in contrast, nestedness in occurrence datasets was influenced by finer scales, thereby affecting community traits such as incidence and composition of species. Our study demonstrates that stacked SDMs was reliable for inferring effective diversities across multiple geographic scales, whereas occurrence datasets are not and may fail to accurately infer diversity patterns. Even well-surveyed areas with species checklists showed low completeness, sharing similarities with occurrence datasets at 1/4° and 1/16°. We recommend conducting complementary analysis of completeness properties of sample coverage to ensure the reliability of diversity assessments.